Update app.py

app.py

@@ -1,11 +1,34 @@
+# app.py
 import gradio as gr
-from huggingface_hub import InferenceClient
+import torch
+from threading import Thread
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
 
-"""
-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")
+# Choose any chat model with a chat template; Zephyr works well:
+MODEL_NAME = "google/gemma-3-270m"
 
+# Load model + tokenizer
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_fast=True)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_NAME,
+    torch_dtype="auto",
+    device_map="auto",
+)
+
+def build_chat(system_message: str, history: list[tuple[str, str]], user_message: str):
+    """Convert Gradio history into a list of chat messages for apply_chat_template."""
+    messages = []
+    if system_message:
+        messages.append({"role": "system", "content": system_message})
+
+    for u, a in history:
+        if u:
+            messages.append({"role": "user", "content": u})
+        if a:
+            messages.append({"role": "assistant", "content": a})
+
+    messages.append({"role": "user", "content": user_message})
+    return messages
 
 def respond(
     message,
@@ -15,34 +38,48 @@ def respond(
     temperature,
     top_p,
 ):
-    messages = [{"role": "system", "content": system_message}]
+    # 1) Build chat messages and tokenize using the model's chat template
+    messages = build_chat(system_message, history, message)
+
+    inputs = tokenizer.apply_chat_template(
+        messages,
+        add_generation_prompt=True,
+        tokenize=True,
+        return_tensors="pt",
+    )
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+    inputs = inputs.to(model.device)
 
-    messages.append({"role": "user", "content": message})
+    # 2) Stream generation
+    streamer = TextIteratorStreamer(
+        tokenizer,
+        skip_prompt=True,
+        skip_special_tokens=True,
+    )
 
-    response = ""
+    gen_kwargs = dict(
+        input_ids=inputs,
+        max_new_tokens=int(max_tokens),
+        do_sample=True,
+        temperature=float(temperature),
+        top_p=float(top_p),
+        eos_token_id=tokenizer.eos_token_id,
+        pad_token_id=tokenizer.eos_token_id,
+        streamer=streamer,
+    )
 
-    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
+    # Run generate() in a background thread while we yield chunks
+    thread = Thread(target=model.generate, kwargs=gen_kwargs)
+    thread.start()
 
-        response += token
+    response = ""
+    for new_text in streamer:
+        response += new_text
         yield response
 
+    thread.join()
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
+# Gradio UI (same controls as your example)
 demo = gr.ChatInterface(
     respond,
     additional_inputs=[
@@ -50,15 +87,10 @@ demo = gr.ChatInterface(
         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)",
+            minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"
         ),
     ],
 )
 
-
 if __name__ == "__main__":
     demo.launch()