feat(frontend): 初步实现聊天页面的智能回复功能

frontend/chat_page.py

@@ -2,7 +2,7 @@ import gradio as gr
 import sys
 from pathlib import Path
 sys.path.append(str(Path(__file__).resolve().parent.parent))
-from global_var import model,tokenizer
+from global_var import get_model, get_tokenizer
 
 def chat_page():
     with gr.Blocks() as demo:
@@ -17,11 +17,39 @@ def chat_page():
             return "", history + [{"role": "user", "content": user_message}]
 
         def bot(history: list):
-            bot_message = random.choice(["How are you?", "I love you", "I'm very hungry"])
+            model = get_model()
-            history.append({"role": "assistant", "content": ""})
+            tokenizer = get_tokenizer()
-            for character in bot_message:
+            print(tokenizer)
-                history[-1]['content'] += character
+            print(model)
-                time.sleep(0.1)
+
+            # 获取用户的最新消息
+            user_message = history[-1]["content"]
+            
+            # 使用 tokenizer 对消息进行预处理
+            messages = [{"role": "user", "content": user_message}]
+            inputs = tokenizer.apply_chat_template(
+                messages,
+                tokenize=True,
+                add_generation_prompt=True,
+                return_tensors="pt",
+            ).to("cuda")
+            
+            # 使用 TextStreamer 进行流式生成
+            from transformers import TextStreamer
+            text_streamer = TextStreamer(tokenizer, skip_prompt=True)
+            
+            # 调用模型进行推理
+            generated_text = ""
+            for new_token in model.generate(
+                input_ids=inputs,
+                streamer=text_streamer,
+                max_new_tokens=1024,
+                use_cache=False,
+                temperature=1.5,
+                min_p=0.1,
+            ):
+                generated_text += tokenizer.decode(new_token, skip_special_tokens=True)
+                history.append({"role": "assistant", "content": generated_text})
                 yield history
 
         msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
@@ -32,4 +60,8 @@ def chat_page():
     return demo
 
 if __name__ == "__main__":
-    chat_page().queue().launch()
+    from model_manage_page import model_manage_page
+    # 装载两个页面
+    demo = gr.TabbedInterface([model_manage_page(), chat_page()], ["模型管理", "聊天"])
+    demo.queue()
+    demo.launch()