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app.py

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+# ==============================================================================
+# app.py - GRADIO WEB UI FOR HF SPACES
+# ==============================================================================
+import gradio as gr
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+print("--- Setting up Model and Tokenizer for Gradio Space ---")
+
+# --- Configuration ---
+base_model_name = "Qwen/Qwen-1_8B-Chat"
+adapter_model_name = "jinv2/qwen-1.8b-chat-lora-stock-quant-edu" # YOUR HF MODEL ID
+# Device will be determined by the Space hardware (CPU or GPU)
+# device_map="auto" will handle this.
+print(f"Base model: {base_model_name}")
+print(f"Adapter model: {adapter_model_name}")
+
+# --- Load Tokenizer ---
+print(f"Loading tokenizer for {base_model_name}...")
+tokenizer = AutoTokenizer.from_pretrained(base_model_name, trust_remote_code=True)
+qwen_pad_eos_token_id = 151643
+if tokenizer.pad_token_id is None:
+    tokenizer.pad_token_id = qwen_pad_eos_token_id
+if tokenizer.eos_token_id is None:
+    tokenizer.eos_token_id = qwen_pad_eos_token_id
+print("Tokenizer configured.")
+
+
+# --- Load Base Model ---
+print(f"Loading base model {base_model_name}...")
+# Use torch_dtype="auto" to let transformers pick best dtype for the hardware
+# On CPU Spaces, float16 might not be ideal.
+# On GPU Spaces, device_map="auto" should place it on GPU.
+base_model = AutoModelForCausalLM.from_pretrained(
+    base_model_name,
+    torch_dtype="auto", # Let transformers decide based on hardware
+    device_map="auto",
+    trust_remote_code=True
+)
+print("Base model loaded.")
+
+# --- Load LoRA Adapter ---
+print(f"Loading LoRA adapter from {adapter_model_name}...")
+try:
+    # Ensure the model is on the correct device before applying PEFT if device_map wasn't "auto"
+    # model = PeftModel.from_pretrained(base_model.to(device), adapter_model_name) # If base_model not on auto
+    model = PeftModel.from_pretrained(base_model, adapter_model_name)
+    print(f"LoRA adapter '{adapter_model_name}' loaded successfully.")
+except Exception as e:
+    print(f"Error loading LoRA adapter: {e}. Using base model as fallback.")
+    model = base_model
+
+model = model.eval()
+print("Model is ready for inference.")
+
+# --- Define the prediction function for Gradio ---
+def predict(message, chat_history_tuples):
+    print(f"\nUser message: {message}")
+    
+    # Convert Gradio's chat_history (list of lists) to model.chat history format (list of tuples)
+    model_chat_history = []
+    if chat_history_tuples: # chat_history_tuples is list of [user, bot] pairs
+        for user_turn, bot_turn in chat_history_tuples:
+            if user_turn is not None and bot_turn is not None: # Ensure both are present
+                 model_chat_history.append((user_turn, bot_turn))
+            elif user_turn is not None: # Handle cases where bot_turn might be None initially
+                 model_chat_history.append((user_turn, ""))
+
+
+    system_prompt = "你是一个专业的股票量化投教助手，请用通俗易懂的大白话回答问题，并尽量保持友好和耐心。"
+    
+    print(f"Model history passed to model.chat: {model_chat_history}")
+    # The model.chat method returns (response, history_tuples)
+    # where history_tuples is the updated list of (query, response) tuples.
+    response_text, updated_model_history = model.chat(
+        tokenizer,
+        message,
+        history=model_chat_history,
+        system=system_prompt
+    )
+    print(f"Model response: {response_text}")
+    
+    # Gradio Chatbot expects a list of lists: [[user_msg1, bot_msg1], [user_msg2, bot_msg2]]
+    # We can reconstruct this from updated_model_history
+    gradio_display_history = []
+    for q, r in updated_model_history:
+        gradio_display_history.append([q, r])
+        
+    return "", gradio_display_history # Return empty string to clear textbox, and updated history list of lists
+
+
+# --- Create Gradio Interface ---
+print("\n--- Building Gradio Interface ---")
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown(
+        f"""
+        # 股票量化投教助手 (Stock Quant Educational Assistant) 📈💡
+        微调自 **{base_model_name}** ，使用了 LoRA 适配器 **{adapter_model_name}**.
+        由 **天算AI科技研发实验室 (Natural Algorithm AI R&D Lab) - jinv2** 开发。
+        *这是一个实验性模型，知识有限，请勿用于真实投资决策。*
+        """
+    )
+    chatbot = gr.Chatbot(label="聊天窗口", height=550, avatar_images=("/kaggle/input/user-image/USER.png", "/kaggle/input/bot-image/ROBOT.png")) # Optional: provide paths to user/bot avatar images if you upload them
+    msg = gr.Textbox(label="你的问题：", placeholder="例如：什么是移动平均线？", lines=2)
+    
+    with gr.Row():
+        submit_button = gr.Button("发送 (Send)", variant="primary")
+        clear_button = gr.Button("清除聊天记录 (Clear)")
+
+    # When message is submitted (Enter or button click)
+    # The inputs to `predict` are `msg` and `chatbot` (which is the history)
+    # The outputs of `predict` are `msg` (to clear it) and `chatbot` (to update it)
+    msg.submit(predict, [msg, chatbot], [msg, chatbot])
+    submit_button.click(predict, [msg, chatbot], [msg, chatbot])
+    
+    clear_button.click(lambda: (None, None, None), None, [msg, chatbot], queue=False)
+
+
+    gr.Examples(
+        examples=[
+            "什么是K线图？",
+            "MACD指标的金叉代表什么？",
+            "网格交易有什么风险？",
+            "RSI指标怎么看超买超卖？",
+            "量化交易能赚钱吗？"
+        ],
+        inputs=msg,
+        label="示例问题 (Example Questions)"
+    )
+
+# --- Launch the interface ---
+print("--- Launching Gradio Interface for HF Space ---")
+# For HF Spaces, queue().launch() is usually sufficient.
+# Share=True is not needed as Spaces provides its own URL.
+# In_browser=True might be useful for local testing.
+demo.queue().launch(debug=False) # debug=True for more logs if needed

requirements.txt

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+gradio
+transformers>=4.40.1
+peft>=0.9.0
+torch>=2.0.0
+accelerate>=0.28.0
+sentencepiece
+# bitsandbytes # 如果你解决了GPU编译问题并想用QLoRA，可以加上，否则移除
+# For Qwen specific tokenizer or model files if not automatically handled by transformers
+# tiktoken (Qwen-1.8B tokenizer might use this, or sentencepiece)
+# transformers_stream_generator (Qwen-1.8B might need this for streaming)