Update app.py

app.py

@@ -1,64 +1,121 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 
-    messages.append({"role": "user", "content": message})
+# Model and tokenizer loading (with error handling)
+try:
+    model_name = "google/gemma-3-1b-it"  # Correct model name
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        torch_dtype=torch.bfloat16,  # Use bfloat16 for efficiency, if supported
+        device_map="auto",  # Automatically use GPU if available, otherwise CPU
+    )
+    # Create the pipeline
+    pipe = pipeline(
+        "text-generation",
+        model=model,
+        tokenizer=tokenizer,
+        torch_dtype=torch.bfloat16, # Make sure pipeline also uses correct dtype
+        device_map="auto", # and device mapping
+        model_kwargs={"attn_implementation": "flash_attention_2"}  # Enable Flash Attention 2 if supported by your hardware and transformers version
+    )
 
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
+except Exception as e:
+    error_message = f"Error loading model or tokenizer: {e}"
+    print(error_message)  # Log the error to the console
+    #  Provide a fallback, even if it's just displaying the error.
+    def error_response(message, history):
+        return f"Model loading failed.  Error: {error_message}"
+    
+    # Minimal Gradio interface to show the error
+    with gr.Blocks() as demo:
+         gr.ChatInterface(error_response)
     demo.launch()
+    exit() # Important: exit to prevent running the rest of the (broken) code
+
+
+# Chat template handling (important for correct prompting)
+def apply_chat_template(messages, chat_template=None):
+    """Applies the chat template to the message history.
+
+    Args:
+        messages: A list of dictionaries, where each dictionary has a "role"
+            ("user" or "assistant") and "content" key.
+        chat_template:  The chat template string (optional). If None,
+        try to get from tokenizer.
+
+    Returns:
+        A single string representing the formatted conversation.
+    """
+    if chat_template is None:
+        if hasattr(tokenizer, "chat_template") and tokenizer.chat_template:
+            chat_template = tokenizer.chat_template
+        else:
+            # Fallback to a simple template if no chat template is found.  This is
+            # *critical* to prevent the model from generating nonsensical output.
+            chat_template = "{% for message in messages %}" \
+                            "{{ '<start_of_turn>' + message['role'] + '\n' + message['content'] + '<end_of_turn>\n' }}" \
+                            "{% endfor %}" \
+                            "{% if add_generation_prompt %}{{ '<start_of_turn>model\n' }}{% endif %}"
+
+    return tokenizer.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True, chat_template=chat_template
+    )
+
+# Prediction function (modified for chat)
+def predict(message, history):
+    """Generates a response to the user's message.
+
+    Args:
+        message: The user's input message (string).
+        history: A list of (user_message, bot_response) tuples representing
+            the conversation history.
+
+    Returns:
+        The generated bot response (string).
+    """
+    # Build the conversation history in the required format.
+    messages = []
+    for user_msg, bot_response in history:
+        messages.append({"role": "user", "content": user_msg})
+        messages.append({"role": "model", "content": bot_response})
+    messages.append({"role": "user", "content": message})
+
+    # Apply the chat template.
+    prompt = apply_chat_template(messages)
+
+    # Generate the response using the pipeline (much cleaner).
+    try:
+        sequences = pipe(
+            prompt,
+            max_new_tokens=512,   # Limit response length
+            do_sample=True,       # Use sampling for more diverse responses
+            temperature=0.7,      # Control randomness (higher = more random)
+            top_k=50,             # Top-k sampling
+            top_p=0.95,            # Nucleus sampling
+            repetition_penalty=1.2, # Reduce repetition
+            pad_token_id=tokenizer.eos_token_id,  # Ensure padding is correct.
+
+        )
+        response = sequences[0]['generated_text'][len(prompt):].strip() # Extract *only* generated text
+        return response
+
+    except Exception as e:
+        return f"An error occurred during generation: {e}"
+
+
+# Gradio interface (using gr.ChatInterface for a chatbot UI)
+with gr.Blocks() as demo:
+    gr.ChatInterface(
+        predict,
+        chatbot=gr.Chatbot(height=500),  # Set a reasonable height
+        textbox=gr.Textbox(placeholder="Ask me anything!", container=False, scale=7),
+        title="Gemma-3-1b-it Chatbot",
+        description="Chat with the Gemma-3-1b-it model.",
+        retry_btn=None,   # Remove redundant buttons
+        undo_btn=None,
+        clear_btn=None,
+    )
+
+demo.launch(share=False) # Set share=True to create a publicly shareable link