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 tensorflow as tf

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


# new_retrieval_model_path = "NlpSql//model//"
# new_retrieval_model = hub.load(new_retrieval_model_path)
conn_global = sqlite3.connect("sales_database.db",check_same_thread=False)
cursor_global = conn_global.cursor()

cursor_global.execute('''
     CREATE TABLE IF NOT EXISTS sales (
        salestype TEXT, --Type of sales like Sellin, SellThru, Sellout
        salescategory TEXT,--Sales Category like Normal, Demo, Free of Cost etc
        channel TEXT,--Sales Channel like General Trade, Modern Trade, Online, Business to Business or NA
        priceband TEXT, --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 TEXT,--Sales Regions like Online, B2B and for GT the regions are East1, East2, West1, West2, West3, North1, North2, North3, North4, South1, South2, South3
        year INTEGER,--Year of sale
        month INTEGER,--Month of sale
        day INTEGER,--Day of sale
        date INTEGER,--Date of sale
        weekyear TEXT,--Week and Year of sale
        segment TEXT,--Segment of sale like Smatphone, Tablet, Note PC Laptop, Wearables
        model TEXT,--Product name or Market name or Model name
        quantity INTEGER,--Sales quantity
        amount INTEGER,--Sales amount in INR
        mtdquantity INTEGER,--Month till date Quantity
        mtdamount INTEGER,--Month till date Amount in INR
        lmtdquantity INTEGER,--Last Month till date quantity
        lmtdamount INTEGER--Last Month till date amount in INR
      );
    ''')

conn_global.commit()

sales_dlb_file = "mx sales 19mar.txt"
df_dlb = pd.read_csv(sales_dlb_file, sep='\t',encoding='cp1252')
df_dlb.to_sql("sales", conn_global, if_exists='replace', index=False)
df_dlb.head()
# sales_dlb_file = "sams_dlb.txt"





cursor_global.execute('''
    CREATE TABLE IF NOT EXISTS sales_dlb (
                     year INTEGER, -- year of sellout/sale,
                     week TEXT,--week of sellout,
                     month INteger,--month of sellout
                     counterselloutvalue INTEGER,-- value of sellout/amount
                     counterselloutvolume INTEGER,-- volume of sellout
                     region TEXT,-- region of sale
                     competitor TEXT,--competitor/competition name
                     priceband TEXT,-- price bands of different model/modelcode sold
                     salestype TEXT,-- salestype of the sale
                     channel TEXT,-- channel through which it is sold
                     status TEXT, -- status of sale
                     modelname TEXT,-- model name or model or market name
                     productgroup TEXT -- product group
        );

    ''')

						
conn_global.commit()

sales_dlb_file = "mx_dlb_26mar_1.txt"
df_dlb = pd.read_csv(sales_dlb_file, sep='\t')
df_dlb.to_sql("sales_dlb", conn_global, if_exists='replace', index=False)
df_dlb.head()


# 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:
        Your task is convert a question into a SQL query, given a sqlite3 database schema.
        Adhere to these rules:
        - **Deliberately go through the question and database schema word by word** to appropriately answer the question
        - **Use Table Aliases** to prevent ambiguity. For example, `SELECT table1.col1, table2.col1 FROM table1 JOIN table2 ON table1.id = table2.id`.
        - When creating a ratio, always cast the numerator as int
        
        ### 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 sales (
        salestype TEXT, --Type of sales like Sellin, SellThru, Sellout
        salescategory TEXT,--Sales Category like Normal, Demo, Free of Cost etc
        channel TEXT,--Sales Channel like General Trade, Modern Trade, Online, Business to Business or NA
        priceband TEXT, --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 TEXT,--Sales Regions like Online, B2B and for GT the regions are East1, East2, West1, West2, West3, North1, North2, North3, North4, South1, South2, South3
        year INTEGER,--Year of sale
        month INTEGER,--Month of sale
        day INTEGER,--Day of sale
        date INTEGER,--Date of sale
        weekyear TEXT,--Week and Year of sale
        segment TEXT,--Segment of sale like Smatphone, Tablet, Note PC Laptop, Wearables
        model TEXT,--Product name or Market name or Model name
        quantity INTEGER,--Sales quantity
        amount INTEGER,--Sales amount in INR
        mtdquantity INTEGER,--Month till date Quantity
        mtdamount INTEGER,--Month till date Amount in INR
        lmtdquantity INTEGER,--Last Month till date quantity
        lmtdamount INTEGER,--Last Month till date amount in INR
      );

        CREATE TABLE IF NOT EXISTS sales_dlb (
                      year INTEGER, -- year of sellout/sale,
                     week TEXT,--week of sellout,
                     month INteger,--month of sellout
                     counterselloutvalue INTEGER,-- value of sellout/amount
                     counterselloutvolume INTEGER,-- volume of sellout
                     region TEXT,-- region of sale
                     competitor TEXT,--competitor/competition name
                     priceband TEXT,-- price bands of different model/modelcode sold
                     salestype TEXT,-- salestype of the sale
                     channel TEXT,-- channel through which it is sold
                     status TEXT, -- status of sale
                     modelname TEXT,-- market name
                     productgroup TEXT -- product group
        );

        ### 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','')
 
        cursor_local.execute(generated_sql)
        result = cursor_local.fetchall()
#         print(result)
        cursor_local.close()
        conn_local.close()
#         for row in result:
#             print(row)
        df = pd.DataFrame(result,columns=[desc[0] for desc in cursor_local.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)




database is now migrated to postgresql,with same name and everything,remove the previous sqlite code and implement code for postgresql,also this time,whatever user is chatting ,store user question,answer,sql query to a 'feedback' table in postgresql