Update app.py

app.py

@@ -6,12 +6,12 @@ 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 chess-specific details
+filename = "output_topic_details.txt"  # Path to the file storing song-specific details
 retrieval_model_name = 'output/sentence-transformer-finetuned/'
 
 openai.api_key = os.environ["OPENAI_API_KEY"]
 
-system_message = "You are a song chatbot specialized in providing song reccomendations based on mood."
+system_message = "You are a song chatbot specialized in providing song recommendations based on mood."
 # Initial system message to set the behavior of the assistant
 messages = [{"role": "system", "content": system_message}]
 
@@ -64,14 +64,15 @@ def find_relevant_segment(user_query, segments):
 
 def generate_response(user_query, relevant_segment):
     """
-    Generate a response emphasizing the bot's capability in providing chess information.
+    Generate a response emphasizing the bot's capability in providing song recommendations.
     """
     try:
-        user_message = f"Here's the information on chess: {relevant_segment}"
+        user_message = f"Here's the information on songs: {relevant_segment}"
 
         # Append user's message to messages list
         messages.append({"role": "user", "content": user_message})
         
+        # Use OpenAI's API to generate a response based on the user's query and system messages
         response = openai.ChatCompletion.create(
             model="gpt-3.5-turbo",
             messages=messages,
@@ -94,49 +95,72 @@ def generate_response(user_query, relevant_segment):
         print(f"Error in generating response: {e}")
         return f"Error in generating response: {e}"
 
-def query_model(question):
+def recommend_songs_based_on_mood(mood):
     """
-    Process a question, find relevant information, and generate a response.
+    Recommend songs based on the user's mood query.
     """
-    if question == "":
-        return "Welcome to ChessBot! Ask me anything about chess rules, strategies, and terminology."
-    relevant_segment = find_relevant_segment(question, segments)
-    if not relevant_segment:
-        return "Could not find specific information. Please refine your question."
-    response = generate_response(question, relevant_segment)
+    # Example logic to recommend songs based on mood (replace with your actual logic)
+    recommended_songs = [
+        "Song A",
+        "Song B",
+        "Song C",
+        "Song D",
+        "Song E"
+    ]
+    
+    # Format the recommendation list as a string
+    recommended_songs_str = "\n- " + "\n- ".join(recommended_songs)
+    
+    return f"Here are some songs you might like based on '{mood}' mood:{recommended_songs_str}"
+
+def query_model(user_query):
+    """
+    Process a user's query, find relevant information, and generate a response.
+    """
+    if user_query == "":
+        return "Welcome to SongBot! Ask me for song recommendations based on mood."
+    
+    # Example logic to identify if the user query is related to song recommendations based on mood
+    if "recommend" in user_query.lower() and ("song" in user_query.lower() or "music" in user_query.lower()):
+        mood = user_query.lower().split("recommend", 1)[1].strip()  # Extract mood from query
+        response = recommend_songs_based_on_mood(mood)
+    else:
+        relevant_segment = find_relevant_segment(user_query, segments)
+        if not relevant_segment:
+            response = "Could not find specific information. Please refine your question."
+        else:
+            response = generate_response(user_query, relevant_segment)
+    
     return response
 
 # Define the welcome message and specific topics the chatbot can provide information about
 welcome_message = """
-# ♟️ Welcome to Song Seeker!
-
-## Your AI-driven assistant for music curation. Created by Fenet, Lia, and Zamira of the 2024 Kode With Klossy DC Camp. 
+# 🎵 Welcome to Song Seeker!
+## Your AI-driven assistant for music curation. Created by Fenet, Lia, and Zamira of the 2024 Kode With Klossy DC Camp.
 """
 
 topics = """
-### Feel Free to ask me anything from the topics below!
+### Feel free to ask me for song recommendations based on mood!
+- Happy songs
 - Sad songs
-- Happy songs 
-- Game phases
-- Common strategies
-- Chess terminology
-- Famous games
-- Chess tactics
+- Chill songs
+- Angry songs
+- Workout songs
 """
 
 # Setup the Gradio Blocks interface with custom layout components
-with gr.Blocks(theme='JohnSmith9982/small_and_pretty') as demo:
+with gr.Blocks(theme='dabble') as demo:
     gr.Markdown(welcome_message)  # Display the formatted welcome message
     with gr.Row():
         with gr.Column():
             gr.Markdown(topics)  # Show the topics on the left side
     with gr.Row():
         with gr.Column():
-            question = gr.Textbox(label="Your question", placeholder="What do you want to ask about?")
-            answer = gr.Textbox(label="ChessBot Response", placeholder="ChessBot will respond here...", interactive=False, lines=10)
+            question = gr.Textbox(label="Your mood (e.g., happy, sad)", placeholder="What mood are you in?")
+            answer = gr.Textbox(label="SongBot Response", placeholder="SongBot will respond here...", interactive=False, lines=10)
             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)
+