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app.py

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+import os
+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+print("=== Application Starting (LoRA Mode) ===")
+
+try:
+    # 1. 設定 Base Model (基礎模型)
+    # 這是您微調時使用的原始模型
+    BASE_MODEL_ID = "QLU-NLP/BianCang-Qwen2.5-7B"
+    
+    # 2. 自動偵測 Adapter (微調權重) 路徑
+    # 這是您上傳的資料夾
+    if os.path.exists("BianCang-Qwen2.5-7B-Instruct_finetuned_model_1"):
+        ADAPTER_PATH = "BianCang-Qwen2.5-7B-Instruct_finetuned_model_1"
+    else:
+        ADAPTER_PATH = "." 
+
+    print(f"Base Model: {BASE_MODEL_ID}")
+    print(f"Adapter Path: {ADAPTER_PATH}")
+
+    # 3. 載入 Tokenizer (通常使用 Base Model 的)
+    print("Loading Tokenizer...")
+    tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL_ID, trust_remote_code=True)
+
+    # 4. 載入 Base Model
+    print("Loading Base Model...")
+    try:
+        base_model = AutoModelForCausalLM.from_pretrained(
+            BASE_MODEL_ID,
+            device_map="auto",
+            torch_dtype=torch.float16,
+            trust_remote_code=True
+        )
+    except Exception as e:
+        print(f"GPU load failed: {e}. Fallback to CPU.")
+        base_model = AutoModelForCausalLM.from_pretrained(
+            BASE_MODEL_ID,
+            device_map="cpu",
+            trust_remote_code=True
+        )
+
+    # 5. 掛載 LoRA Adapter
+    print("Loading LoRA Adapter...")
+    try:
+        model = PeftModel.from_pretrained(base_model, ADAPTER_PATH)
+        print("LoRA Adapter loaded successfully!")
+    except Exception as e:
+        print(f"Failed to load adapter: {e}")
+        print("Running with Base Model only as fallback.")
+        model = base_model
+
+    def predict(message, history):
+        # 構建 Prompt (根據 Qwen 的格式)
+        # 注意：如果您的微調模型有特殊的 Prompt Template，請在此修改
+        
+        system_prompt = "你是一個專業的中醫藥材知識助手。你具備深厚的中醫理論基礎，特別擅長中藥材的性味、歸經、功效與主治。"
+        
+        messages = [
+            {"role": "system", "content": system_prompt}
+        ]
+        
+        for human, assistant in history:
+            messages.append({"role": "user", "content": human})
+            messages.append({"role": "assistant", "content": assistant})
+        
+        messages.append({"role": "user", "content": message})
+        
+        text = tokenizer.apply_chat_template(
+            messages,
+            tokenize=False,
+            add_generation_prompt=True
+        )
+        
+        model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+        
+        generated_ids = model.generate(
+            model_inputs.input_ids,
+            max_new_tokens=512,
+            temperature=0.7,
+            top_p=0.9,
+            do_sample=True
+        )
+        
+        generated_ids = [
+            output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+        ]
+        
+        response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+        return response
+
+    # 建立 Gradio 介面 (這會自動產生 API)
+    demo = gr.ChatInterface(
+        fn=predict,
+        title="BianCang-Qwen2.5-7B TCM Chatbot",
+        description="中醫藥材知識微調模型"
+    )
+
+    if __name__ == "__main__":
+        demo.launch(server_name="0.0.0.0", server_port=7860, show_api=True)
+
+except Exception as e:
+    print(f"!!! CRITICAL ERROR ===\n{e}\n======================")
+    raise e

requirements.txt

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+transformers>=4.46.0
+accelerate>=0.26.0
+gradio>=4.0.0
+peft>=0.7.0
+scipy