Update app.py

app.py

@@ -1,56 +1,57 @@
-import gradio as gr
-from sentence_transformers import SentenceTransformer, util
-import pandas as pd
-import torch
-
-# Load the fine-tuned model from Hugging Face
-model = SentenceTransformer("adityasajja6/fine_tuned_mpnet_model")
-
-# Load the cleaned courses dataset
-courses_df = pd.read_csv('cleaned_analytics_vidhya_courses.csv')
-
-# Load the precomputed course embeddings from the .pt file
-course_embeddings = torch.load('course_embeddings.pt')
-
-# Define the search function
-def search_courses(query, top_k=5):
-    # Create embedding for the query
-    query_embedding = model.encode(query, convert_to_tensor=True)
-    
-    # Compute similarity scores
-    similarities = util.pytorch_cos_sim(query_embedding, course_embeddings)[0]
-    
-    # Find the top_k most similar courses
-    top_results = similarities.topk(k=top_k)
-    
-    # Extract the titles, links, and similarity scores of the top results
-    results = []
-    for idx in top_results.indices:
-        course_title = courses_df.iloc[idx]['Title']
-        course_link = courses_df.iloc[idx]['Link']
-        similarity_score = round(float(similarities[idx]), 4)
-        results.append((course_title, course_link, similarity_score))
-    
-    return results
-
-# Define the Gradio interface
-def gradio_search(query):
-    results = search_courses(query)
-    formatted_results = [
-        f"{title} (Score: {score}) - [Link]({link})"
-        for title, link, score in results
-    ]
-    return "\n\n".join(formatted_results)
-
-# Create a Gradio interface
-interface = gr.Interface(
-    fn=gradio_search,
-    inputs=gr.Textbox(label="Search for courses"),
-    outputs=gr.Markdown(label="Top Matching Courses"),
-    title="Smart Course Search Tool",
-    description="Enter a query to find the most relevant courses from Analytics Vidhya's free courses.",
-)
-
-# Launch the interface
-if __name__ == "__main__":
-    interface.launch()
+import gradio as gr
+from sentence_transformers import SentenceTransformer, util
+import pandas as pd
+import torch
+
+# Load the fine-tuned model from Hugging Face
+model = SentenceTransformer("adityasajja6/fine_tuned_mpnet_model")
+
+# Load the cleaned courses dataset
+courses_df = pd.read_csv('cleaned_analytics_vidhya_courses.csv')
+
+# Load the precomputed course embeddings from the .pt file
+course_embeddings = torch.load('course_embeddings.pt')
+
+# Define the search function
+def search_courses(query, top_k=5):
+    # Create embedding for the query
+    query_embedding = model.encode(query, convert_to_tensor=True)
+    
+    # Compute similarity scores
+    similarities = util.pytorch_cos_sim(query_embedding, course_embeddings)[0]
+    
+    # Find the top_k most similar courses
+    top_results = similarities.topk(k=top_k)
+    
+    # Extract the titles, links, and similarity scores of the top results
+    results = []
+    for idx in top_results.indices:
+        idx = int(idx)  # Convert tensor index to an integer
+        course_title = courses_df.iloc[idx]['Title']
+        course_link = courses_df.iloc[idx]['Link']
+        similarity_score = round(float(similarities[idx]), 4)
+        results.append((course_title, course_link, similarity_score))
+    
+    return results
+
+# Define the Gradio interface
+def gradio_search(query):
+    results = search_courses(query)
+    formatted_results = [
+        f"{title} (Score: {score}) - [Link]({link})"
+        for title, link, score in results
+    ]
+    return "\n\n".join(formatted_results)
+
+# Create a Gradio interface
+interface = gr.Interface(
+    fn=gradio_search,
+    inputs=gr.Textbox(label="Search for courses"),
+    outputs=gr.Markdown(label="Top Matching Courses"),
+    title="Smart Course Search Tool",
+    description="Enter a query to find the most relevant courses from Analytics Vidhya's free courses.",
+)
+
+# Launch the interface
+if __name__ == "__main__":
+    interface.launch()