Update app.py

app.py

@@ -1,118 +1,64 @@
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, logging
-from huggingface_hub import login
-import torch
-import os
 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("google/gemma-3-1b-it")
+
+
+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]})
 
-# --- 1. Authentication (Using User-Provided Token) ---
-
-def authenticate(token):
-    """Attempts to authenticate with the provided token."""
-    try:
-        login(token=token)
-        return True
-    except Exception as e:
-        print(f"Authentication failed: {e}")
-        return False
-
-# --- 2. Model and Tokenizer Setup ---
-
-def load_model_and_tokenizer(model_name="google/gemma-3-1b-it"):
-    """Loads the model and tokenizer."""
-    try:
-        logging.set_verbosity_error()
-        tokenizer = AutoTokenizer.from_pretrained(model_name)
-        model = AutoModelForCausalLM.from_pretrained(
-            model_name,
-            device_map="auto",
-            torch_dtype=torch.bfloat16,
-            attn_implementation="flash_attention_2"
-        )
-        return model, tokenizer
-    except Exception as e:
-        print(f"ERROR: Failed to load model/tokenizer: {e}")
-        raise  # Re-raise for Gradio
-
-# --- 3. Chat Template Function ---
-
-def apply_chat_template(messages, tokenizer):
-    """Applies the chat template."""
-    try:
-        if hasattr(tokenizer, "chat_template") and tokenizer.chat_template:
-            return tokenizer.apply_chat_template(
-                messages, tokenize=False, add_generation_prompt=True
-            )
-        else:
-            print("WARNING: Tokenizer lacks chat_template. Using fallback.")
-            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)
-    except Exception as e:
-        print(f"ERROR: Chat template application failed: {e}")
-        raise
-
-# --- 4. Text Generation Function ---
-
-def generate_response(messages, model, tokenizer, max_new_tokens=256, temperature=0.7, top_k=50, top_p=0.95, repetition_penalty=1.2):
-    """Generates a response."""
-    prompt = apply_chat_template(messages, tokenizer)
-    try:
-        pipeline_instance = pipeline(
-            "text-generation", model=model, tokenizer=tokenizer,
-            torch_dtype=torch.bfloat16, device_map="auto",
-            model_kwargs={"attn_implementation": "flash_attention_2"}
-        )
-        outputs = pipeline_instance(
-            prompt, max_new_tokens=max_new_tokens, do_sample=True,
-            temperature=temperature, top_k=top_k, top_p=top_p,
-            repetition_penalty=repetition_penalty, pad_token_id=tokenizer.eos_token_id
-        )
-        return outputs[0]["generated_text"][len(prompt):].strip()
-    except Exception as e:
-        print(f"ERROR: Response generation failed: {e}")
-        raise
-
-# --- 5. Gradio Interface ---
-model = None  # Initialize model and tokenizer as global variables
-tokenizer = None
-
-def chat(token, message, history):
-    global model, tokenizer  # Access the global model and tokenizer
-
-    if not authenticate(token):
-        return "Authentication failed. Please enter a valid Hugging Face token.", history
-
-    if model is None or tokenizer is None:
-        try:
-            model, tokenizer = load_model_and_tokenizer()
-        except Exception as e:
-            return f"Model loading error: {e}", history
-
-    if not history:
-        history = []
-    messages = [{"role": "user", "content": msg} for msg, _ in history]
-    messages.extend([{"role": "model", "content": resp} for _, resp in history if resp])
     messages.append({"role": "user", "content": message})
 
-    try:
-        response = generate_response(messages, model, tokenizer)
-        history.append((message, response))
-        return "", history
-    except Exception as e:
-        return f"Error during generation: {e}", history
-
-with gr.Blocks() as demo:
-    gr.Markdown("# Gemma Chatbot")
-    gr.Markdown("Enter your Hugging Face API token (read access required):")
-    token_input = gr.Textbox(label="Hugging Face Token", type="password") # Use type="password"
-    chatbot = gr.Chatbot(label="Chat", height=400)
-    msg_input = gr.Textbox(label="Message", placeholder="Ask me anything!")
-    clear_btn = gr.ClearButton([msg_input, chatbot])
-
-    msg_input.submit(chat, [token_input, msg_input, chatbot], [msg_input, chatbot])
-    clear_btn.click(lambda: (None, []), [], [msg_input, chatbot])
-
-
-demo.launch()
+    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__":
+    demo.launch()