from langchain_google_genai import ChatGoogleGenerativeAI
import pandas as pd
import os
import io
from flask import Flask, request, jsonify
from flask_cors import CORS, cross_origin
import logging
from dotenv import load_dotenv
from pandasai import SmartDatalake
from pandasai import SmartDataframe
from pandasai.responses.response_parser import ResponseParser
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from datetime import datetime
import matplotlib.pyplot as plt
import google.generativeai as genai


load_dotenv()

app = Flask(__name__)
cors = CORS(app)



class FlaskResponse(ResponseParser):
    def __init__(self, context) -> None:
        super().__init__(context)

    def format_dataframe(self, result):
        return result['value'].to_html()

    def format_plot(self, result):
        try:
            img_path = result['value']
        except ValueError:
            img_path = str(result['value'])
            print("ValueError:", img_path)
        print("response_class_path:", img_path)
        return img_path

    def format_other(self, result):
        return str(result['value'])

gemini_api_key = os.getenv('Gemini')
llm = ChatGoogleGenerativeAI(api_key=gemini_api_key, model='gemini-1.5-flash', temperature=0.1)

gemini_api_key = os.environ['Gemini']

genai.configure(api_key=gemini_api_key)

generation_config = {
  "temperature": 0.2,
  "top_p": 0.95,
  "max_output_tokens": 5000,
}

model = genai.GenerativeModel(
  model_name="gemini-2.0-flash-thinking-exp",
  generation_config=generation_config,
)
# Endpoint for chat
@app.route("/chat", methods=["POST"])
@cross_origin()
def bot():

    json_table = request.json.get("json_table")
    user_question = request.json.get("user_question")
    #data = request.get_json(force=True)TRye
    #print(req_body)
    #data = eval(req_body)
    #json_table = data["json_table"]
    #user_question = data["user_question"]
    #print(json_table)
    print(user_question)
    data = eval(str(json_table))
    df = pd.DataFrame(data)
    print(list(df))
    pandas_agent = SmartDataframe(df,config={"llm":llm, "response_parser":FlaskResponse})
    answer = pandas_agent.chat(user_question)
    return jsonify(answer)
    return answer
    #df = df.rename(co

# Reports endpoint
@app.route("/report", methods=["POST"])
@cross_origin()
def marketing_rec():
    json_data = request.json.get("json_data")

    prompt = """
You are an expert business analyst. Analyze the following data and generate a comprehensive and insightful business report, including appropriate key perfomance indicators and recommendations.
data:
"""  +  str(json_data)

    
    response = model.generate_content(prompt)
    report = response.text


    return jsonify(str(report))



if __name__ == "__main__":
    app.run(debug=True, host="0.0.0.0", port=7860)