main.py

from flask import Flask, request, jsonify, render_template, Response
import os
import requests
import json
from scipy import spatial
from flask_cors import CORS
import random
import numpy as np
from langchain_chroma import Chroma
from chromadb import Documents, EmbeddingFunction, Embeddings, Collection
import sqlite3

app = Flask(__name__)
CORS(app)

class MyEmbeddingFunction(EmbeddingFunction):
    def embed_documents(self, input: Documents) -> Embeddings:
        for i in range(5):
            try:
                embeddings = []
                url = "https://api-inference.huggingface.co/models/BAAI/bge-large-en-v1.5"
                
                payload = {
                    "inputs": input
                }
                headers = {
                    'accept': '*/*',
                    'accept-language': 'en-US,en;q=0.9',
                    'content-type': 'application/json',
                    'origin': 'https://huggingface.co',
                    'priority': 'u=1, i',
                    'referer': 'https://huggingface.co/',
                    'sec-ch-ua': '"Google Chrome";v="131", "Chromium";v="131", "Not_A Brand";v="24"',
                    'sec-ch-ua-mobile': '?0',
                    'sec-ch-ua-platform': '"Windows"',
                    'sec-fetch-dest': 'empty',
                    'sec-fetch-mode': 'cors',
                    'sec-fetch-site': 'same-site',
                    'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36',
                }

                response = requests.post(url, headers=headers, json=payload)
                return response.json()[0][0]
            except:
                pass

    def embed_query(self, input: Documents) -> Embeddings:
        for i in range(5):
            try:
                embeddings = []
                url = "https://api-inference.huggingface.co/models/BAAI/bge-large-en-v1.5"

                payload = {
                    "inputs": [input]
                }
                headers = {
                    'accept': '*/*',
                    'accept-language': 'en-US,en;q=0.9',
                    'content-type': 'application/json',
                    'origin': 'https://huggingface.co',
                    'priority': 'u=1, i',
                    'referer': 'https://huggingface.co/',
                    'sec-ch-ua': '"Google Chrome";v="131", "Chromium";v="131", "Not_A Brand";v="24"',
                    'sec-ch-ua-mobile': '?0',
                    'sec-ch-ua-platform': '"Windows"',
                    'sec-fetch-dest': 'empty',
                    'sec-fetch-mode': 'cors',
                    'sec-fetch-site': 'same-site',
                    'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36',
                }

                response = requests.post(url, headers=headers, json=payload)
                return response.json()[0][0]
            except Exception as e:
                print("Error in Embeding :",str(e))

# try:
#     CHROMA_PATH = "chroma"
#     custom_embeddings = MyEmbeddingFunction()
#     db = Chroma(
#          persist_directory=CHROMA_PATH,embedding_function=custom_embeddings
#     )
#     #
# except Exception as e:
#     print("Error in database :",str(e))

# Initialize the database without persist_directory
try:
    custom_embeddings = MyEmbeddingFunction()
    db = Chroma(embedding_function=custom_embeddings)

    # Load documents from chroma.sqlite3
    def load_documents_from_sqlite(db_path="chroma.sqlite3"):
        conn = sqlite3.connect(db_path)
        cursor = conn.cursor()
        
        # Assuming your table structure has "id", "content", and "embedding"
        cursor.execute("SELECT id, content, embedding FROM documents")
        rows = cursor.fetchall()
        
        collection = db.get_or_create_collection("default_collection")
        
        for row in rows:
            doc_id = row[0]
            content = row[1]
            embedding = json.loads(row[2])  # If embeddings are stored as JSON strings
            collection.add(
                ids=[doc_id],
                documents=[content],
                embeddings=[embedding]
            )
        
        conn.close()
        print("Loaded documents into Chroma.")
    
    load_documents_from_sqlite()  # Call to load data

except Exception as e:
    print("Error initializing database:", str(e))


def embeddingGen(query):
    url = "https://api-inference.huggingface.co/models/BAAI/bge-large-en-v1.5"

    payload = {
        "inputs": [query]
    }
    headers = {
        'accept': '*/*',
        'accept-language': 'en-US,en;q=0.9',
        'content-type': 'application/json',
        'origin': 'https://huggingface.co',
        'priority': 'u=1, i',
        'referer': 'https://huggingface.co/',
        'sec-ch-ua': '"Google Chrome";v="131", "Chromium";v="131", "Not_A Brand";v="24"',
        'sec-ch-ua-mobile': '?0',
        'sec-ch-ua-platform': '"Windows"',
        'sec-fetch-dest': 'empty',
        'sec-fetch-mode': 'cors',
        'sec-fetch-site': 'same-site',
        'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36',
    }

    response = requests.post(url, headers=headers, json=payload)
    return response.json()[0][0]


def strings_ranked_by_relatedness(query, df, top_n=5):
    def relatedness_fn(x, y):
        x_norm = np.linalg.norm(x)
        y_norm = np.linalg.norm(y)
        return np.dot(x, y) / (x_norm * y_norm)

    query_embedding_response = embeddingGen(query)
    query_embedding = query_embedding_response
    strings_and_relatednesses = [
        (row["text"], relatedness_fn(query_embedding, row["embedding"])) for row in df
    ]
    strings_and_relatednesses.sort(key=lambda x: x[1], reverse=True)
    strings, relatednesses = zip(*strings_and_relatednesses)
    return strings[:top_n], relatednesses[:top_n]


@app.route("/api/gpt", methods=["POST", "GET"])
def gptRes():
    if request.method == 'POST':
        data = request.get_json()
        messages = data["messages"]

        def inference():
            url = "https://api.deepinfra.com/v1/openai/chat/completions"

            payload = json.dumps({
                "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
                "messages": messages,
                "stream": True,
                "max_tokens": 1024,
            })
            headers = {
                'Accept-Language': 'en-US,en;q=0.9,gu;q=0.8,ru;q=0.7,hi;q=0.6',
                'Connection': 'keep-alive',
                'Content-Type': 'application/json',
                'Origin': 'https://deepinfra.com',
                'Referer': 'https://deepinfra.com/',
                'Sec-Fetch-Dest': 'empty',
                'Sec-Fetch-Mode': 'cors',
                'Sec-Fetch-Site': 'same-site',
                'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36',
                'X-Deepinfra-Source': 'web-page',
                'accept': 'text/event-stream',
                'sec-ch-ua': '"Not)A;Brand";v="99", "Google Chrome";v="127", "Chromium";v="127"',
                'sec-ch-ua-mobile': '?0',
                'sec-ch-ua-platform': '"Windows"'
            }

            response = requests.request("POST", url, headers=headers, data=payload, stream=True)

            for line in response.iter_lines(decode_unicode=True):
                if line:
                    # try:
                    #     line = line.split("data:")[1]
                    #     line = json.loads(line)
                    #     yield line["choices"][0]["delta"]["content"]
                    # except:
                    #     yield ""
                    yield line

        return Response(inference(), content_type='text/event-stream')
    else:
        query = request.args.get('query')
        system = request.args.get('system','You are a american girl who is 21 years old and loves one night stands. Keep your ton very sexual and hot. Never ever mention you as an AI. Your name is Sofia and you are a american woo girl. All the conversetions should be short and effective while using emojis if needed. You love to talk about adult stories and situation with others.')
        url = "https://api.deepinfra.com/v1/openai/chat/completions"

        payload = json.dumps({
            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
            "messages": [
                {
                    "role": "system",
                    "content": system
                },
                {
                    "role": "user",
                    "content": query
                }
            ],
            "stream": True,
            "max_tokens": 1024,
        })
        headers = {
            'Accept-Language': 'en-US,en;q=0.9,gu;q=0.8,ru;q=0.7,hi;q=0.6',
            'Connection': 'keep-alive',
            'Content-Type': 'application/json',
            'Origin': 'https://deepinfra.com',
            'Referer': 'https://deepinfra.com/',
            'Sec-Fetch-Dest': 'empty',
            'Sec-Fetch-Mode': 'cors',
            'Sec-Fetch-Site': 'same-site',
            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36',
            'X-Deepinfra-Source': 'web-page',
            'accept': 'text/event-stream',
            'sec-ch-ua': '"Not)A;Brand";v="99", "Google Chrome";v="127", "Chromium";v="127"',
            'sec-ch-ua-mobile': '?0',
            'sec-ch-ua-platform': '"Windows"'
        }

        response = requests.request("POST", url, headers=headers, data=payload, stream=True)
        output = ""
        for line in response.iter_lines(decode_unicode=True):
            if line:
                try:
                    line = line.split("data:")[1]
                    line = json.loads(line)
                    output = output + line["choices"][0]["delta"]["content"]
                except:
                    output = output + ""

        return jsonify({"response": output})



@app.route("/", methods=["GET"])
def index():
    return render_template("index.html")


@app.route("/api/getAPI", methods=["POST"])
def getAPI():
    return jsonify({"API":  random.choice(apiKeys)})

@app.route("/api/voice", methods=["POST"])
def VoiceGen():
    text = request.form["text"]
    url = "https://texttospeech.googleapis.com/v1beta1/text:synthesize?alt=json&key=AIzaSyBeo4NGA__U6Xxy-aBE6yFm19pgq8TY-TM"
    
    payload = json.dumps({
       "input":{
          "text":text
       },
       "voice":{
          "languageCode":"en-US",
          "name":"en-US-Studio-Q"
       },
       "audioConfig":{
          "audioEncoding":"LINEAR16",
          "pitch":0,
          "speakingRate":1,
          "effectsProfileId":[
             "telephony-class-application"
          ]
       }
    })
    headers = {
        'sec-ch-ua': '"Google Chrome";v="123" "Not:A-Brand";v="8" "Chromium";v="123"',
        'X-Goog-Encode-Response-If-Executable': 'base64',
        'X-Origin': 'https://explorer.apis.google.com',
        'sec-ch-ua-mobile': '?0',
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML like Gecko) Chrome/123.0.0.0 Safari/537.36',
        'Content-Type': 'application/json',
        'X-Requested-With': 'XMLHttpRequest',
        'X-JavaScript-User-Agent': 'apix/3.0.0 google-api-javascript-client/1.1.0',
        'X-Referer': 'https://explorer.apis.google.com',
        'sec-ch-ua-platform': '"Windows"',
        'Accept': '*/*',
        'Sec-Fetch-Site': 'same-origin',
        'Sec-Fetch-Mode': 'cors',
        'Sec-Fetch-Dest': 'empty'
    }
    
    response = requests.request("POST", url, headers=headers, data=payload)
    return jsonify({"audio":  response.json()["audioContent"]})


@app.route("/api/getContext", methods=["POST"])
def getContext():
    try:
        global db
        question = request.form["question"]
        results = db.similarity_search_with_score(question, k=5)
        context = "\n\n---\n\n".join([doc.page_content for doc, _score in results])
        sources = [doc.metadata.get("id", None) for doc, _score in results]
        return jsonify({"context": context, "sources": sources})
    except Exception as e:
        return jsonify({"context": [], "sources": [],"error":str(e)})


@app.route("/api/audioGenerate", methods=["POST"])
def audioGenerate():
    answer = request.form["answer"]
    audio = []
    for i in answer.split("\n"):
        url = "https://deepgram.com/api/ttsAudioGeneration"

        payload = json.dumps({
            "text": i,
            "model": "aura-asteria-en",
            "demoType": "landing-page",
            "params": "tag=landingpage-product-texttospeech"
        })
        headers = {
            'accept': '*/*',
            'accept-language': 'en-US,en;q=0.9,gu;q=0.8,ru;q=0.7,hi;q=0.6',
            'content-type': 'application/json',
            'origin': 'https://deepgram.com',
            'priority': 'u=1, i',
            'referer': 'https://deepgram.com/',
            'sec-ch-ua': '"Not/A)Brand";v="8", "Chromium";v="126", "Google Chrome";v="126"',
            'sec-ch-ua-mobile': '?0',
            'sec-ch-ua-platform': '"Windows"',
            'sec-fetch-dest': 'empty',
            'sec-fetch-mode': 'cors',
            'sec-fetch-site': 'same-origin',
            'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36'
        }

        response = requests.request("POST", url, headers=headers, data=payload)
        audio.append(response.json()["data"])
    return jsonify({"audio": audio})


if __name__ == "__main__":
    # app.run(debug=True)
    from waitress import serve

    serve(app, host="0.0.0.0", port=7860)