app.py

import pandas as pd
import gradio as gr

# Load your CSV file
data = pd.read_csv('course.csv')

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

# Dummy retriever function for search (replace this with your actual search logic)
class Retriever:
    def __init__(self, documents):
        self.documents = documents

    def get_relevant_documents(self, query):
        # A simple search based on query matching the content (you can replace with more advanced logic)
        results = []
        for doc in self.documents:
            if query.lower() in doc['content'].lower():
                results.append(doc)
        return results

# Initialize the retriever with the documents
retriever = Retriever(docs)

# Define the search function for Gradio interface
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()

# Create the Gradio interface
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)