import pandas as pd
import numpy as np
import torch
from textblob import TextBlob
import openai
import gradio as gr
from pinecone import Pinecone
from langdetect import detect

openai.api_key = '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'
pc = Pinecone(api_key="2c47d51e-211b-4611-8808-5510e07d1f94",environment = "us-east-1")
def get_embedding(text, model="text-embedding-ada-002"):
    return openai.embeddings.create(input=[text], model=model).data[0].embedding
def check_and_correct_spelling(query):
    blob = TextBlob(query)
    corrected_query = str(blob.correct())
    return corrected_query
def correct_and_complete_query(text):
    blob = TextBlob(text)
    corrected_text = str(blob.correct())

    # Use OpenAI to complete the query
    completion_prompt = f"Complete the following query in a way that is related to product search: '{corrected_text}'"
    response = openai.completions.create(
        model="gpt-3.5-turbo-instruct",
        prompt=completion_prompt,
        max_tokens=100,
        temperature=0.5
    )

    return response.choices[0].text.strip()
def translate_to_english(text):
    if detect(text) != 'en':
        translation_prompt = f"Translate the following text to English:\n\n'{text}'"
        response = openai.completions.create(
            model="gpt-3.5-turbo-instruct",
            prompt=translation_prompt,
            max_tokens=100,
            temperature=0.5
        )
        return response.choices[0].text.strip()
    return text
def is_query_relevant(query, relevant_keywords):
    for keyword in relevant_keywords:
        if keyword.lower() in query.lower():
            return True
    return False
def process_query(query):
    query = check_and_correct_spelling(query)

    query = correct_and_complete_query(query)

    query = translate_to_english(query)

    # Step 4: Check if the query is relevant
    # if not is_query_relevant(query):
    #     return "The query is not relevant. Please enter a different query."

    return query
def search_in_pinecone2(query):
    processed_query = process_query(query)
    embedding = get_embedding(query)
    search_results = index.query(vector=embedding, top_k=5, include_metadata=True)

    result_strings = []
    for result in search_results['matches']:
        product_name = result['metadata'].get('product_name', 'No name available')
        product_link = result['metadata'].get('product_url', 'No link available')
        score = result['score']
        result_string = f"Product: {product_name}\nLink: {product_link}\nScore: {score}\n"
        result_strings.append(result_string)

    return "\n".join(result_strings)
index = pc.Index('zepto')
interface = gr.Interface(
    fn=search_in_pinecone2,
    inputs=gr.Textbox(label="Enter your query"),
    outputs=gr.Textbox(label="Top 5 Similar Products"),
    title="Product Similarity Search",
    description="Enter a query to find the top 5 similar products based on your search."
)

# Launch the interface
interface.launch()