Update app.py

app.py

@@ -8,7 +8,7 @@ print("Starting the application...")
 
 # Load the dataset from the same directory
 print("Loading dataset...")
-df = pd.read_csv('courses.csv')  # Assuming courses.csv is in the same directory as app.py
+df = pd.read_csv('courses.csv')  # courses.csv
 print(f"Dataset loaded. Number of rows: {df.shape[0]}")
 
 # Load a pre-trained sentence transformer model
@@ -16,7 +16,7 @@ print("Loading Sentence Transformer model...")
 model = SentenceTransformer('all-MiniLM-L6-v2')
 print("Model loaded successfully.")
 
-# Create a combined column for embedding (e.g., title + description + keywords)
+# Create a combined column for embedding
 print("Generating embeddings for courses...")
 df['combined_text'] = df['title'] + " " + df['description'] + " " + df['keywords']
 course_embeddings = model.encode(df['combined_text'].tolist(), convert_to_tensor=True)
@@ -28,7 +28,7 @@ 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
+    # Compute similarities between the query and each course embedding
     print("Calculating cosine similarities...")
     similarities = cosine_similarity(
         query_embedding.cpu().detach().numpy().reshape(1, -1),
@@ -43,7 +43,7 @@ def search_courses(user_query):
     print(f"Found {len(results)} results.")
     return results
 
-# Define Gradio function for user interaction
+# Gradio function for user interaction
 def gradio_search(query):
     results = search_courses(query)
     return results
@@ -58,7 +58,7 @@ iface = gr.Interface(
     description="Find the most relevant courses based on your query."
 )
 
-# Launch the app (for local testing or deploying in Hugging Face Spaces)
+# Launch the app
 print("Launching the app...")
 iface.launch()