Update app.py

app.py

@@ -1,64 +1,54 @@
 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(model="kz919/QwQ-0.5B-Distilled-SFT")
-
-
-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]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load the model and tokenizer locally
+model_name = "kz919/QwQ-0.5B-Distilled-SFT"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Ensure the model runs on GPU if available
+import torch
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+
+# Define the function to handle chat responses
+def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p):
+    # Prepare the prompt by combining history and system messages
+    prompt = system_message + "\n"
+    for user_input, assistant_response in history:
+        prompt += f"User: {user_input}\nAssistant: {assistant_response}\n"
+    prompt += f"User: {message}\nAssistant:"
+
+    # Tokenize the input prompt
+    inputs = tokenizer(prompt, return_tensors="pt").to(device)
+
+    # Generate a response
+    outputs = model.generate(
+        inputs.input_ids,
+        max_length=max_tokens,
         temperature=temperature,
         top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+        pad_token_id=tokenizer.eos_token_id,
+    )
 
-        response += token
-        yield response
+    # Decode the generated tokens and yield the response
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    yield response.split("Assistant:")[-1].strip()
 
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
+# Create the Gradio interface
 demo = gr.ChatInterface(
     respond,
     additional_inputs=[
         gr.Textbox(value="You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step.", 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)"),
     ],
 )
 
-
+# Launch the Gradio app
 if __name__ == "__main__":
     demo.launch()
+