Update app.py

app.py

@@ -1,78 +1,78 @@
-    import gradio as gr
-    from huggingface_hub import InferenceClient, hf_hub_download
-    from transformers import AutoTokenizer
-    import os
-    import torch
+import gradio as gr
+from huggingface_hub import InferenceClient, hf_hub_download
+from transformers import AutoTokenizer
+import os
+import torch
 
-    # --- 设置模型 ID ---
-    model_id = "xingyu1996/tiger-gpt2"
-    client = InferenceClient(model_id)
+# --- 设置模型 ID ---
+model_id = "xingyu1996/tiger-gpt2"
+client = InferenceClient(model_id)
 
-    # --- 关键变化: 直接加载与训练时相同的分词器 ---
-    tokenizer = AutoTokenizer.from_pretrained("gpt2")
+# --- 关键变化: 直接加载与训练时相同的分词器 ---
+tokenizer = AutoTokenizer.from_pretrained("gpt2")
 
-    def respond(
-        message,
-        history: list[tuple[str, str]],
-        max_tokens,
-        temperature,
-        top_p,
-    ):
-        prompt = message
-        response_ids = []
-        response_text = ""
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    max_tokens,
+    temperature,
+    top_p,
+):
+    prompt = message
+    response_ids = []
+    response_text = ""
 
-        # --- 参数准备 ---
-        generation_args = {
-            "max_new_tokens": max_tokens,
-            "stream": True,
-            "details": True,  # 让 API 返回 token ID (重要变化)
-        }
+    # --- 参数准备 ---
+    generation_args = {
+        "max_new_tokens": max_tokens,
+        "stream": True,
+        "details": True,  # 让 API 返回 token ID (重要变化)
+    }
 
-        if temperature is not None and temperature > 0:
-            generation_args["temperature"] = temperature
-        if top_p is not None and top_p < 1.0:
-            generation_args["top_p"] = top_p
+    if temperature is not None and temperature > 0:
+        generation_args["temperature"] = temperature
+    if top_p is not None and top_p < 1.0:
+        generation_args["top_p"] = top_p
 
-        try:
-            # --- 调用 API, 获取 token IDs ---
-            for output in client.text_generation(prompt, **generation_args):
-                if hasattr(output, 'token'):  # 流式生成时的输出
-                    # 这里 output.token 是一个字典，包含 id 和 text
-                    token_id = output.token.id
-                    response_ids.append(token_id)
-                    
-                    # 使用我们自己的 tokenizer 来解码
-                    current_text = tokenizer.decode(response_ids, skip_special_tokens=True)
-                    response_text = current_text
-                    
-                    yield response_text
-                elif hasattr(output, 'generated_text'):  # 非流式生成时的最终输出
-                    # 如果直接返回了完整文本，就用它
-                    response_text = output.generated_text
-                    yield response_text
-        except Exception as e:
-            print(f"推理时发生错误: {type(e).__name__} - {e}")
-            yield f"抱歉，推理时遇到错误: {type(e).__name__} - {str(e)}"
+    try:
+        # --- 调用 API, 获取 token IDs ---
+        for output in client.text_generation(prompt, **generation_args):
+            if hasattr(output, 'token'):  # 流式生成时的输出
+                # 这里 output.token 是一个字典，包含 id 和 text
+                token_id = output.token.id
+                response_ids.append(token_id)
+                
+                # 使用我们自己的 tokenizer 来解码
+                current_text = tokenizer.decode(response_ids, skip_special_tokens=True)
+                response_text = current_text
+                
+                yield response_text
+            elif hasattr(output, 'generated_text'):  # 非流式生成时的最终输出
+                # 如果直接返回了完整文本，就用它
+                response_text = output.generated_text
+                yield response_text
+    except Exception as e:
+        print(f"推理时发生错误: {type(e).__name__} - {e}")
+        yield f"抱歉，推理时遇到错误: {type(e).__name__} - {str(e)}"
 
 
-    # 其他 Gradio 界面代码不变
-    demo = gr.ChatInterface(
-        respond,
-        additional_inputs=[
-            gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-            gr.Slider(minimum=0.1, maximum=2.0, 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)",
-            ),
-        ],
-        title=f"推理测试: {model_id}",
-        description="输入中文文本，模型将进行补全。"
-    )
+# 其他 Gradio 界面代码不变
+demo = gr.ChatInterface(
+    respond,
+    additional_inputs=[
+        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+        gr.Slider(minimum=0.1, maximum=2.0, 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)",
+        ),
+    ],
+    title=f"推理测试: {model_id}",
+    description="输入中文文本，模型将进行补全。"
+)
 
-    if __name__ == "__main__":
-        demo.launch()
+if __name__ == "__main__":
+    demo.launch()