# -*- coding: utf-8 -*-
"""app

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1MuuKKNek3EmP5A5aGk8ail0MmT7w10f9
"""

!pip install -U langchain-community

!pip install langchain==0.3.0 llama-index==0.12.0 sentence-transformers faiss-cpu gradio

import pandas as pd

# Load your uploaded file
data = pd.read_csv('/content/course.csv')

# Combine TITLE, DESCRIPTION, and CURRICULUM for processing
docs = []
for _, row in data.iterrows():
    content = f"{row['TITLE']}\n{row['DESCRIPTION']}\n{row['CURRICULUM']}"
    docs.append({"content": content, "metadata": {"title": row['TITLE'], "url": row['URL']}})

from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS

# Create embeddings
embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
texts = [doc["content"] for doc in docs]
metadatas = [doc["metadata"] for doc in docs]

# Create FAISS Index
vectorstore = FAISS.from_texts(texts, embedding_model, metadatas=metadatas)

import os

# Replace 'YOUR_API_KEY' with your actual OpenAI API key
os.environ["OPENAI_API_KEY"] = "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"

from langchain.chains import RetrievalQA
from langchain.llms import OpenAI

retriever = vectorstore.as_retriever()
qa_chain = RetrievalQA.from_chain_type(llm=OpenAI(model_name="text-davinci-003"), retriever=retriever)

import gradio as gr

def smart_search(query):
    results = retriever.get_relevant_documents(query)
    response = ""
    for result in results:
        title = result.metadata.get("title", "No Title")
        url = result.metadata.get("url", "No URL")
        response += f"**{title}**\n[Link to Course]({url})\n\n"
    return response.strip()

interface = gr.Interface(
    fn=smart_search,
    inputs="text",
    outputs="markdown",
    title="Smart Search for Analytics Vidhya Free Courses",
    description="Enter a keyword or a query to find relevant free courses on Analytics Vidhya."
)

# Launch the Gradio app
interface.launch(share=True)