Full code to be run

app.py

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+#Library install
+!pip install transformers sentence-transformers gradio
+import pandas as pd
+from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
+import gradio as gr
+
+# Load the dataset
+df = pd.read_csv('/content/courses.csv')  # Replace with actual path to courses.csv
+# Load a pre-trained sentence transformer model
+model = SentenceTransformer('all-MiniLM-L6-v2')
+
+# Create a combined column for embedding (e.g., title + description + keywords)
+df['combined_text'] = df['title'] + " " + df['description'] + " " + df['keywords']
+course_embeddings = model.encode(df['combined_text'].tolist(), convert_to_tensor=True)
+def search_courses(user_query):
+    # Encode the user query
+    query_embedding = model.encode(user_query, convert_to_tensor=True)
+
+    # Compute cosine similarities between the query and each course embedding
+    similarities = cosine_similarity(
+        query_embedding.cpu().detach().numpy().reshape(1, -1),
+        course_embeddings.cpu().detach().numpy()
+    )
+
+    # Get indices of top matching courses (top 5 results)
+    top_matches = similarities.argsort()[0][-5:][::-1]
+
+    # Retrieve top matching courses
+    results = [{"title": df.iloc[i]["title"], "description": df.iloc[i]["description"]} for i in top_matches]
+    return results
+
+# Define Gradio function for user interaction
+def gradio_search(query):
+    results = search_courses(query)
+    return results
+
+# Set up Gradio interface
+iface = gr.Interface(
+    fn=gradio_search,
+    inputs="text",
+    outputs="json",
+    title="Smart Course Search",
+    description="Find the most relevant courses based on your query."
+)
+
+# Launch the app (for local testing or deploying in Hugging Face Spaces)
+iface.launch()