app.py

import os
import gradio as gr
import openai
#from numpy._core.defchararray import endswith, isdecimal, startswith
from openai import OpenAI
from dotenv import load_dotenv
from pathlib import Path
from time import sleep
import audioread
import queue
import threading
from glob import glob
import copy
import base64
import json
from PIL import Image
from io import BytesIO
from pydantic import BaseModel
import pprint
import pandas as pd
import yfinance as yf
from datetime import datetime, timedelta
import pytz
import math
import numpy as np
from pylatexenc.latex2text import LatexNodes2Text
import requests
from urllib.parse import quote
import geo_distance
import geo_locate


load_dotenv(override=True)
key = os.getenv('OPENAI_API_KEY')
users = os.getenv('LOGNAME')
unames = users.split(',')
pwds = os.getenv('PASSWORD')
pwdList = pwds.split(',')
DEEPSEEK_KEY=os.getenv('DEEPSEEK_KEY')
GROQ_KEY=os.getenv('GROQ_KEY')
BRAVE_KEY=os.getenv('BRAVE_KEY')
BRAVE_SEARCH_KEY=os.getenv('BRAVE_SEARCH_KEY')
LOCATIONID_KEY=os.getenv('LOCATIONID_KEY')

site = os.getenv('SITE')
if site == 'local':
    dp = Path('./data')
    dp.mkdir(exist_ok=True)
    dataDir = './data/'
else:
    dp = Path('/data')
    dp.mkdir(exist_ok=True)
    dataDir = '/data/'
stock_data_path = dataDir + 'Stocks.txt'

braveNewsEndpoint = "https://api.search.brave.com/res/v1/news/search"
braveSearchEndpoint = "https://api.search.brave.com/res/v1/web/search"

speak_file = dataDir + "speek.wav"

# client = OpenAI(api_key = key)

#digits = ['zero: ','one: ','two: ','three: ','four: ','five: ','six: ','seven: ','eight: ','nine: ']

abbrevs = {'St. ' : 'Saint ', 'Mr. ': 'mister ', 'Mrs. ':'mussus ', 'Mr. ':'mister ', 'Ms. ':'mizz '}

special_chat_types = ['math', 'logic']

news_interval_choices = [("Day", "pd"), ("Week", "pw"), ("Month", "pm"), ("Year", "py")]

def get_distance(addr1, addr2):
    (lat1, lon1) = geo_locate.get_geo_coords(addr1, LOCATIONID_KEY)
    (lat2, lon2) = geo_locate.get_geo_coords(addr2, LOCATIONID_KEY)
    distance = geo_distance.great_circle_distance_miles(lat1, lon1, lat2, lon2)
    return distance

def get_openai_file(file_id, container_id):
    url = f'https://api.openai.com/v1/containers/{container_id}/files/{file_id}/content'
    headers= {"Authorization": "Bearer " + key}

    response = requests.get(
        url,
        headers=headers
        )
    return response

def list_openai_container_files(container_id):
    url = f'https://api.openai.com/v1/containers/{container_id}/files'
    headers= {"Authorization": "Bearer " + key}

    response = requests.get(
        url,
        headers=headers
        )
    return response

def create_openai_container(name):
    url = 'https://api.openai.com/v1/containers'
    headers= {"Authorization": "Bearer " + key, "Content-Type": "application/json",}
    json_data = {"name": name}

    response = requests.post(
        url,
        headers=headers,
        json=json_data
        )

    return json.loads(response.content)["id"]


class Step(BaseModel):
    explanation: str
    output: str

class MathReasoning(BaseModel):
    steps: list[Step]
    final_answer: str

def get_brave_search_results(query: str):
    rv = ''
    url = f'{braveSearchEndpoint}?q={quote(query)}&count=20'
    response = requests.get(
        url,
        headers= {"Accept": "application/json",
                  "X-Subscription-Token": BRAVE_KEY
                  },
        )
    rv ='''Following are list items delineated by *item separator*
At the end of each item is (item source, item age)
*item separator*  '''
    jdata = response.json()
    web_results = jdata['web']['results']
    for item in web_results:
        title = item['title']
        description = item['description']
        rv += f'{title}: {description} --'
        try:  # extra_snippets can be missing
            for snip in item['extra_snippets']:
                rv += (snip + ' ')
        except:
            pass
        try:
            host = item['meta_url']['hostname']
        except:
            host = 'unknown'
        try:
            age = item['age']
        except:
            age = 'unknown'
        rv += f' (Item source: {host}, Item age: {age})'
        rv += ' *item separator* '

    return rv


def get_brave_news(query: str, interval: str = 'pd'):
    url = f'{braveNewsEndpoint}?q={quote(query)}&count=20&extra_snippets=true&freshness={interval}' 
    response = requests.get(
        url,
        headers= {"Accept": "application/json",
                  "X-Subscription-Token": BRAVE_KEY
                  },
        )
    rv ='''Following are list items delineated by *item separator*
At the end of each item is (item source, item age)
*item separator*  '''
    jdata = response.json()
    for item in jdata['results']:
        title = item['title']
        description = item['description']
        rv += f'{title}: {description} --'
        try:  # extra_snippets can be missing
            for snip in item['extra_snippets']:
                rv += (snip + ' ')
        except:
            pass
        try:
            host = item['meta_url']['hostname']
        except:
            host = 'unknown'
        try:
            age = item['age']
        except:
            age = 'unknown'
        rv += f' (Item source: {host}, Item age: {age})'
        rv += ' *item separator* '
    return rv

def Client():
    return OpenAI(api_key = key)

def test_plot_df():
    data = {
        "month": ['2024-01','2024-02','2024-03'],
        "value": [22.4, 30.1, 25.6]
        }
    return pd.DataFrame(data)


def md(txt):
    # if 'DOCTYPE' in txt:
    #     return str(txt.replace('GPT','<br>GPT'))
    # else:
    return str(txt).replace('```', ' ').replace('  ', '&nbsp;&nbsp;').replace('  ', '&nbsp;&nbsp;').replace('  ', '&nbsp;&nbsp;').replace('\n','<br>').replace('~~','~')
    # return txt

def etz_now():
    eastern = pytz.timezone('US/Eastern')
    ltime = datetime.now(eastern)
    return ltime

def date_from_utime(utime):
    ts = int(utime)
    dt = datetime.utcfromtimestamp(ts)
    eastern = pytz.timezone('US/Eastern')
    return dt.astimezone(eastern).strftime('%Y-%m-%d')

def convert_latex_math(text):
    lines = text.split('\n')
    start_line = False
    out_txt = ''
    for line in lines:
        if len(line) == 0:
            out_txt += '\n'
            continue
        else:
            if line == r'\]':
                continue
            if line == r'\[':
                start_line = True
                continue
            if start_line:
                line = '\n' + LatexNodes2Text().latex_to_text(line.strip())
                start_line = False
            if line.startswith(r'\['):
                loc = line.find(r'\]')
                if loc > 0:
                    latex_code = line[2:loc]
                    line = '\n' + LatexNodes2Text().latex_to_text(latex_code)
        out_txt += (line + '\n')
    return out_txt

def stock_list():
    rv = ''
    with open(stock_data_path, 'rt') as fp:
        lines = fp.readlines()
        for line in lines:
            (name, symbol, shares) = line.rstrip().split(',')
            name = name.strip()
            symbol = symbol.strip()
            rv += f'{symbol}  {name}\n'
    return rv

def get_stock_list():
    stock_list = {}
    with open(stock_data_path, 'rt') as fp:
        lines = fp.readlines()
        for line in lines:
            (name, symbol, shares) = line.rstrip().split(',')
            stock_list[symbol.strip()] = (name.strip(),shares.strip())
    return stock_list

def get_stock_news(search_symbol):
    fuzzy = True
    have_symbol = False
    search_symbol = search_symbol.strip().upper()
    stock_list = get_stock_list()
    search_term = search_symbol
    if search_symbol in stock_list.keys():
        have_symbol = True
        (search_term, shares) = stock_list[search_symbol]
    try:
        news = yf.Search(search_term, news_count=5, enable_fuzzy_query=fuzzy).news
    except:
        return (f'No results for search term {search_term}, check spelling', None)
    rv = ''
    for item in news:
        rv += f'Title: {item["title"]}\n'
        rv += f'Publisher: {item["publisher"]}\n'
        rv += f'Date published: {date_from_utime(item["providerPublishTime"])}\n'
        rv += f'Link: [URL]({item["link"]})\n\n'

    if have_symbol:
        (plot_df, ymax, deltas) = stock_week_df(search_symbol)
    else:
        (plot_df, ymax, deltas) = (pd.DataFrame(), 0.0, (0.0, 0.0, 0.0))

    return (rv, plot_df, ymax, deltas)

def stock_history_df(num_weeks):
    values = []
    dates = []
    xmax = 0
    for offset in range(num_weeks+1):
        (value, date) = get_stock_report(False, offset)
        # date = date[5:]
        values.append(value)
        dates.append(date)
        if float(value) > xmax:
            xmax = float(value)
    values.reverse()
    dates.reverse()
    data = {
        "date": dates,
        "value" : values
        }
    return (pd.DataFrame(data), f'{int(xmax + 10000)}')

def stock_deltas(values):
    num = len(values)
    month_end_avg = float(np.average(np.array(values[-3:])))
    month_start_avg = float(np.average(np.array(values[0:4])))
    week_start_avg = float(np.average(np.array(values[-7:-4])))
    week_end_avg = float(np.average(np.array(values[-2:])))
    month_delta = 100 * (month_end_avg - month_start_avg)/month_start_avg
    week_delta = 100 * (week_end_avg - week_start_avg)/week_start_avg
    daily_delta = 100 * ((float(values[num-1])/float(values[num-2])) - 1.0)
    # avg = np.average(npa)
    return (month_delta, week_delta, daily_delta)

def stock_week_df(symbol):
    try:
        dates = []
        values = []
        ymax = 0
        etime = etz_now()
        if etime.hour >= 16:
            etime = etime + timedelta(days=1)
        week_ago = etime - timedelta(days=40) # was 8
        end = etime.strftime('%Y-%m-%d')
        start = week_ago.strftime('%Y-%m-%d')
        df = yf.download(symbol.upper(),
                        start = start,
                        end = end,
                        progress = False,
                        )
        vals2d = df.values.tolist()
        valsTxt = []
        numDays = len(vals2d)
        for i in range(numDays):
            valsTxt.append(vals2d[i][0])
        for val in valsTxt:
            v = round(float(val),2)
            values.append(v)
            if v > ymax:
                ymax = v
        for row in df.index:
            dates.append(row.strftime('%Y-%m-%d'))
        # fit_data = lms_fit(dates, values)
        # pct_delta = lms_fit_trend(dates, values)
        deltas = stock_deltas(values)
        data = {
            "date": dates,
            "value" : values,
            # "fit" : fit_data
            }
        # fig = make_mp_figure(dates, values, fit_data, ymax)
        return (pd.DataFrame(data), ymax, deltas)
    except:
        return (pd.DataFrame(), ymax, (0.0, 0.0, 0.0))

def stock_recent_delta(symbol):
    try:
        dates = []
        values = []
        ymax = 0
        etime = etz_now()
        if etime.hour >= 16:
            etime = etime + timedelta(days=1)
        week_ago = etime - timedelta(days=8)
        end = etime.strftime('%Y-%m-%d')
        start = week_ago.strftime('%Y-%m-%d')
        df = yf.download(symbol.upper(),
                        start = start,
                        end = end,
                        progress = False,
                        )
        vals2d = df.values.tolist()
        valsTxt = []
        numDays = len(vals2d)
        for i in range(numDays):
            valsTxt.append(vals2d[i][0])
        for val in valsTxt:
            v = round(float(val),2)
            values.append(v)
            if v > ymax:
                ymax = v
        for row in df.index:
            dates.append(row.strftime('%Y-%m-%d'))
        start_val = float(np.average(np.array(values[:2])))
        end_val = float(values[len(values)-1])
        return f'{(end_val/start_val - 1.0)*100:.1f}'
    except:
        return 'NA'

def get_alerts():
    try:
        rv = ''
        # stock_data = {}
        global stock_data_path
        with open(stock_data_path, 'rt') as fp:
            lines = fp.readlines()
        for line in lines:
            (name, symbol, shares) = line.rstrip().split(',')
            name = name.strip()
            symbol = symbol.strip()
            delta_pct = stock_recent_delta(symbol)
            if delta_pct == 'NA':
                rv += f'\n{symbol} ({name})   NA'
            else:
                rv += f'\n{symbol} ({name})   {delta_pct}%'
                if abs(float(delta_pct)) > 3:
                    rv += ' **\*\*\*** '
        return 'Stock price changes over last week:\nChanges greater than +/-3% marked by  **\*\*\***\n ' + rv + '\n'
    except:
        return "Error getting stock deltas\n"

def get_stock_report(verbose = True, offset = 0):
    try:
        stock_data = {}
        global stock_data_path
        error_msg = ''
        with open(stock_data_path, 'rt') as fp:
            lines = fp.readlines()
        for line in lines:
            (name, symbol, shares) = line.rstrip().split(',')
            name = name.strip()
            symbol = symbol.strip()
            shares = shares.strip()
            stock_data[symbol] = {"symbol": symbol, "name": name, "shares": shares, "closing": '0'}
        for symbol in stock_data.keys():
            (closing_price, closing_date) = get_last_closing(symbol, offset)
            if closing_price == 0:
                error_msg += f'Error getting closing for {symbol}\n'
            stock_data[symbol]['closing'] = f'{closing_price:.2f}'
        total_value = 0.0
        if verbose:
            rv = f'At closing on {closing_date}:\n'
            for item in stock_data.values():
                rv += str(item) + '\n'
                total_value += float(item['closing']) * float(item['shares'])
            rv += (f'\nTotal value = {total_value:.2f}\n')
            if len(error_msg) > 0:
                rv += error_msg
            rv += f'Eastern time is: {etz_now()}'
        else:
            for item in stock_data.values():
                total_value += float(item['closing']) * float(item['shares'])
            return (total_value, closing_date)
    except:
        rv = 'Error getting stock report'
    return rv

def get_last_closing(symbol, offset=0, timeout=10):
    try:
        etime = etz_now()
        if etime.hour >= 16:
            etime = etime + timedelta(days=1)
        if offset > 0:
            etime = etime - timedelta(weeks=offset)
        five_days_ago = etime - timedelta(days=6)
        end = etime.strftime('%Y-%m-%d')
        start = five_days_ago.strftime('%Y-%m-%d')
        df = yf.download(symbol,
                        start = start,
                        end = end,
                        progress = False,
                        timeout=timeout,
                        auto_adjust=False,
                        )
        # print(df)
        closing_date = 'unknown'
        data_top = df.tail(1)
        for row in data_top.index:
            closing_date = row.strftime('%Y-%m-%d')
            # print(closing_date)
        return (df.iat[-1,0], closing_date)
    except:
        return (0.0, "0000-00-00")

def get_total_daily_closing_sequence(num_days):
    try:
        first_loop = True
        max_val = 0.0
        stock_list = get_stock_list()
        symbols = [s for s in stock_list.keys()]
        # symbols = symbols[8:10]
        etime = etz_now()
        if etime.hour >= 16:
            etime = etime + timedelta(days=1)
        end = etime.strftime('%Y-%m-%d')
        start_time = etime - timedelta(days = num_days)
        start = start_time.strftime('%Y-%m-%d')
        df = yf.download(symbols,
                start = start,
                end = end,
                progress = False,
                auto_adjust=False,
                )
        # val2d = df.values.tolist()
        dates = []
        for row in df.index:
            dates.append(row.strftime('%Y-%m-%d'))
        # columns = list(df.columns.values)
        # cvals = df[columns[0]].tolist()

        for sym in symbols:
            (name, shares) = stock_list[sym]
            values = df[('Close', sym)].tolist()
            n = len(values)
            for i in range(n):
                if math.isnan(float(values[i])):
                    if i == 0:
                        values[0] = values[1]
                    else:
                        values[i] = values[i-1]
            if first_loop:
                first_loop = False
                total_values = values.copy()
                for i in range(n):
                    total_values[i] = float(total_values[i]) * float(shares)
            else:
                for i in range(n):
                    total_values[i] += (float(values[i]) * float(shares))
        for i in range(n):
            total_values[i] = round(total_values[i], 2)
            if total_values[i] > max_val:
                max_val = total_values[i]
        data = {
            "date": dates,
            "value" : total_values
            }
        return (pd.DataFrame(data), max_val)
    except:
        return (pd.DataFrame(), 0.0)

def get_daily_closing_sequence(symbol, num_days):
    try:
        dates = []
        values = []
        etime = etz_now()
        if etime.hour >= 16:
            etime = etime + timedelta(days=1)
        end = etime.strftime('%Y-%m-%d')
        start_time = etime - timedelta(days = num_days)
        start = start_time.strftime('%Y-%m-%d')
        df = yf.download(symbol,
                        start = start,
                        end = end,
                        progress = False,
                        )
        vals2d = df.values.tolist()
        valsTxt = []
        values = [round(float(vals2d[i][0]),2) for i in range(len(vals2d))]
        for row in df.index:
            dates.append(row.strftime('%Y-%m-%d'))
        return(dates, values)
    except:
        return([],[])

def create_stock_data_file(txt):
    with open(stock_data_path, 'wt') as fp:
        fp.write(txt)

def solve(prompt, chatType):
    tokens_in = 0
    tokens_out = 0
    tokens = 0
    if chatType == 'math':
        instruction = "You are a helpful math tutor. Guide the user through the solution step by step."
    elif chatType == "logic":
        instruction = "you are an expert in logic and reasoning.  Guide the user through the solution step by step"
    try:
        completion = Client().beta.chat.completions.parse(
            model = 'gpt-4o-2024-08-06',
            messages = [
                {"role": "system", "content": instruction},
                {"role": "user", "content": prompt}
            ],
            response_format=MathReasoning,
            max_tokens = 2000
        )

        tokens_in = completion.usage.prompt_tokens
        tokens_out = completion.usage.completion_tokens
        tokens = completion.usage.total_tokens
        msg = completion.choices[0].message 
        if msg.parsed:
            dr = msg.parsed.model_dump()
            response = pprint.pformat(dr)
        elif msg.refusal:
            response = msg.refusal

    except Exception as e:
        if type(e) == openai.LengthFinishReasonError:
            response = 'Too many tokens' 
        else:
            response = str(e)
    return (response, tokens_in, tokens_out, tokens)

def genUsageStats(do_reset=False):
    result = []
    ttotal4o_in = 0
    ttotal4o_out = 0
    ttotal4mini_in = 0
    ttotal4mini_out = 0
    totalAudio = 0
    totalSpeech = 0
    totalImages = 0
    totalHdImages = 0
    if do_reset:
        dudPath = dataDir + '_speech.txt'
        if os.path.exists(dudPath):
            os.remove(dudPath)
    for user in unames:
        tokens4o_in = 0
        tokens4o_out = 0
        tokens4mini_in = 0
        tokens4mini_out = 0
        fp = dataDir + user + '_log.txt'
        if os.path.exists(fp):
            accessOk = False
            for i in range(3):
                try:
                    with open(fp) as f:
                        dataList = f.readlines()
                    if do_reset:
                        os.remove(fp)
                    else:
                        for line in dataList:
                            (u, t) = line.split(':')
                            (t, m) = t.split('-')
                            (tin, tout) = t.split('/')
                            incount = int(tin)
                            outcount = int(tout)
                            if 'mini' in m:
                                tokens4mini_in += incount
                                tokens4mini_out += outcount
                                ttotal4mini_in += incount
                                ttotal4mini_out += outcount
                            else:
                                tokens4o_in += incount
                                tokens4o_out += outcount
                                ttotal4o_in += incount
                                ttotal4o_out += outcount
                    accessOk = True
                    break
                except:
                    sleep(3)
            if not accessOk:
                return f'File access failed reading stats for user: {user}'
        userAudio = 0
        fp = dataDir + user + '_audio.txt'
        if os.path.exists(fp):
            accessOk = False
            for i in range(3):
                try:
                    with open(fp) as f:
                        dataList = f.readlines()
                    if do_reset:
                        os.remove(fp)
                    else:
                        for line in dataList:
                            (dud, len) = line.split(':')
                            userAudio += int(len)
                        totalAudio += int(userAudio)
                    accessOk = True
                    break
                except:
                    sleep(3)
            if not accessOk:
                return f'File access failed reading audio stats for user: {user}'
        userSpeech = 0
        fp = dataDir + user + '_speech.txt'
        if os.path.exists(fp):
            accessOk = False
            for i in range(3):
                try:
                    with open(fp) as f:
                        dataList = f.readlines()
                    if do_reset:
                        os.remove(fp)
                    else:
                        for line in dataList:
                            (dud, len) = line.split(':')
                            userSpeech += int(len)
                        totalSpeech += int(userSpeech)
                    accessOk = True
                    break
                except:
                    sleep(3)
            if not accessOk:
                return f'File access failed reading speech stats for user: {user}'
        user_images = 0
        user_hd_images = 0
        fp = image_count_path(user)
        if os.path.exists(fp):
            accessOk = False
            for i in range(3):
                try:
                    with open(fp) as f:
                        dataList = f.readlines()
                    if do_reset:
                        os.remove(fp)
                    else:
                        for line in dataList:
                            x = line.strip()
                            if x == 'hd':
                                user_hd_images += 1
                                totalHdImages += 1
                            else:
                                user_images += 1
                                totalImages += 1
                    accessOk = True
                    break
                except:
                    sleep(3)
            if not accessOk:
                return f'File access failed reading image gen stats for user: {user}'
        result.append([user, f'{tokens4mini_in}/{tokens4mini_out}', f'{tokens4o_in}/{tokens4o_out}', f'audio:{userAudio}',f'speech:{userSpeech}', f'images:{user_images}/{user_hd_images}'])
    result.append(['totals', f'{ttotal4mini_in}/{ttotal4mini_out}', f'{ttotal4o_in}/{ttotal4o_out}', f'audio:{totalAudio}',f'speech:{totalSpeech}', f'images:{totalImages}/{totalHdImages}'])
    return result       

def new_conversation(user):
    clean_up(user)  # .wav files
    flist = []
    for ext in ['png','docx','xlsx','pdf','pptx', 'csv']:
        flist.extend(glob(f'{dataDir}{user}*.{ext}'))
    flist.extend(glob(f'{dataDir}{user}_image.b64'))
    for fpath in flist:
        if os.path.exists(fpath):
            os.remove(fpath)
    if user == unames[0]:
        mode_list = ["Advanced", "Chat", "News", "Search"]
    else:
        mode_list = ["Chat", "News", "Search"]
    return [None, [], gr.Markdown(value='', label='Dialog', container=True),
            gr.Image(visible=False, value=None),  gr.Image(visible=False, value=None), '',
            gr.LinePlot(visible=False), gr.Dropdown(value='pd', visible=False),
            gr.Dropdown(choices=mode_list, value=mode_list[0]),
            gr.DownloadButton(label='Download File', visible=False, value=None), '',
            gr.File(label='Upload File', visible=False)]

def updatePassword(txt):
    password = txt.lower().strip()
    if password == pwdList[0]:
        mode_list = ["Advanced", "Chat", "News", "Search"]
    else:
        mode_list = ["Chat", "News", "Search"]
    return [password, "*********", gr.Dropdown(choices=mode_list, value=mode_list[0])]

# def parse_math(txt):
#     ref = 0
#     loc = txt.find(r'\(')
#     if loc == -1:
#         return txt
#     while (True):
#         loc2 = txt[ref:].find(r'\)')
#         if loc2 == -1:
#             break
#         loc = txt[ref:].find(r'\(')
#         if loc > -1:
#             loc2 += 2
#             slice = txt[ref:][loc:loc2]
#             frag = lconv.convert(slice)
#             txt = txt[:loc+ref] + frag + txt[loc2+ref:]
#             ref = len(txt[ref:loc]) + len(frag)
#     return txt

def get_response(inputs, previous_response_id, container_id, image_file, uploaded_file_path):
    instructions = '''
You are a helpful assistant who knows how to browse the web for info and to write and run python
code and to generate images. 
'''
    instructions += f'''
Do not use latex for math expressions in text output.
If a chart, table or plot is produced, return it as an image.
If a powerpoint slide is created, return it as an image but do not offer a download link.
If the user asks you to output a file, You must include the file you generate in the annotation
of the output text.
If a MCP server requires a password input you will use {pwdList[0]}.
'''
    if uploaded_file_path != '' and uploaded_file_path.casefold().split('.')[-1] == 'pdf':
        pdf_b64 = ''
        with open(uploaded_file_path, 'rb') as fp:
            data = fp.read()
            b64data = base64.b64encode(data)
            pdf_b64 = b64data.decode('utf-8')
        inputs.append(
            {
               "role" :"user",
               "content": [
                   {
                   "type": "input_file",
                   "filename": f'{os.path.basename(uploaded_file_path)}',
                   "file_data": f'data:application/pdf;base64,{pdf_b64}',
                   }
                ]
            }
        )
        
    if image_file != '':
        with open(image_file, 'rt') as fp:
            b64data = fp.read()
        inputs.append(
            {
                "role": "user",
                 "content": [
                     {
                         "type": "input_image",
                         "image_url": f'data:image/jpeg;base64, {b64data}',
                    }
                ]
            }
        )

    response = Client().responses.create(
        model= "gpt-5-mini",      #"gpt-5-mini", "o4-mini",
        tools=[{ "type": "web_search_preview" },
                { "type": "code_interpreter", "container": container_id},    #{'type': 'auto'}},
                { "type": "image_generation", "quality": "medium", "size": "1024x1024"},
            # {"type": "function", "name": "get_distance",
            #   "description": "get calculated straight-line (great-circle) distance between two locations or addresses.",
            #   "parameters": {
            #       "type": "object", "properties": {
            #           "addr1": {
            #               "type": "string",
            #               "description": "The street address or other designation of a location.",
            #         },
            #         "addr2": {
            #             "type": "string",
            #             "description": "The street address or other designation of a location.",
            #             },
            #       },
            #     "required": ["addr1", "addr2"],
            #     },
                {
                    "type": "mcp",
                    "server_label": "Geo_distance",
                    "server_description": "A MCP server to compute straight line distances between two locations",
                    "server_url": "https://dlflannery-geo-distance.hf.space/gradio_api/mcp/",
                    "require_approval": "never",
                    },
    
        ],
        previous_response_id=previous_response_id,
        instructions = instructions,
        input=inputs,
        reasoning ={
            "effort": "medium",
            "summary": "auto"
        }
    )

    return response

def chat(prompt, user_window, pwd_window, past, response, gptModel, uploaded_image_file='',
        plot=None, news_interval = 'pd', mode = 'Chat', uploaded_file_path=''):
    image_out = gr.Image(visible=False, value=None)
    file_out = gr.DownloadButton(value=None)
    image_gen_model = 'gpt-4o-2024-08-06'
    user_window = user_window.lower().strip()
    isBoss = False
    query = ''
    if not response:
        response = ''
    plot = gr.LinePlot(visible=False)
    # plot = gr.Plot(visible=False)
    if user_window == unames[0] and pwd_window == pwdList[0]:
        isBoss = True
        if prompt == 'stats':
            response = genUsageStats()
            return [past, str(response), None, gptModel, uploaded_image_file, plot, image_out, file_out, uploaded_file_path]
        if prompt == 'reset':
            response = genUsageStats(True)
            return [past, md(response), None, gptModel, uploaded_image_file, plot, image_out, file_out, uploaded_file_path]
        if prompt.startswith('gpt4'):
            gptModel = 'gpt-4o-2024-08-06'
            prompt = prompt[5:]
        if prompt.startswith('gpt5m'):
            gptModel = 'gpt-5-mini'
            prompt = prompt[6:]
        if prompt.startswith("clean"):
            user = prompt[6:]
            response = f'cleaned all .wav and .b64 files for {user}'
            final_clean_up(user, True)
            return [past, response, None, gptModel, uploaded_image_file, plot, image_out, file_out, uploaded_file_path]
        if prompt.startswith('files'):
            (log_cnt, wav_cnt, other_cnt, others, log_list) = list_permanent_files()
            response = f'{log_cnt} log files\n{wav_cnt} .wav files\n{other_cnt} Other files:\n{others}\nlogs: {str(log_list)}'
            return [past, response, None, gptModel, uploaded_image_file, plot, image_out, file_out, uploaded_file_path]
        if prompt.startswith('stock'):
            args = prompt.split(' ')
            num = len(args)
            if num == 1:
                response = stock_list()
                return [past, md(response), None, gptModel, uploaded_image_file, plot, image_out, file_out, uploaded_file_path]
            elif num == 2:
                if args[1] == 'alerts':
                    response = get_alerts()
                    return [past, md(response), None, gptModel, uploaded_image_file, plot, image_out, file_out, uploaded_file_path]
                else:
                    response = get_stock_report()
                    if args[1] == 'value':
                        return [past, md(response), None, gptModel, uploaded_image_file, plot, image_out, file_out, uploaded_file_path]
                    elif args[1] == 'history':
                        (plot_df, ymax) = get_total_daily_closing_sequence(40)   #stock_history_df(12)
                        # ymax = float(ymax)
                        return [past, md(response), None, gptModel, uploaded_image_file, # plot]
                               gr.LinePlot(plot_df, x="date", y="value", visible=True, x_label_angle=270,
                                          y_lim=[500000, 900000], label="Portfolio Value History"), image_out, file_out, uploaded_file_path]
            elif num >= 3:
                if args[1] == 'news':
                    symbol = ' '.join(args[2:])
                    (response, plot_df, ymax, (dm, dw, dd)) = get_stock_news(symbol)
                    ymax *= 1.1
                    mdtxt = md(f'News for {symbol}:\nTrends: Month = {dm:.1f}%, Week = {dw:.1f}%, Day = {dd:.1f}%\n\n' + response)
                    if plot_df.empty:
                        return [past, mdtxt, None, gptModel, uploaded_image_file, plot, image_out, file_out, uploaded_file_path]
                    else:
                        return [past, mdtxt, None, gptModel, uploaded_image_file,
                                    gr.LinePlot(plot_df, x="date", y="value", visible=True, x_label_angle=270,
                                                    y_lim=[0, ymax],label=f"{symbol.upper()} Recent Prices",
                                                    color_map={''}), image_out, file_out, uploaded_file_path]
        if prompt.startswith('stockload'):
            create_stock_data_file(prompt[9:].lstrip())
            return [past, 'Stock data file created', None, gptModel, uploaded_image_file, plot, image_out, file_out, uploaded_file_path]
    if user_window in unames and pwd_window == pwdList[unames.index(user_window)]:
        chatType = 'normal'
        deepseek = False
        using_groq = False
        reasoning = False
        prompt = prompt.strip()
        need_turn = True
        responses = []
        inputs = []
        prev_id = None
        text = ''
        reasoning = ''
        show_reasoning = False
        if mode == "Advanced":
            if len(past):
                (prev_id, container_id) = past.pop()
                past = []
        while mode == 'Advanced' and need_turn:
            need_turn = False
            if len(past) == 0:
                container_id = create_openai_container('My Container')
                file_text = ''
                if uploaded_file_path != '':
                    upfile_ext = uploaded_file_path.casefold().split('.')[-1]
                    if upfile_ext == 'txt':
                        with open(uploaded_file_path, 'rt') as fp:
                            file_text = fp.read() + '\n'
                        uploaded_file_path = ''
                inputs.append(
                    {"role": "user", "content": f"{file_text + prompt}"}
                    )
            else:
                (prev_id, container_id) = past.pop()
                for item in past:
                    response += item
            try:
                result = get_response(inputs, prev_id, container_id,
                                     uploaded_image_file, uploaded_file_path)
                uploaded_image_file = ''
                uploaded_file_path = ''
            except Exception as e:
                return [[], f"Sorry, there was an error ({str(e)})  getting the AI response",
                       prompt, gptModel, uploaded_image_file, plot, image_out, file_out, uploaded_file_path]

            image_done = False
            ann_files = [] # (container_id, file_id, filename)
            code_files = [] # (container_id, file_id, filename)
            text += '\n???  AI returned no text for this query\n'
            for output in result.output:
                if output.type == 'message':
                    for content in output.content:
                        if content.type == 'output_text':
                            text = content.text
                            for anns in content.annotations:
                                if anns.type == "container_file_citation":
                                    try:
                                        file_name = anns.filename
                                        file_id = anns.file_id
                                        cont_id = anns.container_id
                                        if file_name.find('.png') > 0:
                                            if not image_done:
                                                image_done = True
                                                fpath = dataDir + user_window + '.png'
                                                image_data = get_openai_file(file_id, cont_id).content
                                                with open(fpath,'wb') as fp:
                                                    fp.write(image_data)
                                                image_out = gr.Image(visible=True, value=fpath)
                                        else:
                                            ann_files.append((cont_id, file_id, file_name))
                                    except:
                                        pass
                            # for ann in file_anns:
                            #     text += f'\n\n{str(ann)}\n'
                elif output.type == 'code_interpreter_call' :
                    cont_id = output.container_id
                    file_list_object = list_openai_container_files(cont_id)
                    try:
                        file_list_json = json.loads(file_list_object.content)
                        for file_item in file_list_json['data']:
                            file_bytes = file_item['bytes']
                            file_id = file_item['id']
                            file_name = file_item['path']
                            code_files.append((cont_id, file_id, file_name))
                    except:
                        pass
                elif output.type == 'function_call':
                    if output.name == 'get_distance':
                        args = json.loads(output.arguments)
                        distance = get_distance(args['addr1'], args['addr2'])
                        inputs.append({
                            "type": "function_call_output",
                            "call_id": f"{output.call_id}",
                            "output": f"{float(distance):.2f}",
                            } )
                        need_turn = True
                        continue
                elif output.type == 'image_generation_call':
                    if len(output.result) > 500 and not image_done:
                        image_done = True;
                        image_data = base64.b64decode(output.result)
                        fpath = dataDir + user_window + '.png'
                        with open(fpath,'wb') as fp:
                            fp.write(image_data)
                        image_out = gr.Image(visible=True, value=fpath)
                elif isBoss and output.type == 'reasoning':
                    for item in output.summary:
                        reasoning += f'\nReasoning: {item.text}'
            do_file_download = False
            ext = ''
            backup_image = None
            if len(ann_files) > 0:
                (cont_id, file_id, file_name) = ann_files[-1]
                ext = file_name.split('.')[-1].casefold()
                do_file_download = True
            elif len(code_files) > 0:
                for i in range(len(code_files)):
                    (cont_id, file_id, file_name) = code_files[i]
                    if file_name.casefold().find('access') >= 0:
                        continue
                    ext = file_name.split('.')[-1].casefold()
                    if ext == 'png':
                        if not image_done:
                            backup_image = code_files[i]
                    else:
                        do_file_download = True
                        break
            if not do_file_download and not image_done and backup_image:
                (cont_id, file_id, file_name) = backup_image
                fpath = dataDir + user_window + '.png'
                image_data = get_openai_file(file_id, cont_id).content
                with open(fpath,'wb') as fp:
                    fp.write(image_data)
                image_out = gr.Image(visible=True, value=fpath)

            if do_file_download:
                fpath = dataDir + user_window + '.' + ext
                try:
                    data = get_openai_file(file_id, cont_id).content  
                    with open(fpath,'wb') as fp:
                        fp.write(data)
                    file_name = os.path.basename(file_name)
                    file_out = gr.DownloadButton(label='Download '+ file_name, visible=True, value=fpath)
                except:
                    text += f'\nUnable to load code-generated file: {file_name}'
                # text += '\nIf a download link is given above, ignore it. Use the button below'
            if need_turn:
                # past.append(md(prompt))
                past.append((result.id, container_id))
                continue
            out_text = "\n".join(line for line in text.splitlines() if 
                                 'download' not in line.casefold())
            res = md("\n\n***YOU***: " + prompt + "\n\n***GPT***: " + out_text + '\n' + reasoning)
            response += res
            past.append(res)
            past.append((result.id, container_id))
            tokens_in = result.usage.input_tokens
            tokens_out = result.usage.output_tokens
            dataFile = new_func(user_window)
            with open(dataFile, 'a') as f:
                f.write(f'{user_window}:{tokens_in}/{tokens_out}-4omini\n')
            if isBoss:
                response += md(f"\n\ngpt-5-mini: tokens in/out = {tokens_in}/{tokens_out}\n")
            return [past, response , None, gptModel, uploaded_image_file, plot, image_out, file_out, uploaded_file_path]
        if mode == 'Search':
            loc = prompt.find('q:')
            if loc > -1:
                query = prompt[loc+2:]
                prompt = prompt[0:loc]
        augmented_prompt = prompt
        finish_reason = 'ok'
        if prompt.lower().startswith('dsr1 '):
            deepseek = True
            ds_model = 'deepseek-ai/DeepSeek-R1'
            prompt = prompt[5:]
        elif prompt.lower().startswith('ds1.5 '):
            deepseek = True
            ds_model = 'deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B'
            prompt = prompt[6:]
        elif prompt.lower().startswith('ds14 '):
            deepseek = True
            ds_model = 'deepseek-ai/DeepSeek-R1-Distill-Qwen-14B'
            prompt = prompt[5:]
        elif prompt.lower().startswith('ds70 '):
            deepseek = True
            ds_model = 'deepseek-ai/DeepSeek-R1-Distill-Llama-70B'
            prompt = prompt[5:]
        elif prompt.lower().startswith('ds70g '):
            deepseek = True
            using_groq = True
            ds_model = 'deepseek-r1-distill-llama-70b'
            prompt = prompt[6:]
        elif prompt.lower().startswith('o1m '):
            reasoning = True
            gptModel = 'o1-mini'
            prompt = prompt[4:] + \
            '. Provide a detailed step-by-step description of your reasoning. Do not use Latex for math expressions.'
        elif prompt.lower().startswith('solve'):
            prompt = 'How do I solve ' + prompt[5:] + ' Do not use Latex for math expressions.'
            chatType = 'math'
        elif prompt.lower().startswith('puzzle'):
            chatType = 'logic'
            prompt = prompt[6:]
        if deepseek:
            prompt = prompt + '. Do not use Latex for math expressions.'
        if past == []:
            if mode == 'News':
                if news_interval != "None":
                    news = get_brave_news(prompt, news_interval)
                    augmented_prompt = f'{news}\n{prompt}\nGive highest priority to information just provided\n'
                    augmented_prompt += 'Mention item source and item age for each item used\n'
            elif mode == 'Search':
                news = get_brave_search_results(prompt)
                augmented_prompt = f'{news}\nThe topic is: {prompt}\nGive highest priority to information just provided\n'
                augmented_prompt += ' \n' + query
                augmented_prompt += ' \n Do not use Latex for math expressions.'
        past.append({"role":"user", "content":augmented_prompt})
        gen_image = (uploaded_image_file != '')
        if chatType in special_chat_types:
           (reply, tokens_in, tokens_out, tokens) = solve(prompt, chatType)
           final_text = reply
           reply = md(reply)
           reporting_model = image_gen_model
        elif not gen_image:
            if deepseek:
                if using_groq:
                    client = OpenAI(api_key=GROQ_KEY, base_url='https://api.groq.com/openai/v1')
                    completion = client.chat.completions.create(
                        temperature=0.6,
                        model= ds_model,
                        messages=past,
                        )
                    reporting_model='deepseek70-groq'
                else:
                    client = OpenAI(api_key=DEEPSEEK_KEY, base_url='https://api.together.xyz/v1')
                    completion = client.chat.completions.create(
                        temperature=0.6,
                        model= ds_model,
                        messages=past,
                        max_tokens=16000
                        )
                    reporting_model='deepseek-together-' + ds_model[-3:].replace('.5B','1.5B')
                    if completion.choices[0].finish_reason == 'length':
                        finish_reason = "Truncated due to token limit"
            else:
                completion = Client().chat.completions.create(model=gptModel,
                                            messages=past)
                reporting_model = gptModel
        else:
            (completion, msg) = analyze_image(user_window, image_gen_model, prompt)
            uploaded_image_file= ''
            reporting_model = image_gen_model
            if not msg == 'ok':
                return [past, msg, None, gptModel, uploaded_image_file, plot, image_out, file_out, uploaded_file_path]
        if not chatType in special_chat_types:
            reply = completion.choices[0].message.content
            # if 'groq' in reporting_model:
            if deepseek:
                reply = convert_latex_math(reply)
            final_text = reply
            if deepseek:
                loc1 = reply.find('<think>')
                if loc1 > -1:
                    loc2 = reply.find('</think>')
                    if loc2 > loc1:
                        final_text = reply[loc2 + 8:]
                reply = reply.replace('<think>','\n***Thinking***\n').replace('</think>','\n***Done thinking***\n')
            tokens_in = completion.usage.prompt_tokens
            tokens_out = completion.usage.completion_tokens
            tokens = completion.usage.total_tokens
        if len(query) > 0:
            prompt = 'Search topic = ' + prompt + ', query = ' + query
        response += "\n\n***YOU***: " + prompt + "\n\n***GPT***: " +  reply.replace('```','\n\n```\n\n')
        if isBoss:
            response += md(f"\n\n{reporting_model}: tokens in/out = {tokens_in}/{tokens_out}\n")
        if finish_reason != 'ok':
            response += md(f"\n{finish_reason}\n")
        if tokens > 40000:
            response += "\n\nTHIS DIALOG IS GETTING TOO LONG. PLEASE RESTART CONVERSATION SOON."
        past.append({"role":"assistant", "content": final_text})
        if not deepseek and not reasoning:
            accessOk = False
            for i in range(3):
                try:
                    dataFile = new_func(user_window)
                    with open(dataFile, 'a') as f:
                        m = '4o'
                        if 'mini' in reporting_model:
                            m = '4omini'
                        f.write(f'{user_window}:{tokens_in}/{tokens_out}-{m}\n')
                    accessOk = True
                    break
                except Exception as e:
                    sleep(3)
            if not accessOk:
                response += f"\nDATA LOG FAILED, path = {dataFile}"
        return [past, response , None, gptModel, uploaded_image_file, plot, image_out, file_out, uploaded_file_path]
    else:
        return [[], "User name and/or password are incorrect", prompt, gptModel, uploaded_image_file, plot, image_out, file_out, uploaded_file_path]

def new_func(user):
    dataFile = dataDir + user + '_log.txt'
    return dataFile

def image_count_path(user):
    fpath = dataDir + user + '_image_count.txt'
    return fpath

def transcribe(user, pwd, fpath):
    user = user.lower().strip()
    pwd = pwd.lower().strip()
    if not (user in unames and pwd in pwdList):
        return 'Bad credentials'
    with audioread.audio_open(fpath) as audio:
        duration = int(audio.duration)
        if duration > 0:
            with open(dataDir + user + '_audio.txt','a') as f:
                f.write(f'audio:{str(duration)}\n')
    with open(fpath,'rb') as audio_file:
        transcript = Client().audio.transcriptions.create(
            model='whisper-1', file = audio_file ,response_format = 'text' )
    reply = transcript
    return str(reply)

def pause_message():
    return "Audio input is paused.  Resume or Stop as desired"

# def gen_output_audio(txt):
#     if len(txt) < 10:
#         txt = "This dialog is too short to mess with!"
#     response = Client().audio.speech.create(model="tts-1", voice="fable", input=txt)
#     with open(speak_file, 'wb') as fp:
#         fp.write(response.content)
#     return speak_file


# def set_speak_button(txt):
#     vis = False
#     if txt and len(txt) > 2:
#         vis = True
#     return gr.Button(visible=vis)

def update_user(user_win):
    user_win = user_win.lower().strip()
    user = 'unknown'
    for s in unames:
        if user_win == s:
            user = s
            break
    return [user, user]

def speech_worker(chunks=[],q=[]):
    for chunk in chunks:
        fpath = q.pop(0)
        response = Client().audio.speech.create(model="tts-1", voice="fable", input=chunk, speed=0.85, response_format='wav')
        with open(fpath, 'wb') as fp:
            fp.write(response.content)

def gen_speech_file_names(user, cnt):
    rv = []
    for i in range(0, cnt):
        rv.append(dataDir + f'{user}_speech{i}.wav')
    return rv

def final_clean_up(user, do_b64 = False):
    user = user.strip().lower()
    if user == 'kill':
        flist = glob(dataDir + '*')
    elif user == 'all':
        flist = glob(dataDir + '*_speech*.wav')
        if do_b64:
            flist.extend(glob(dataDir + '*.b64'))
    else:
        flist = glob(dataDir + f'{user}_speech*.wav')
        if do_b64:
            flist.append(dataDir + user + '_image.b64')
    for fpath in flist:
        try:
            os.remove(fpath)
        except:
            continue

def delete_image(user):
    fpath = dataDir + user + '.png'
    if os.path.exists(fpath):
        os.remove(fpath)

def list_permanent_files():
    flist = os.listdir(dataDir)
    others = []
    log_cnt = 0
    wav_cnt = 0
    other_cnt = 0
    list_logs = []
    for fpath in flist:
        if fpath.endswith('.txt'):
            log_cnt += 1
            list_logs.append(fpath)
        elif fpath.endswith('.wav'):
            wav_cnt += 1
        else:
            others.append(fpath)
    other_cnt = len(others)
    if log_cnt > 5:
        list_logs = []
    return (str(log_cnt), str(wav_cnt), str(other_cnt), str(others), list_logs)

def make_image(prompt, user, pwd):
    user = user.lower().strip()
    msg = 'Error: unable to create image.'
    fpath = None
    model = 'dall-e-2'
    quality = 'standard'
    size = '512x512'
    if user in unames and pwd == pwdList[unames.index(user)]:
        if len(prompt.strip()) == 0:
            return [gr.Image(value=None, visible=False), 'You must provide a prompt describing image you desire']
        if prompt.startswith('hd '):
            prompt = prompt[3:]
            model = 'gpt-image-1'   #'dall-e-3'
            size = '1024x1024'
            quality = 'high'  #hd'
            try:
                response = Client().images.generate(model=model, prompt=prompt,size=size,
                     quality=quality) # response_format='b64_json',
            except Exception as ex:
                msg = ex.message
                return [gr.Image(visible=False, value=None), msg]
        else:
            try:
                response = Client().images.generate(model=model, prompt=prompt,size=size,
                    response_format='b64_json')
            except Exception as ex:
                msg = ex.message
                return [gr.Image(visible=False, value=None), msg]
        if len(response.data) == 0:
            msg = "OpenAI returned no image data"
            return [gr.Image(visible=False, value=None), msg]
        try:
            image_data = response.data[0].b64_json 
            with Image.open(BytesIO(base64.b64decode(image_data))) as image:
                fpath = dataDir + user + '.png'
                image.save(fpath)
            with open(image_count_path(user), 'at') as fp:
                if quality == 'hd':
                    fp.write('hd\n')
                else:
                    fp.write('1\n')
            msg = 'Image created!'
        except:
            return [gr.Image(visible=False, value=None), msg]
    else:
        msg = 'Incorrect user name or password'
        return [gr.Image(visible=False, value=None), msg]
    return [gr.Image(visible=True, value=fpath), msg]

def show_help():
    txt = '''
    1.  Gemeral:
        1.1 Login with user name and password (not case-sensitive)
        1.2 Type prompts (questions, instructions) into "Prompt or Question" window (OR) you can speak prompts by
           tapping the audio "Record" button, saying your prompt, then tapping the "Stop" button.
           Your prompt will appear in the Prompt window, and you can edit it there if needed.
        1.3 Text in the "Dialog" window can be spoken by tapping the "Speak Dialog" button.
    2.  Select Mode:
        2.1 Chat mode interacts with the GPT model with info limited to when last trained.
        2.2 News mode searches the internet for news posted within the period selected in "News Window"
        2.3 Search mode searches the internet based on prompt as topic. Optionally if you prompt with
             \<topic\> **q:** \<question\>, it searches topic and answers question based on search results.
    3.  Chat:
        3.1 Enter prompt and tap the "Submit Prompt/Question" button.  The responses appear in the Dialog window.
        3.2 Enter follow-up questions in the Prompt window either by typing or speaking. Tap the voice
              entry "Reset Voice Entry" button to enable additional voice entry. Then tap "Submit Prompt/Question".
        3.3 If topic changes or when done chatting, tap the "Restart Conversation" button.
    4.  Solve math equations or logic problems providing step-by-step analysis, using Chat mode:
        4.1 Math:  Make "solve" the first word in your prompt, followed by the equation, e.g., x^2 - x + 1 = 0
        4.2 Logic: Make "puzzle" the first word in your prompt, followed by a detailed description of a logic
           problem with the answer(s) you desire.
    5.  Make Image:
        5.1 Enter description of desired image in prompt window via either typing or voice entry
        5.2 Tap the "Make Image" button.  This can take a few seconds.
        5.3 There is a download button on the image display if your system supports file downloads.
        5.4 When done viewing image, tap the "Restart Conversation" button
    6.  Analyze an Image you provide:
        6.1 Enter what you want to know about the image in the prompt window. You can include instructions
           to write a poem about something in the image, for example.  Or just say "what's in this image?"
        6.2 Tap the "Upload Image to Analyze" button.
        6.3 An empty image box will appear lower left. Drag or upload image into it. It offers web cam or camera
               input also.
        6.4 The image should appear. This can take some time with a slow internet connection and large image.
        6.5 Tap the "Submit Prompt/Question" button to start the analysis.  This initiates a chat dialog and
               you can ask follow-up questions. However, the image is not re-analyzed for follow-up dialog.
    Hints:
        1. Better chat and image results are obtained by including detailed descriptions and instructions
            in the prompt.
        2. Always tap "Restart Conversation" before requesting an image or changing topics.
        3. Audio input and output functions depend on the hardware capability of your device.
        4. "Speak Dialog" will voice whatever is currently in the Dialog window.  You can repeat it and you
             can edit what's to be spoken.  Except:  In a chat conversation, spoken dialog will only include
             the latest prompt/response ("YOU:/GPT:") sequence.'''
    return str(txt).replace('```', ' ').replace('  ', '&nbsp;&nbsp;').replace('  ', '&nbsp;&nbsp;').replace('  ', '&nbsp;&nbsp;').replace('\n','<br>')

def upload_image(prompt, user, password, mode):
    if not (user in unames and password == pwdList[unames.index(user)]):
        return [gr.Image(visible=False, interactive=True), "Incorrect user name and/or password"]
    if len(prompt) < 3 and mode != 'Advanced':
        return [gr.Image(visible=False, interactive=True), "You must provide prompt/instructions (what to do with the image)"]
    return [gr.Image(visible=True, interactive=True), '']

def load_image(image, user):
    status = 'OK, image is ready! Tap "Submit Prompt/Question" to start analyzing'
    try:
        with open(image, 'rb') as image_file:
            base64_image = base64.b64encode(image_file.read()).decode('utf-8')
        fpath = dataDir + user + '_image.b64'
        with open(fpath, 'wt') as fp:
            fp.write(base64_image)
    except:
        status = 'Unable to upload image'
    return [fpath, status]

def analyze_image(user, model, prompt):
    status = 'ok'
    try:
        with open(dataDir + user + '_image.b64', 'rt') as fp:
            base64_image = fp.read()
    except:
        status = "base64 image file not found"
        return [None, status]

    completion = Client().chat.completions.create(
        model=model,
        messages=[
            { "role": "user",
               "content": [
                   {
                       "type": "text",
                       "text": prompt
                    },
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": f"data:image/jpeg;base64,{base64_image}",
                            "detail": "high"
                            }
                    }
                   ]
               }
            ],
        max_tokens= 500
    )
    # response = completion.choices[0].message.content
    return [completion, status]

def mode_change(mode):
    if mode != "News":
        return gr.Dropdown(visible=False)
    else:
        return gr.Dropdown(visible=True, value='pd')

def upload_file(user, password):
    if not (user in unames and password == pwdList[unames.index(user)]):
        return [gr.File(visible=False, label='Upload File'), 'Incorrect user and/or password']
    return [gr.File(visible=True, label='UploadFile'), '']

def load_file(file, user):
    fname = os.path.basename(file.name)
    out_path = dataDir + user + '-' + fname
    with open(file.name, 'rb') as fp:
        data = fp.read()
        with open(out_path, 'wb') as fp2:
            fp2.write(data)
    return [out_path, f'File {fname} uploaded\n']

#     return [value

# outputs=[uploaded_file_path, output_window]

with gr.Blocks(theme=gr.themes.Soft()) as demo:
    history = gr.State([])
    password = gr.State("")
    user = gr.State("unknown")
    model = gr.State('gpt-4o-mini')     #"gpt-4o-mini") 'gpt-5-mini'
    q = gr.State([])
    qsave = gr.State([])
    uploaded_image_file = gr.State('')
    uploaded_file_path = gr.State('')

    def clean_up(user):
        flist = glob(dataDir + f'{user}_speech*.wav')
        for fpath in flist:
            try:
                os.remove(fpath)
            except:
                continue

    def initial_audio_output(txt, user):
        global digits
        global abbrevs
        if not user in unames:
            return [gr.Audio(sources=None), []]
        clean_up(user)
        q = []
        if len(txt.strip()) < 5:
            return ['None', q]
        try:
            loc = txt.rindex('YOU:')
            txt = txt[loc:]
        except:
            pass
        for s,x in abbrevs.items():
            txt = txt.replace(s, x)
        words_in = txt.replace('**', '').replace('&nbsp;','').split('<br>')
        words_out = []
        for s in words_in:
            s = s.lstrip('- *@#$%^&_=+-')
            if len(s) > 0:
                loc = s.find(' ')
                if loc > 1:
                    val = s[0:loc]
                    isnum = val.replace('.','0').isdecimal()
                    if isnum:
                        if val.endswith('.'):
                            val = val[:-1].replace('.',' point ') + '., '
                        else:
                            val = val.replace('.', ' point ') + ', '
                        s = 'num'+ val + s[loc:]
                words_out.append(s)
        chunklist = []
        for chunk in words_out:
            if chunk.strip() == '':
                continue
            isnumbered = chunk.startswith('num')
            number = ''
            loc = 0
            if isnumbered:
                chunk = chunk[3:]
                loc = chunk.index(',')
                number = chunk[0:loc]
                chunk = chunk[loc:]
            locs = []
            for i in range(1,len(chunk)-1):
                (a, b, c) = chunk[i-1:i+2]
                if a.isdecimal() and b == '.' and c.isdecimal():
                    locs.append(i)
            for i in locs:
                chunk = chunk[:i] + ' point ' + chunk[i+1:]
            if len(chunk) > 50:
                finechunks = chunk.split('.')
                for fchunk in finechunks:
                    if isnumbered:
                        fchunk = number + fchunk
                        isnumbered = False
                    if len(fchunk) > 0:
                        if fchunk != '"':
                            chunklist.append(fchunk)
            else:
                line = number + chunk
                if line != '"':
                    chunklist.append(line)
        total_speech = 0
        for chunk in chunklist:
            total_speech += len(chunk)
        with open(dataDir + user + '_speech.txt','a') as f:
            f.write(f'speech:{str(total_speech)}\n')
        chunk = chunklist[0]
        if chunk.strip() == '':
            return gr.Audio(sources=None)
        fname_list = gen_speech_file_names(user, len(chunklist))
        q = fname_list.copy()
        qsave = fname_list.copy()
        fname = q.pop(0)
        if len(chunklist) > 0:
            threading.Thread(target=speech_worker, daemon=True, args=(chunklist[1:],fname_list[1:])).start()
        response = Client().audio.speech.create(model="tts-1", voice="fable", input=chunk, speed=0.85, response_format='wav')
        with open(fname, 'wb') as fp:
            fp.write(response.content)
        return [fname, q]

    def gen_output_audio(q, user):
        try:
            fname = q.pop(0)
        except:
            final_clean_up(user)
            return [None, gr.Audio(sources=None)]
        if not os.path.exists(fname):
            sleep(3)
            if not os.path.exists(fname):
                response = Client().audio.speech.create(model="tts-1", voice="fable",
                    input='Sorry, text-to-speech is responding too slow right now', speed=0.85, response_format='wav')
                with open(fname, 'wb') as fp:
                    fp.write(response.content)
                q = []
        return [fname, q]


    gr.Markdown('# GPT Chat')
    gr.Markdown('Enter user name & password.  Tap "Help & Hints" button for more instructions.')
    with gr.Row():
        user_window = gr.Textbox(label = "User Name")
        user_window.blur(fn=update_user, inputs=user_window, outputs=[user, user_window])
        pwd_window = gr.Textbox(label = "Password")
        help_button = gr.Button(value='Help & Hints')
    with gr.Row():
        audio_widget = gr.Audio(type='filepath', format='wav',waveform_options=gr.WaveformOptions(
           show_recording_waveform=True), sources=['microphone'], scale = 3, label="Prompt/Question Voice Entry") # , max_length=120)
        reset_button = gr.ClearButton(value="Reset Voice Entry", scale=1) #new_func1()
    with gr.Row():
        clear_button = gr.Button(value="Restart Conversation")
        # gpt_chooser=gr.Radio(choices=[("GPT-3.5","gpt-3.5-turbo"),("GPT-4o","gpt-4o-mini")],
        #                      value="gpt-3.5-turbo", label="GPT Model", interactive=True)
        button_do_image = gr.Button(value='Make Image')
        button_upload_file = gr.Button(value='Upload Input File')
        button_get_image = gr.Button(value='Upload Image to Analyze')
        speak_output = gr.Button(value="Speak Dialog", visible=True)
        submit_button = gr.Button(value="Submit Prompt/Question")
    with gr.Row():
        prompt_window = gr.Textbox(label = "Prompt or Question", scale=7)
        mode = gr.Dropdown(choices=[ 'Chat', 'News', 'Search'], label='Mode', scale=1, interactive=True)
        news_period = gr.Dropdown(choices=news_interval_choices,
                                 interactive=True,label='News Window',scale=1, visible=False)
    gr.Markdown('### **Dialog:**')
    #output_window = gr.Text(container=True, label='Dialog')
    output_window = gr.Markdown(container=True)
    file_download = gr.DownloadButton(label='Download File', visible=False, value=None)
    with gr.Row():
        with gr.Column():
            image_window2 = gr.Image(visible=False, interactive=True, label='Image to Analyze', type='filepath')
        with gr.Column():
            image_window = gr.Image(visible=False, label='Generated Image')
    with gr.Row():
        file_uploader = gr.File(visible=False, label='Upload File', type='filepath')
    with gr.Row():
        plot = gr.LinePlot(test_plot_df(), x="month", y="value", visible=False, label="Portfolio Value History")
    submit_button.click(chat,
             inputs=[prompt_window, user_window, password, history, output_window, model,
                    uploaded_image_file, plot, news_period, mode, uploaded_file_path],
             outputs=[history, output_window, prompt_window, model, uploaded_image_file, plot,
                     image_window, file_download, uploaded_file_path])
    clear_button.click(fn=new_conversation, inputs=user_window,
                      outputs=[prompt_window, history, output_window, image_window, image_window2,
                      uploaded_image_file, plot, news_period, mode, file_download, uploaded_file_path,
                      file_uploader])
    audio_widget.stop_recording(fn=transcribe, inputs=[user_window, password, audio_widget],
                                outputs=[prompt_window])
    audio_widget.pause_recording(fn=pause_message, outputs=[prompt_window])
    reset_button.add(audio_widget)
    audio_out = gr.Audio(autoplay=True, visible=False)
    audio_out.stop(fn=gen_output_audio, inputs=[q, user_window], outputs = [audio_out, q])
    speak_output.click(fn=initial_audio_output, inputs=[output_window, user_window], outputs=[audio_out, q])
    # output_window.change(fn=set_speak_button, inputs=output_window,outputs=speak_output)
    button_do_image.click(fn=make_image, inputs=[prompt_window,user_window, password],outputs=[image_window, output_window])
    image_window.change(fn=delete_image, inputs=[user])
    help_button.click(fn=show_help, outputs=output_window)
    button_get_image.click(fn=upload_image,inputs = [prompt_window, user, password, mode],
                          outputs = [image_window2, output_window])
    image_window2.upload(fn=load_image, inputs=[image_window2, user], outputs=[uploaded_image_file, output_window])
    mode.change(fn=mode_change, inputs=mode,outputs=news_period)
    pwd_window.blur(updatePassword, inputs = pwd_window, outputs = [password, pwd_window, mode])
    button_upload_file.click(fn=upload_file, inputs=[user, password],
                            outputs=[file_uploader, output_window])
    file_uploader.upload(fn=load_file, inputs=[file_uploader, user], outputs=[uploaded_file_path, output_window])
    # demo.unload(final_clean_up(user))
demo.launch(share=True, allowed_paths=[dataDir], ssr_mode=False)