Update app.py

app.py

@@ -3,16 +3,19 @@ from sentence_transformers import SentenceTransformer, util
 import openai
 import os
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
 # Initialize paths and model identifiers for easy configuration and maintenance
 filename = "output_topic_details.txt"  # Path to the file storing song recommendation details
 retrieval_model_name = 'output/sentence-transformer-finetuned/'
 openai.api_key = os.environ["OPENAI_API_KEY"]
+
 # Attempt to load the necessary models and provide feedback on success or failure
 try:
     retrieval_model = SentenceTransformer(retrieval_model_name)
     print("Models loaded successfully.")
 except Exception as e:
     print(f"Failed to load models: {e}")
+
 def load_and_preprocess_text(filename):
     """
     Load and preprocess text from a file, removing empty lines and stripping whitespace.
@@ -25,11 +28,12 @@ def load_and_preprocess_text(filename):
     except Exception as e:
         print(f"Failed to load or preprocess text: {e}")
         return []
+
 segments = load_and_preprocess_text(filename)
-def find_relevant_segment(user_query, segments):
+
+def find_relevant_segments(user_query, segments, top_k=5):
     """
-    Find the most relevant text segment for a user's query using cosine similarity among sentence embeddings.
-    This version finds the best match based on the content of the query.
+    Find the top-k most relevant text segments for a user's query using cosine similarity among sentence embeddings.
     """
     try:
         # Lowercase the query for better matching
@@ -39,20 +43,21 @@ def find_relevant_segment(user_query, segments):
         segment_embeddings = retrieval_model.encode(segments)
         # Compute cosine similarities between the query and the segments
         similarities = util.pytorch_cos_sim(query_embedding, segment_embeddings)[0]
-        # Find the index of the most similar segment
-        best_idx = similarities.argmax()
-        # Return the most relevant segment
-        return segments[best_idx]
+        # Find the indices of the top-k most similar segments
+        top_k_indices = similarities.topk(top_k).indices
+        # Return the most relevant segments
+        return [segments[idx] for idx in top_k_indices]
     except Exception as e:
-        print(f"Error in finding relevant segment: {e}")
-        return ""
-def generate_response(user_query, relevant_segment):
+        print(f"Error in finding relevant segments: {e}")
+        return []
+
+def generate_response(user_query, relevant_segments):
     """
     Generate a response providing song recommendations based on mood.
     """
     try:
         system_message = "You are a music recommendation chatbot designed to suggest songs based on mood, catering to Gen Z's taste in music."
-        user_message = f"User query: {user_query}. Recommended songs: {relevant_segment}"
+        user_message = f"User query: {user_query}. Recommended songs: {', '.join(relevant_segments)}"
         messages = [
             {"role": "system", "content": system_message},
             {"role": "user", "content": user_message}
@@ -70,17 +75,19 @@ def generate_response(user_query, relevant_segment):
     except Exception as e:
         print(f"Error in generating response: {e}")
         return f"Error in generating response: {e}"
+
 def query_model(question):
     """
     Process a question, find relevant information, and generate a response.
     """
     if question == "":
         return "Welcome to the Song Recommendation Bot! Ask me for song recommendations based on your mood."
-    relevant_segment = find_relevant_segment(question, segments)
-    if not relevant_segment:
+    relevant_segments = find_relevant_segments(question, segments)
+    if not relevant_segments:
         return "Could not find specific song recommendations. Please refine your question."
-    response = generate_response(question, relevant_segment)
+    response = generate_response(question, relevant_segments)
     return response
+
 # Define the welcome message and specific topics the chatbot can provide information about
 welcome_message = """
 # 🎶: Welcome to the Song Recommendation Bot!
@@ -98,8 +105,9 @@ topics = """
 - Nostalgic
 - Self care
 """
+
 # Setup the Gradio Blocks interface with custom layout components
-with gr.Blocks() as demo:
+with gr.Blocks(css="custom.css") as demo:
     gr.Markdown(welcome_message)  # Display the formatted welcome message
     with gr.Row():
         with gr.Column():
@@ -111,4 +119,4 @@ with gr.Blocks() as demo:
             submit_button = gr.Button("Submit")
             submit_button.click(fn=query_model, inputs=question, outputs=answer)
 # Launch the Gradio app to allow user interaction
-demo.launch(share=True)
+demo.launch(share=True)