from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
import tensorflow_hub as hub
import pandas as pd
import re
import numpy as np
from sentence_transformers import SentenceTransformer

import gradio as gr
import sqlite3
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from nltk.stem import PorterStemmer
import pandas as pd
import sqlite3
import torch
import transformers 
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import gradio as gr
import datefinder
from queue import Queue
from nltk.stem import PorterStemmer
# Load similarity model
from sentence_transformers import SentenceTransformer
import psycopg2 

conn = None
try: 
	# connect to the PostgreSQL server 
	print('Connecting to the PostgreSQL database...') 
	conn = psycopg2.connect( 
		host = '107.110.152.126',
        dbname = 'new',
        user = 'datamartuser',
        password = 'Password1234',
        port = 4432
	) 
	conn.autocommit = True
	# Creating a cursor with name cur. 
	cur = conn.cursor() 
	print('Connected to the PostgreSQL database') 
	
	# Execute a query: 
	# To display the PostgreSQL 
	# database server version 
	
	
except(Exception, psycopg2.DatabaseError) as error: 
	print(error) 

retrieval_model_path = "NlpSql//model_jatin//"
retrieval_model = SentenceTransformer(retrieval_model_path)


# Load LLM model
model_path = "weights/sql-weights"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path,device_map='cuda')

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = model.to(device)

# Load data
data = pd.read_csv('NlpSql/Data/dataaa.txt', sep="\t", header=None)

headers = data.iloc[0]
new_df = pd.DataFrame(data.values[1:], columns=headers)
new_df['date'] = pd.to_datetime(new_df['date']).dt.strftime('%d-%b-%Y')
new_df['date'] = pd.to_datetime(new_df['date'], errors='coerce')

# Convert dates back to a string format, or ‘NO_DATE’ for NaT/NaN values
new_df['date'] = new_df['date'].dt.strftime('%d-%b-%Y').fillna('NO_DATE')



def preprocess_input(usertext):
    dates = list(datefinder.find_dates(usertext))
    if dates:
        # Use the first detected date
        formatted_date = dates[0].strftime("%d-%b-%Y")
        filtered_df = new_df[new_df['date'] == formatted_date]  # Filter for matching date
    else:
        # Filter for entries without a valid date
        filtered_df = new_df[new_df['date'] == 'NO_DATE']
    if filtered_df.empty:
        return None, "No matching data found for your query."
    return filtered_df, None

def get_most_similar_question(userText, history):
    filtered_df, error_msg = preprocess_input(userText.lower())
    if filtered_df is None:
        # The provided implementation of predict was not given, but it's assumed to handle the null case
        return predict(userText, history)
    filtered_df.loc[:, 'Question'] = filtered_df['Question'].str.lower()
#     filtered_df['Question'] = filtered_df['Question'].str.lower()
    question_list = filtered_df["Question"].to_list()
    question_embeddings = retrieval_model.encode(question_list)
    user_text_embedding = retrieval_model.encode([userText.lower()])

    # Calculate similarity scores
    similarity_scores = np.inner(user_text_embedding, question_embeddings).flatten()
    top_match_idx = np.argmax(similarity_scores)
#     print('similarity_scores',similarity_scores)
    print('top_match_idx',top_match_idx)
    print(similarity_scores[top_match_idx])
    if similarity_scores[top_match_idx] < 0.75:
        return predict(userText, history)
    
    if similarity_scores[top_match_idx] == 0:
        return "Sorry, no similar questions were found for your query."
    
    matched_data = filtered_df.iloc[top_match_idx,[10]]
    
    # Convert matched data to HTML for presentation
    matched_data_df = pd.DataFrame([matched_data.values], columns=matched_data.index)
    html_output = f"<h3>Here is the most similar question for your query:</h3>{matched_data_df.to_html(index=False)}"
    return html_output
# gr.ChatInterface(get_most_similar_question).launch(auth=authenticate_user,inline=True, share=True,server_name= '0.0.0.0', server_port=7897)
	

# Define dummy users for authentication
dummy_users = [
    {"username": "jatin", "password": "qwer@345"},
    {"username": "ajoy", "password": "qwer@345"},
    {"username": "user3", "password": "pass3"},
    {"username": "user4", "password": "pass4"},
    {"username": "user5", "password": "pass5"}
]

# Authenticate user function
def authenticate_user(username, password):
    for user in dummy_users:
        if user["username"] == username and user["password"] == password:
            return True
    return False



porter_stemmer =PorterStemmer()
#--------------------------------------
def fn_preprocess_question(question):
    return ' '.join([porter_stemmer.stem(word) for word in question.split()])
dict_predefined_answers ={
    "Who are you":"I am the SAMS bot, your Virtual Sales Assistant. I’m here to help you navigate through SAMS databases and provide the information you need.",
    "Hi":"Hello, Welcome to SAMS Virtual Sales Assistant. I am designed to assist you in retrieving information from various SAMS databases. Please feel free to ask your queries, such as, 'What is the total sellout across India for July 16, 2022? among others.",
    "What can you do":"I am equipped to offer you comprehensive insights and data from SAMS databases. Whether you need sales figures or specific reports, just ask, and I’ll provide the most accurate information available.Although my training is still going on.",
    "How can I use this service":"Using this service is simple. Just type in your query about any sales or database-related information, like ‘Show the sales trend for product XYZ during the last month,' and I'll fetch the details for you.",
    "I'm not sure what to ask.":"No problem at all. You can start with general queries like 'What were the top-selling products last month?'' or 'Update me on the current monthly sales report.' I am here to guide you through accessing the precise information you need.",
    "Thank you":"You're most welcome! If you have any more questions or need further assistance, I'm here to help. Your success is my priority.",
    "Goodbye":"Farewell! If you ever require any assistance or have more queries in the future, don't hesitate to reach out. Have a great day ahead!",
    "Bye":"Farewell! If you ever require any assistance or have more queries in the future, don't hesitate to reach out. Have a great day ahead!",
    "How are you":"I am good,Please ask anything related to SAMS and I will try to provide you best possible solution.Thanks"
}
def generate(tokenizer,prompt):
    eos_token_id = tokenizer.convert_tokens_to_ids(["```"])[0]
    inputs = tokenizer(prompt, return_tensors="pt").to(device)
    generated_ids = model.generate(
        **inputs,
        num_return_sequences=1,
        eos_token_id=eos_token_id,
        pad_token_id=eos_token_id,
        max_new_tokens=400,
        num_beams=5,
        temperature=0.000001,
        do_sample=True
    )


    outputs = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
#     print('outputs',outputs[0])
    return outputs[0]
#-----    
def predict(question,history):
    print('Question',question)
#     print('history',history)
    try:
#         if username not in user_sessions:
#             return 'User Authentication Failed.'
#         preprocessed_question_var = fn_preprocess_question(question)
        preprocessed_question_var = question
        for predefined_question_var,predefined_answer_var in dict_predefined_answers.items():
            if fn_preprocess_question(predefined_question_var) == preprocessed_question_var:
                return predefined_answer_var


#         conn_local = sqlite3.connect("sales_database.db",check_same_thread=False)
#         cursor_local = conn_local.cursor()

        ### Answer Given the database schema,here is the SQL query that answers [QUESTION]{question}[/QUESTION] [SQL] """.format(question=question)
        prompt = """### Instructions:
        You are a Postgres expert. Given an input question, first create a syntactically correct Postgres query to run, then look at the results of the query and return the answer to the input question.
        Unless the user specifies in the question a specific number of examples to obtain, query for at most 5 results using the LIMIT clause as per Postgres. You can order the results to return the most informative data in the database.
        Never query for all columns from a table. You must query only the columns that are needed to answer the question. Wrap each column name in double quotes (") to denote them as delimited identifiers.
        Pay attention to use only the column names you can see in the tables below. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.
        Pay attention to use date('now') function to get the current date, if the question involves "today".
        Please make accurate predictions regarding the the column values and return the right column name. If values are repeating in more than one column(s) return the first column name where the value was found.
        Always explicitly type cast the first parameter of round() function as 'integer'.
        
        ### Input:
        Generate a SQL query that answers the question `{question}`.
        This query will run on a database whose schema is represented in this string:        
        
       CREATE TABLE IF NOT EXISTS mx_sellout (
        year INTEGER,--Year of sale
        month numeric(18,10),--Month of sale

        weekyear character varying(100) COLLATE pg_catalog."default",--Week and Year of sale
        day INTEGER,--Day of sale
        date date,--Date of sale
        salestype character varying(100) COLLATE pg_catalog."default", --Type of sales like Sellin, SellThru, Sellout

        salescategory character varying(100) COLLATE pg_catalog."default",--Sales Category like Normal, Demo, Free of Cost etc

        channel character varying(100) COLLATE pg_catalog."default",--Sales Channel like General Trade, Modern Trade, Online, Business to Business or NA

        priceband character varying(100) COLLATE pg_catalog."default", --Price bands of Product like Less than 6K, 6K to 10K, 10K to 15K, 15K to 20K, 20K to 30K, 30K to 40K, Greater than 40K etc

        region character varying(100) COLLATE pg_catalog."default",--Sales Regions like Online, B2B and for GT the regions are East1, East2, West1, West2, West3, North1, North2, North3, North4, South1, South2, South3

        productgroup character varying(100) COLLATE pg_catalog."default" -- product group

        segment character varying(100) COLLATE pg_catalog."default",--Segment of sale like Smatphone, Tablet, Note PC Laptop, Wearables
        modelname character varying(100) COLLATE pg_catalog."default",--Product name or Market name or Model name,
        model character varying(100) COLLATE pg_catalog."default",--Product code or Market code or Model code

        quantity double precision,--Sales quantity
        amount double precision,--Sales amount in INR
      );

        CREATE TABLE IF NOT EXISTS mx_dlb (
                     year INTEGER, -- year of sellout/sale,
                     weekyear character varying(100) COLLATE pg_catalog."default",--weekyear of sellout,
                     day integer,
                     date date,
                     month numeric(18,10),--month of sellout
                     counterselloutvalue double precision,-- value of sellout/amount
                     counterselloutvolume double precision,-- volume of sellout
                     region character varying(100) COLLATE pg_catalog."default",-- region of sale
                     competitor character varying(100) COLLATE pg_catalog."default",--competitor/competition name
                     priceband character varying(100) COLLATE pg_catalog."default",-- price bands of different model/modelcode sold
                     salestype character varying(100) COLLATE pg_catalog."default",-- salestype of the sale
                     channel character varying(100) COLLATE pg_catalog."default",-- channel through which it is sold
                     segment character varying(100) COLLATE pg_catalog."default",
                     modelname character varying(100) COLLATE pg_catalog."default",-- market name
        );
      ### Response:
        Based on your instructions, here is the SQL query I have generated to answer the question `{question}`:
        ```sql
        """.format(question=question,history=history)
        outputs = generate(tokenizer,prompt)
        generated_sql = outputs.split("```sql")[-1].split("```")[0].split(";")[0].strip()
        print(generated_sql)
#         generated_sql = generated_sql.replace('::numeric','')
        cur.execute(generated_sql) 
        result = cur.fetchall()
#         print(result)
#         cur.close() 
        df = pd.DataFrame(result,columns=[desc[0] for desc in cur.description])
        sql_datagram = generated_sql
#         print(generated_sql)
        # Create a DataFrame with the SQL datagram as the first row
        df_sql_row = pd.DataFrame([{'Generated_SQL': sql_datagram}])

        # Concatenate the SQL row with your existing DataFrame (df)
        df_with_sql = pd.concat([df,df_sql_row], keys=['',''],ignore_index=True)

        # Convert the DataFrame with SQL to HTML
        df_html_with_sql = df.to_html(index=False)
#         prompt1='''### Instructions:
#         You are a data analyst, please provide your two line comment on the given result `{result}` also keep in mind the question asked by user which is `{question}` . 
#         ### Response:
#         Based on your instructions, here is the Analysis I have generated for dataframe `{result}`:
#         ```insights'''.format(result=result,question=question)
#     #     df_str = df.to_string(index=False)
#         outputs_1= generate(tokenizer,prompt1)
#         print('outputs_1[0]',outputs_1)
#         insgihts = outputs_1.split("```insights")[-1].split("```")[0].split(";")[0].strip()
#         print(insgihts)
        html_output_1 = f"<h3>Sql Query : \n\n{generated_sql}\n\n</h3>{df_html_with_sql}\n<h2>Above results are derived using the Generated SQL query.</h2>"
        
        return html_output_1
    except Exception as e:
        print(f"An exception occurred:{str(e)}")
        return "The answer for this query is not available right now.Kindly modify the query that can best fit the database schema.Please provide updated query."

gr.ChatInterface(get_most_similar_question).launch(auth=authenticate_user,inline=True, share=True,server_name= '0.0.0.0', server_port=7898)
       

Store the question asked by user and generated sql return by llm or predefined question or answer from similarity model into a table in postgresql