Create app.py

app.py

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+import gradio as gr
+from huggingface_hub import InferenceClient
+from optimum.intel import OVModelForCausalLM
+from transformers import AutoTokenizer, pipeline
+
+# 載入模型和標記器
+model_id = "hsuwill000/Qwen2.5-1.5B-Instruct-openvino-8bit"
+model = OVModelForCausalLM.from_pretrained(model_id, device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+# 建立生成管道
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+def respond(message, history):
+    # 將當前訊息與歷史訊息合併
+    input_text = message if not history else history[-1]["content"] + " " + message
+    input_text = message+",(450字內回覆)"
+    # 獲取模型的回應
+    response = pipe(input_text, max_length=512, truncation=True, num_return_sequences=1)
+    reply = response[0]['generated_text']
+    
+    # 返回新的消息格式
+    print(f"Message: {message}")
+    print(f"Reply: {reply}")
+    return reply
+    
+# 設定 Gradio 的聊天界面
+demo = gr.ChatInterface(fn=respond, title="Qwen2.5-3B-Instruct-openvino", description="Qwen2.5-3B-Instruct-openvino", type='messages')
+
+if __name__ == "__main__":
+    demo.launch()