Delete app.py

app.py

@@ -1,106 +0,0 @@
-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