from flask import Flask, request, jsonify
from mistral import Mistral7B
from gpt import ChatGpt
from news import News
from datetime import datetime
from os import listdir
from web import Online_Scraper
import requests
from RT import RealTimeGemini
from time import time as t
import os

app = Flask(__name__)

 

@app.route('/mistral7b', methods=['POST'])
def generate():
    # Get data from the request
    data = request.json
    prompt = data.get('prompt', '')
    messages = data.get('messages', [])
    key = data.get('key', '')
    
    # Call Mistral7B function
    response, updated_messages, execution_time = Mistral7B(prompt, messages,key)

    # Prepare the response
    result = {
        'response': response,
        'messages': updated_messages,
        'execution_time': execution_time
    }
    return jsonify(result)

@app.route('/chatgpt', methods=['POST'])
def chat():
    # Get data from the request
    data = request.json
    user_message = data.get('message', '')
    messages = data.get('messages', [])

    # Call ChatGpt function
    response, updated_messages, execution_time = ChatGpt(user_message, messages)

    # Prepare the response
    result = {
        'response': response,
        'messages': updated_messages,
        'execution_time': execution_time
    }
    return jsonify(result)

@app.route('/news', methods=['GET'])
def get_news():
    # Get data from the request
    key = request.args.get('key', '')
    cache_flag = request.args.get('cache', 'True').lower() == 'true'

    # Call News function
    news, error, execution_time = News(key, cache_flag)

    # Prepare the response
    result = {
        'news': news,
        'error': error,
        'execution_time': execution_time
    }
    return jsonify(result)

@app.route('/web', methods=['GET'])
def Web():
    key = request.args.get('prompt', '')
    result = {
        'response': Online_Scraper(key)
    }
    return jsonify(result)

@app.route('/imageneration', methods=['POST'])
def IMGEN():
    data = request.json
    prompt = data.get('prompt', '')
    key = data.get('key', '')

    
    API_URL = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-2-1"
    headers = {"Authorization": f"Bearer {key}"}


    return requests.post(API_URL, headers=headers, json={"inputs": prompt,}).content

@app.route('/generativeai', methods=['POST'])
def Genration():
    try:
        import google.generativeai as genai
        generation_config = {
            "temperature": 0.7,
            "top_p": 1,
            "top_k": 1,
            "max_output_tokens": 300,
        }
        
        safety_settings = [
            {
            "category": "HARM_CATEGORY_HARASSMENT",
            "threshold": "BLOCK_NONE"
            },
            {
            "category": "HARM_CATEGORY_HATE_SPEECH",
            "threshold": "BLOCK_NONE"
            },
            {
            "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
            "threshold": "BLOCK_NONE"
            },
            {
            "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
            "threshold": "BLOCK_NONE"
            },
        ]
        
        model = genai.GenerativeModel(
         model_name="gemini-pro",
         generation_config=generation_config,
         safety_settings=safety_settings)
    
        data = request.json
        messages = data.get('messages', [])
        key = data.get('key', '')
        
        C=t()
        genai.configure(api_key=key)
        response = model.generate_content(messages)
        
        
        # Prepare the response
        result = {
            'response': response.text,
            'execution_time': t()-C
        }
        return jsonify(result)
    except Exception as e:
        result = {
            'response': f"{e}",
            'execution_time': t()-C
        }
        return jsonify(result)


@app.route('/realtime', methods=['POST'])
def GenrationRT():
    try:
        
        import google.generativeai as genai
        generation_config = {
            "temperature": 0.9,
            "top_p": 1,
            "top_k": 1,
            "max_output_tokens": 2048,
        }
        
        safety_settings = [
            {
            "category": "HARM_CATEGORY_HARASSMENT",
            "threshold": "BLOCK_NONE"
            },
            {
            "category": "HARM_CATEGORY_HATE_SPEECH",
            "threshold": "BLOCK_NONE"
            },
            {
            "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
            "threshold": "BLOCK_NONE"
            },
            {
            "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
            "threshold": "BLOCK_NONE"
            },
        ]
        
        model = genai.GenerativeModel(
         model_name="gemini-pro",
         generation_config=generation_config,
         safety_settings=safety_settings)
    
        data = request.json
        query = data.get('prompt', "hello ?")
        messages = data.get('messages', [])
        key = data.get('key', '')
        
        C=t()
        genai.configure(api_key=key)
        response = RealTimeGemini(query,messages,model)
        
        
        # Prepare the response
        result = {
            'response': response,
            'execution_time': t()-C
        }
        return jsonify(result)
    except Exception as e:
        result = {
            'response': f"{e}",
            'execution_time': t()-C
        }
        return jsonify(result)


@app.route('/divyanshpizza', methods=['GET'])
def get_counters():
    return jsonify(counter),jsonify({"data":str(listdir(r"static/data/"))})


if __name__ == '__main__':
    app.run()