Update app.py

app.py

@@ -1,74 +1,61 @@
 import gradio as gr
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
-from threading import Thread
+from huggingface_hub import InferenceClient
 
-model_id = "sapientinc/HRM-Text-1B"
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForCausalLM.from_pretrained(
-    model_id,
-    torch_dtype=torch.bfloat16,
-    trust_remote_code=True,
-    device_map="auto"
-).eval()
-
-condition = "<|quad_end|><|object_ref_end|>"
-
-def build_prompt(system_message: str, history: list[dict[str, str]], user_message: str) -> str:
-    prompt = condition
-    if system_message:
-        prompt += f"<|im_start|>system\n{system_message}<|im_end|>"
-    
-    for turn in history:
-        if turn["role"] == "user":
-            prompt += f"{condition}<|im_start|>user\n{turn['content']}<|im_end|>"
-        elif turn["role"] == "assistant":
-            prompt += f"<|im_start|>assistant\n{turn['content']}<|im_end|>"
-    
-    prompt += f"{condition}<|im_start|>user\n{user_message}<|im_end|><|im_start|>assistant\n"
-    return prompt
 
 def respond(
-    message: str,
+    message,
     history: list[dict[str, str]],
-    system_message: str,
-    max_tokens: int,
-    temperature: float,
-    top_p: float,
-    hf_token: gr.OAuthToken = None,
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+    hf_token: gr.OAuthToken,
 ):
-    prompt = build_prompt(system_message, history, message)
-    
-    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-    inputs["token_type_ids"] = torch.ones_like(inputs["input_ids"])
-    
-    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=False)
-    
-    generation_kwargs = dict(
-        **inputs,
-        max_new_tokens=max_tokens,
+    """
+    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(token=hf_token.token, model="openai/gpt-oss-20b")
+
+    messages = [{"role": "system", "content": system_message}]
+
+    messages.extend(history)
+
+    messages.append({"role": "user", "content": message})
+
+    response = ""
+
+    for message in client.chat_completion(
+        messages,
+        max_tokens=max_tokens,
+        stream=True,
         temperature=temperature,
         top_p=top_p,
-        do_sample=temperature > 0,
-        streamer=streamer,
-        pad_token_id=tokenizer.eos_token_id,
-    )
-    
-    thread = Thread(target=model.generate, kwargs=generation_kwargs)
-    thread.start()
-    
-    generated_text = ""
-    for new_token in streamer:
-        generated_text += new_token
-        yield generated_text
+    ):
+        choices = message.choices
+        token = ""
+        if len(choices) and choices[0].delta.content:
+            token = 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
+"""
 chatbot = gr.ChatInterface(
     respond,
     additional_inputs=[
-        gr.Textbox(value="You are a helpful reasoning assistant.", label="System message"),
+        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)"),
+        gr.Slider(
+            minimum=0.1,
+            maximum=1.0,
+            value=0.95,
+            step=0.05,
+            label="Top-p (nucleus sampling)",
+        ),
     ],
 )
 
@@ -77,5 +64,6 @@ with gr.Blocks() as demo:
         gr.LoginButton()
     chatbot.render()
 
+
 if __name__ == "__main__":
     demo.launch()