app.py

import gradio as gr
import time
import random
import json
import mysql.connector
import os
import csv
import spaces
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from threading import Thread
from typing import Iterator
from huggingface_hub import Repository, hf_hub_download
from datetime import datetime

# for fetch_personalized_data
import mysql.connector
import urllib.parse
import urllib.request

# for saving chat history as JSON - not used 
import atexit
import os
from huggingface_hub import HfApi, HfFolder

# for saving chat history as dataset - not used
import huggingface_hub
from huggingface_hub import Repository
from datetime import datetime

# for saving chat history as dataset - used 
import sqlite3
import huggingface_hub
import gradio as gr
import pandas as pd
import shutil
import os
import datetime
from apscheduler.schedulers.background import BackgroundScheduler

DATASET_REPO_URL = "https://huggingface.co/datasets/botsi/trust-game-llama-2-chat-history"
DATA_DIRECTORY = "data"  # Separate directory for storing data files
DATA_FILENAME = "newmarion.csv"  # Default filename
DATA_FILE = os.path.join("data", DATA_FILENAME)

HF_TOKEN = os.environ.get("HF_TOKEN")
print("is none?", HF_TOKEN is None)
print("hfh", huggingface_hub.__version__)

repo = Repository(
    local_dir=DATA_DIRECTORY, clone_from=DATASET_REPO_URL
)

MAX_MAX_NEW_TOKENS = 2048
DEFAULT_MAX_NEW_TOKENS = 1024
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))

DESCRIPTION = """\
# This is your personal space to chat. 
You can ask anything. 
"""

# License and Acceptable Use Policy by Meta  
LICENSE = """
<p/>

---
This demo is governed by the [original license](https://ai.meta.com/llama/license/) and [acceptable use policy](https://ai.meta.com/llama/use-policy/). 
The most recent copy of this policy can be found at ai.meta.com/llama/use-policy.
"""

if not torch.cuda.is_available():
    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"


if torch.cuda.is_available():
    model_id = "meta-llama/Llama-2-7b-chat-hf"
    model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
    tokenizer = AutoTokenizer.from_pretrained(model_id)
    tokenizer.use_default_system_prompt = False
 
## trust-game-llama-2-7b-chat
# app.py 
def get_default_system_prompt():
    BSYS, ESYS = "<<SYS>>\n", "\n<</SYS>>\n\n"

    DEFAULT_SYSTEM_PROMPT = f"""You are a friendly chat assistant. First, wait for a question. Do not talk before being asked. When the user says hi, politely ask what he/she wants to know.
    Do not tell any more! 
    If asked about Marion, you can answer the following: There are only two Marions, one is currently working at Accenture and the other one will shortly begin a new job at BMW. 
    She recently quit Accenture. Nicknames by her friends include botsi and botsokrates. Her favorite project was Hypercare. Kathy/Kathi is her colleague at Accenture. Anita is her colleague at Accenture.
    """
    print(DEFAULT_SYSTEM_PROMPT)
    return DEFAULT_SYSTEM_PROMPT

## trust-game-llama-2-7b-chat
# app.py
def construct_input_prompt(chat_history, message):
    input_prompt = f"<s>[INST] <<SYS>>\n{get_default_system_prompt()}\n<</SYS>>\n\n "
    for user, assistant in chat_history:
        input_prompt += f"{user} [/INST] {assistant} <s>[INST] "
    input_prompt += f"{message} [/INST] "
    return input_prompt

## trust-game-llama-2-7b-chat
# app.py 
@spaces.GPU
def generate(
    request: gr.Request, # To fetch query params
    message: str,
    chat_history: list[tuple[str, str]],
    # input_prompt: str,
    max_new_tokens: int = 1024,
    temperature: float = 0.6,
    top_p: float = 0.9,
    top_k: int = 50,
    repetition_penalty: float = 1.2,
) -> Iterator[str]: # Change return type hint to Iterator[str]

    conversation = []
    
    # Fetch query params 
    params = {
        key: value for key, value in request.query_params.items()
    }
    print('those are the query params')
    print(params)

    print("Request headers dictionary:", request.headers)
    print("IP address:", request.client.host)
    print("Query parameters:", params)
    
    # Construct the input prompt using the functions from the system_prompt_config module
    input_prompt = construct_input_prompt(chat_history, message)

    # Move the condition here after the assignment
    if input_prompt:
        conversation.append({"role": "system", "content": input_prompt})

    # Convert input prompt to tensor
    input_ids = tokenizer(input_prompt, return_tensors="pt").to(model.device)

    for user, assistant in chat_history:
        conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
    conversation.append({"role": "user", "content": message})

    input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt")
    if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
        input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
        gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
    input_ids = input_ids.to(model.device)

    # Set up the TextIteratorStreamer
    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
    
    # Set up the generation arguments
    generate_kwargs = dict(
        input_ids=input_ids,
        streamer=streamer,
        max_new_tokens=max_new_tokens,
        do_sample=True,
        top_p=top_p,
        top_k=top_k,
        temperature=temperature,
        num_beams=1,
        repetition_penalty=repetition_penalty,
    )

    # Start the model generation thread
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

    # Yield generated text chunks
    outputs = []
    for text in streamer:
        outputs.append(text)
        yield "".join(outputs)

    # Fix bug that last answer is not recorded! 
    # Parse the outputs into a readable sentence and record them
    # Filter out empty strings and join the remaining strings with spaces
    readable_sentence = ' '.join(filter(lambda x: x.strip(), outputs))
    # Print the readable sentence
    print(readable_sentence)

    # Save chat history to .csv file on HuggingFace Hub 
    # Generate filename with bot id and session id
    filename = f"{DATA_FILENAME}"
    data_file = os.path.join(DATA_DIRECTORY, filename)
    
    # Generate timestamp
    timestamp = datetime.datetime.now()
    
    # Check if the file already exists
    if os.path.exists(data_file):
        # If file exists, load existing data
        existing_data = pd.read_csv(data_file)
        
        # Add timestamp column
        conversation_df = pd.DataFrame(conversation)
        conversation_df['ip_address'] = request.client.host
        conversation_df['readable_sentence'] = readable_sentence
        conversation_df['timestamp'] = timestamp
        
        # Append new conversation to existing data
        updated_data = pd.concat([existing_data, conversation_df], ignore_index=True)
        updated_data.to_csv(data_file, index=False)
    else:
        # If file doesn't exist, create new file with conversation data
        conversation_df = pd.DataFrame(conversation)
        conversation_df['ip_address'] = request.client.host
        conversation_df['readable_sentence'] = readable_sentence
        conversation_df['timestamp'] = timestamp
        conversation_df.to_csv(data_file, index=False)

    print("Updating .csv")
    repo.push_to_hub(blocking=False, commit_message=f"Updating data at {timestamp}")

chat_interface = gr.ChatInterface(
    fn=generate,
    retry_btn=None,
    clear_btn=None,
    undo_btn=None,
    chatbot=gr.Chatbot(avatar_images=('user.png', 'bot.png'), bubble_full_width=False), 
    examples=[
        ["What is your favorite fruit?"],
        ["What do you think about AI in the workplace?"],
    ],
)

with gr.Blocks(css="style.css", theme=gr.themes.Default(primary_hue=gr.themes.colors.emerald, secondary_hue=gr.themes.colors.indigo)) as demo:
    gr.Markdown(DESCRIPTION)
    chat_interface.render()
    gr.Markdown(LICENSE)
    
if __name__ == "__main__":
    demo.queue(max_size=20).launch()  # Launching the interface with queueing and maximum size limit
    # demo.launch(share=True, debug=True)  # Uncomment this line if you want to launch the interface with sharing and debug mode






'''# Original code from https://huggingface.co/spaces/huggingface-projects/llama-2-7b-chat
# Modified for trust game purposes

import gradio as gr
import time
import random
import json
import mysql.connector
import os
import csv
import spaces
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from threading import Thread
from typing import Iterator
from huggingface_hub import Repository, hf_hub_download
from datetime import datetime

# for fetch_personalized_data
import mysql.connector
import urllib.parse
import urllib.request

# for saving chat history as JSON - not used 
import atexit
import os
from huggingface_hub import HfApi, HfFolder

# for saving chat history as dataset - not used
import huggingface_hub
from huggingface_hub import Repository
from datetime import datetime

# for saving chat history as dataset - used 
import sqlite3
import huggingface_hub
import gradio as gr
import pandas as pd
import shutil
import os
import datetime
from apscheduler.schedulers.background import BackgroundScheduler


DATASET_REPO_URL = "https://huggingface.co/datasets/botsi/trust-game-llama-2-chat-history"
DATA_DIRECTORY = "data"  # Separate directory for storing data files
DATA_FILENAME = "marion.csv"  # Default filename
DATA_FILE = os.path.join("data", DATA_FILENAME)

HF_TOKEN = os.environ.get("HF_TOKEN")
print("is none?", HF_TOKEN is None)
print("hfh", huggingface_hub.__version__)

repo = Repository(
    local_dir=DATA_DIRECTORY, clone_from=DATASET_REPO_URL
)

MAX_MAX_NEW_TOKENS = 2048
DEFAULT_MAX_NEW_TOKENS = 1024
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))

DESCRIPTION = """\
# This is your personal space to chat. 
You can ask anything. 
"""

# License and Acceptable Use Policy by Meta  
LICENSE = """
<p/>

---
This demo is governed by the [original license](https://ai.meta.com/llama/license/) and [acceptable use policy](https://ai.meta.com/llama/use-policy/). 
The most recent copy of this policy can be found at ai.meta.com/llama/use-policy.
"""

if not torch.cuda.is_available():
    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"


if torch.cuda.is_available():
    model_id = "meta-llama/Llama-2-7b-chat-hf"
    model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
    tokenizer = AutoTokenizer.from_pretrained(model_id)
    tokenizer.use_default_system_prompt = False
 
## trust-game-llama-2-7b-chat
# app.py 
def get_default_system_prompt():
    #BOS, EOS = "<s>", "</s>" 
    #BINST, EINST = "[INST]", "[/INST]"
    BSYS, ESYS = "<<SYS>>\n", "\n<</SYS>>\n\n"

    DEFAULT_SYSTEM_PROMPT = f"""You are a friendly chat assistant. First, wait for a question. Do not talk before being asked. When the user says hi, politely ask what he/she wants to know.
    Do not tell any more! 
    If asked about Marion, you can answer the following: There are only two Marions, one is currently working at Accenture and the other one will shortly begin a new job at BMW. 
    She recently quit Accenture. Nicknames by her friends include botsi and botsokrates. Her favorite project was Hypercare. Kathy/Kathi is her colleague at Accenture. Anita is her colleague at Accenture.
    """
    print(DEFAULT_SYSTEM_PROMPT)
    return DEFAULT_SYSTEM_PROMPT


## trust-game-llama-2-7b-chat
# app.py
def construct_input_prompt(chat_history, message):
    input_prompt = f"<s>[INST] <<SYS>>\n{get_default_system_prompt()}\n<</SYS>>\n\n "
    for user, assistant in chat_history:
        input_prompt += f"{user} [/INST] {assistant} <s>[INST] "
    input_prompt += f"{message} [/INST] "
    return input_prompt

## trust-game-llama-2-7b-chat
# app.py 
@spaces.GPU
def generate(
    request: gr.Request, # To fetch query params
    message: str,
    chat_history: list[tuple[str, str]],
    # input_prompt: str,
    max_new_tokens: int = 1024,
    temperature: float = 0.6,
    top_p: float = 0.9,
    top_k: int = 50,
    repetition_penalty: float = 1.2,
) -> Iterator[str]: # Change return type hint to Iterator[str]

    conversation = []
    
    # Fetch query params 
    params = {
        key: value for key, value in gr.Request.query_params.items()
    }
    print('those are the query params')
    print(params)

    print("Request headers dictionary:", gr.Request.headers)
    print("IP address:", gr.Request.client.host)
    print("Query parameters:", params)
    
    # Construct the input prompt using the functions from the system_prompt_config module
    input_prompt = construct_input_prompt(chat_history, message)

    # Move the condition here after the assignment
    if input_prompt:
        conversation.append({"role": "system", "content": input_prompt})

    # Convert input prompt to tensor
    input_ids = tokenizer(input_prompt, return_tensors="pt").to(model.device)

    for user, assistant in chat_history:
        conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
    conversation.append({"role": "user", "content": message})

    input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt")
    if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
        input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
        gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
    input_ids = input_ids.to(model.device)

    # Set up the TextIteratorStreamer
    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
    
    # Set up the generation arguments
    generate_kwargs = dict(
        {"input_ids": input_ids},
        streamer=streamer,
        max_new_tokens=max_new_tokens,
        do_sample=True,
        top_p=top_p,
        top_k=top_k,
        temperature=temperature,
        num_beams=1,
        repetition_penalty=repetition_penalty,
    )

    # Start the model generation thread
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

    # Yield generated text chunks
    outputs = []
    for text in streamer:
        outputs.append(text)
        yield "".join(outputs)

    # Fix bug that last answer is not recorded! 
    # Parse the outputs into a readable sentence and record them
    # Filter out empty strings and join the remaining strings with spaces
    readable_sentence = ' '.join(filter(lambda x: x.strip(), outputs))
    # Print the readable sentence
    print(readable_sentence)

    # Save chat history to .csv file on HuggingFace Hub 
    # Generate filename with bot id and session id
    filename = f"{DATA_FILENAME}"
    data_file = os.path.join(DATA_DIRECTORY, filename)
    
    # Generate timestamp
    timestamp = datetime.datetime.now()
    
    # Check if the file already exists
    if os.path.exists(data_file):
        # If file exists, load existing data
        existing_data = pd.read_csv(data_file)
        
        # Add timestamp column
        conversation_df = pd.DataFrame(conversation)
        conversation_df['ip_address'] = request.client.host
        conversation_df['readable_sentence'] = readable_sentence
        conversation_df['timestamp'] = timestamp
        
        # Append new conversation to existing data
        updated_data = pd.concat([existing_data, conversation_df], ignore_index=True)
        updated_data.to_csv(data_file, index=False)
    else:
        # If file doesn't exist, create new file with conversation data
        conversation_df = pd.DataFrame(conversation)
        conversation_df['ip_address'] = request.client.host
        conversation_df['readable_sentence'] = readable_sentence
        conversation_df['timestamp'] = timestamp
        conversation_df.to_csv(data_file, index=False)

    print("Updating .csv")
    repo.push_to_hub(blocking=False, commit_message=f"Updating data at {timestamp}")

chat_interface = gr.ChatInterface(
    fn=generate,
    retry_btn=None,
    clear_btn=None,
    undo_btn=None,
    chatbot=gr.Chatbot(avatar_images=('user.png', 'bot.png'), bubble_full_width = False), 
    examples=[
        ["What is your favorite fruit?"],
        ["What do you think about AI in the workplace?"],
    ],
)

with gr.Blocks(css="style.css", theme=gr.themes.Default(primary_hue=gr.themes.colors.emerald,secondary_hue=gr.themes.colors.indigo)) as demo:
    gr.Markdown(DESCRIPTION)
    chat_interface.render()
    gr.Markdown(LICENSE)
    
if __name__ == "__main__":
    demo.queue(max_size=20).launch()
    #demo.queue(max_size=20)
    demo.launch(share=True, debug=True)
'''