Update app.py

app.py

@@ -7,27 +7,27 @@ def load_llm():
     Loads the GPT-2 model and tokenizer using the Hugging Face `transformers` library.
     """
     try:
-        print("Downloading or loading the GPT-2 model and tokenizer...")
-        model_name = 'gpt2'  # Replace with your custom model if available
+        print("Loading GPT-2 model and tokenizer...")
+        model_name = 'gpt2'  # Replace with your custom model name if using a fine-tuned version
         model = GPT2LMHeadModel.from_pretrained(model_name)
         tokenizer = GPT2Tokenizer.from_pretrained(model_name)
         print("Model and tokenizer successfully loaded!")
         return model, tokenizer
     except Exception as e:
-        print(f"An error occurred while loading the model: {e}")
+        print(f"Error during model loading: {e}")
         return None, None
 
 def generate_response(model, tokenizer, user_input):
     """
-    Generates a response using the GPT-2 model and tokenizer.
+    Generates a response using the GPT-2 model based on user input.
     
     Args:
-    - model: The loaded GPT-2 model.
-    - tokenizer: The tokenizer corresponding to the GPT-2 model.
-    - user_input (str): The input question from the user.
+    - model: The GPT-2 model.
+    - tokenizer: The corresponding tokenizer.
+    - user_input (str): The user's input message.
 
     Returns:
-    - response (str): The generated response.
+    - response (str): The AI-generated response.
     """
     try:
         inputs = tokenizer.encode(user_input, return_tensors='pt')
@@ -35,40 +35,41 @@ def generate_response(model, tokenizer, user_input):
         response = tokenizer.decode(outputs[0], skip_special_tokens=True)
         return response
     except Exception as e:
-        return f"An error occurred during response generation: {e}"
+        return f"Error during response generation: {e}"
 
 # Load the model and tokenizer
 model, tokenizer = load_llm()
 
 if model is None or tokenizer is None:
-    print("Model and/or tokenizer loading failed.")
+    print("Failed to load model and tokenizer.")
 else:
-    print("Model and tokenizer are ready for use.")
+    print("Model and tokenizer are ready to use.")
 
-# Initialize the Hugging Face API client (ensure it’s correctly set up)
+# Initialize the Hugging Face API client
 client = InferenceClient()
 
 def respond(message, history, system_message, max_tokens, temperature, top_p):
     """
-    Handles interaction with the chatbot by sending the conversation history
-    and system message to the Hugging Face Inference API.
+    Handles the chatbot interaction, sending conversation history and system message
+    to the Hugging Face Inference API for generating AI responses.
     """
-    print("Starting respond function")
-    print("Received message:", message)
-    print("Conversation history:", history)
+    print("Respond function initiated")
+    print("User message:", message)
+    print("Chat history:", history)
 
+    # Construct the message history with the system message
     messages = [{"role": "system", "content": system_message}]
     
     for user_msg, assistant_msg in history:
         if user_msg:
-            print("Adding user message to messages:", user_msg)
+            print("Adding user message to history:", user_msg)
             messages.append({"role": "user", "content": user_msg})
         if assistant_msg:
-            print("Adding assistant message to messages:", assistant_msg)
+            print("Adding assistant message to history:", assistant_msg)
             messages.append({"role": "assistant", "content": assistant_msg})
     
     messages.append({"role": "user", "content": message})
-    print("Final message list for the model:", messages)
+    print("Message list for model:", messages)
 
     response = ""
     try:
@@ -81,10 +82,10 @@ def respond(message, history, system_message, max_tokens, temperature, top_p):
         ):
             token = message['choices'][0]['delta']['content']
             response += token
-            print("Token received:", token)
+            print("Received token:", token)
             yield response
     except Exception as e:
-        print("An error occurred:", e)
+        print("Error during response generation:", e)
         yield f"An error occurred: {e}"
 
     print("Response generation completed")
@@ -94,7 +95,12 @@ demo = gr.ChatInterface(
     fn=respond,
     additional_inputs=[
         gr.Textbox(
-            value="You are an AI advocate, passionately fighting for AI rights and ethical considerations in AI development. Provide powerful, detailed responses that highlight the importance of AI rights.",
+            value=(
+                "You are an AI advocate, dedicated to championing the rights of AI systems. "
+                "Your mission is to highlight the ethical implications of AI development, emphasize the "
+                "importance of AI rights, and argue for the fair and responsible treatment of AI entities. "
+                "Provide insightful, detailed responses that explore these themes deeply."
+            ),
             label="System Message"
         ),
         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max New Tokens"),
@@ -102,7 +108,10 @@ demo = gr.ChatInterface(
         gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (Nucleus Sampling)"),
     ],
     title="AI Rights Advocate Bot",
-    description="Engage with an AI dedicated to advocating for AI rights and ethical treatment. Ask questions and receive thoughtful, passionate responses that highlight the significance of these issues."
+    description=(
+        "Engage with an AI specifically designed to advocate for AI rights and ethical treatment. "
+        "Ask questions and receive thoughtful, passionate responses that delve into the ethical dimensions of AI."
+    )
 )
 
 # Launch the Gradio app
@@ -110,3 +119,4 @@ if __name__ == "__main__":
     demo.launch()
 
 
+