main.py

import google.generativeai as palm
import pandas as pd
import os
import io
from flask import Flask, request, jsonify
from flask_cors import CORS, cross_origin
import pandas as pd
import firebase_admin
from firebase_admin import credentials, firestore, auth
import requests
import pandas as pd
from datetime import datetime
import os
from pandasai.llm import GoogleGemini
from pandasai import SmartDataframe, SmartDatalake

from pandasai.responses.response_parser import  ResponseParser
import matplotlib.pyplot as plt
from wordcloud import WordCloud
import random

from dotenv import load_dotenv
import json


from dotenv import load_dotenv

load_dotenv()


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

class FlaskResponse(ResponseParser):
        def __init__(self,context) -> None:
              super().__init__(context)
        def format_dataframe(self,result):
               st.dataframe(result['value'])
               return
        def format_plot(self,result):
               st.image(result['value'])
               return
        def format_other(self, result):
               st.write(result['value'])
               return

gemini_api_key = os.environ['Gemini']

@app.route("/", methods=["GET"])
def home():
   
    return "Hello Qx!"



llm = GoogleGemini(api_key=gemini_api_key)
        
# Initialize Firebase app
if not firebase_admin._apps:
   
    cred = credentials.Certificate("quant-app-99d09-firebase-adminsdk-6prb1-37f34e1c91.json")
    firebase_admin.initialize_app(cred)

db = firestore.client()




@app.route("/predict", methods=["POST"])
@cross_origin()
def bot():


    user_id = request.json.get("user_id")
    user_question = request.json.get("user_question")
    load_dotenv()
    inventory_ref = db.collection("users").document(user_id).collection('inventory')


    sales_ref = db.collection("users").document(user_id).collection('sales')

    inventory_list = []
    for doc in inventory_ref.stream():
        a = doc.to_dict()
        inventory_list.append(a)

    sales_list = []
    for doc in sales_ref.stream():
        a = doc.to_dict()
        sales_list.append(a)

    inventory_df = pd.DataFrame(inventory_list)
    sales_df = pd.DataFrame(sales_list)

    lake = SmartDatalake([inventory_df, sales_df], config={"llm":llm, "response_parser":FlaskResponse, "enable_cache": False})
    response = lake.chat(user_question)

    print(user_question)
    
   
    return jsonify(response)


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