Update app.py

app.py

@@ -1,64 +1,76 @@
+import huggingface_hub as hf_hub
+import time
+import openvino_genai as ov_genai
+import numpy as np
 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]})
-
-    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,
-    ):
-        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)",
-        ),
-    ],
-)
+
+# 下載模型
+model_id = "OpenVINO/Qwen3-0.6B-int4-ov"
+model_path = "Qwen3-0.6B-int4-ov"
+
+hf_hub.snapshot_download(model_id, local_dir=model_path, local_dir_use_symlinks=False) # Added local_dir_use_symlinks=False to avoid potential issues
+
+# 建立推理管線
+device = "CPU"
+pipe = ov_genai.LLMPipeline(model_path, device)
+tokenizer = pipe.get_tokenizer()
+tokenizer.set_chat_template(tokenizer.chat_template)
+
+
+def generate_response(prompt):
+    """
+    Generates a response using the OpenVINO LLM pipeline.
+
+    Args:
+        prompt (str): The input prompt.
+
+    Returns:
+        str: The generated response.
+    """
+    start_time = time.time()
+    output = pipe.generate([prompt], max_length=1024)
+    end_time = time.time()
+
+    generated_text = output.text[0]  # Extract the generated text
+
+    performance_metrics = f"Generate duration: {output.perf_metrics.get_generate_duration().mean:.2f}ms\n"
+    performance_metrics += f'Throughput: {output.perf_metrics.get_throughput().mean:.2f} tokens/s'
+
+    return generated_text, performance_metrics
+
+
+def main():
+    """
+    Creates and launches the Gradio interface.
+    """
+
+    with gr.Blocks() as demo:
+        gr.Markdown("# OpenVINO Qwen3-8B Demo")  # Add a title
+        prompt_input = gr.Textbox(lines=3, label="Enter your prompt:")
+        output_text = gr.Textbox(label="Generated Response")
+        performance_text = gr.Textbox(label="Performance Metrics", visible=False) # Initially hidden
+
+        def update_output(prompt):
+            response, performance = generate_response(prompt)
+            return response, performance # return both values
+
+        prompt_input.change(
+            fn=update_output,
+            inputs=prompt_input,
+            outputs=[output_text, performance_text],  # Output both response and metrics
+        )
+
+        # Button to show/hide performance metrics
+        show_metrics_button = gr.Button("Show/Hide Performance Metrics")
+        show_metrics_button.click(
+            fn=lambda visible: not visible,
+            inputs=[performance_text.visible],
+            outputs=[performance_text.visible],
+        )
 
 
-if __name__ == "__main__":
     demo.launch()
+
+
+if __name__ == "__main__":
+    main()