Create app.py

app.py

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+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)