Update app.py

app.py

@@ -6,18 +6,20 @@ import os
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
 # Initialize paths and model identifiers for easy configuration and maintenance
-filename = "output_topic_details.txt"
+filename = "output_topic_details.txt"  # Path to the file storing destress-specific details
 retrieval_model_name = 'output/sentence-transformer-finetuned/'
 
 # openai.api_key = os.environ["OPENAI_API_KEY"]
 
 system_message = "You are a comfort chatbot specialized in providing information on destressing activities."
+# Initial system message to set the behavior of the assistant
 messages = [{"role": "system", "content": system_message}]
 messages.append({
-    "role": "system",
-    "content": "Do not use Markdown Format. Do not include hashtags or asterisks"
+	"role": "system",
+	"content": "Do not use Markdown Format. Do not include hashtags or asterisks"
 })
 
+# Attempt to load the necessary models and provide feedback on success or failure
 try:
     retrieval_model = SentenceTransformer(retrieval_model_name)
     print("Models loaded successfully.")
@@ -25,6 +27,9 @@ 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.
+    """
     try:
         with open(filename, 'r', encoding='utf-8') as file:
             segments = [line.strip() for line in file if line.strip()]
@@ -37,20 +42,38 @@ def load_and_preprocess_text(filename):
 segments = load_and_preprocess_text(filename)
 
 def find_relevant_segment(user_query, segments):
+    """
+    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.
+    """
     try:
+        # Lowercase the query for better matching
         lower_query = user_query.lower()
+        
+        # Encode the query and the segments
         query_embedding = retrieval_model.encode(lower_query)
         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]
     except Exception as e:
         print(f"Error in finding relevant segment: {e}")
         return ""
 
 def generate_response(user_query, relevant_segment):
+    """
+    Generate a response emphasizing the bot's capability in providing therapy, destressing activites, and student opportunities information.
+    """
     try:
         user_message = f"Here's the information on your request: {relevant_segment}"
+
+        # Append user's message to messages list
         messages.append({"role": "user", "content": user_message})
         
         response = openai.ChatCompletion.create(
@@ -63,8 +86,12 @@ def generate_response(user_query, relevant_segment):
             presence_penalty=0.5,
         )
         
+        # Extract the response text
         output_text = response['choices'][0]['message']['content'].strip()
+        
+        # Append assistant's message to messages list for context
         messages.append({"role": "assistant", "content": output_text})
+        
         return output_text
         
     except Exception as e:
@@ -72,6 +99,9 @@ def generate_response(user_query, relevant_segment):
         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 CalmConnect's CalmBot! Ask me anything about destressing strategies and we'll provide you ways to unlock your inner calm!"
     relevant_segment = find_relevant_segment(question, segments)
@@ -83,16 +113,6 @@ def query_model(question):
 welcome_message = ""
 topics = ""
 
-# Custom CSS to center content
-custom_css = """
-#centered-container {
-    display: flex;
-    justify-content: center;
-    align-items: center;
-    height: 100vh; /* Full viewport height */
-}
-"""
-
 theme = gr.themes.Default(
     primary_hue="neutral",
     secondary_hue="neutral",
@@ -109,24 +129,26 @@ theme = gr.themes.Default(
     button_primary_background_fill="#f8f1ea",
     button_primary_background_fill_dark="#f8f1ea"
 )
-
+    
 # Setup the Gradio Blocks interface with custom layout components
-with gr.Blocks(css=custom_css, theme=theme) as demo:
+with gr.Blocks(theme=theme) as demo:
+
+    gr.Markdown(welcome_message)  # Display the formatted welcome message
     
-    # Centering container
-    with gr.Container(elem_id="centered-container"):
-        gr.Markdown(welcome_message)
+    with gr.Row():
+        with gr.Column(scale=0.8):
+            gr.Markdown(topics)
+          
+        
+           # Show the topics on the left side
         with gr.Row():
-            with gr.Column(scale=0.8):
-                gr.Markdown(topics)
-            
-            with gr.Row():
-                with gr.Column():
-                    question = gr.Textbox(label="You", placeholder="What do you want to talk to CalmBot about?")
-                    answer = gr.Textbox(label="CalmBot's Response :D", placeholder="CalmBot will respond here..", interactive=False, lines=20)
-                    submit_button = gr.Button("Submit")
-                    submit_button.click(fn=query_model, inputs=question, outputs=answer)
+            with gr.Column():
+                question = gr.Textbox(label="You", placeholder="What do you want to talk to CalmBot about?")
+                answer = gr.Textbox(label="CalmBot's Response :D", placeholder="CalmBot will respond here..", interactive=False, lines=20)
+                submit_button = gr.Button("Submit")
+                submit_button.click(fn=query_model, inputs=question, outputs=answer)
+
+demo.launch()
 
 # Launch the Gradio app to allow user interaction
 demo.launch(share=True)
-