Update app.py

app.py

@@ -1,64 +1,123 @@
+import os
 import gradio as gr
-from huggingface_hub import InferenceClient
+from gradio import ChatMessage
+from typing import Iterator
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+import torch
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# Загрузка модели и токенизатора
+model_name = "FractalGPT/RuQwen2.5-3B-Instruct-AWQ"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
 
+# Создание пайплайна для генерации текста
+text_generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0)
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+def format_chat_history(messages: list) -> str:
+    """
+    Форматирует историю чата в строку, которую модель может понять.
+    """
+    formatted_history = ""
+    for message in messages:
+        if message.get("role") == "user":
+            formatted_history += f"User: {message.get('content', '')}\n"
+        elif message.get("role") == "assistant":
+            formatted_history += f"Assistant: {message.get('content', '')}\n"
+    return formatted_history
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+def stream_model_response(user_message: str, messages: list) -> Iterator[list]:
+    """
+    Генерирует ответ модели с поддержкой истории чата.
+    """
+    try:
+        print(f"\n=== New Request ===")
+        print(f"User message: {user_message}")
+        
+        # Форматируем историю чата
+        chat_history = format_chat_history(messages)
+        
+        # Формируем входной текст для модели
+        input_text = f"{chat_history}User: {user_message}\nAssistant:"
+        
+        # Генерируем ответ модели
+        response = text_generator(input_text, max_length=512, do_sample=True, temperature=0.7, top_p=0.9)
+        model_response = response[0]['generated_text'].split("Assistant:")[-1].strip()
+        
+        # Добавляем ответ модели в историю чата
+        messages.append(
+            ChatMessage(
+                role="assistant",
+                content=model_response
+            )
+        )
+        
+        yield messages
+        
+        print(f"\n=== Final Response ===\n{model_response}")
+                
+    except Exception as e:
+        print(f"\n=== Error ===\n{str(e)}")
+        messages.append(
+            ChatMessage(
+                role="assistant",
+                content=f"I apologize, but I encountered an error: {str(e)}"
+            )
+        )
+        yield messages
 
-    messages.append({"role": "user", "content": message})
+def user_message(msg: str, history: list) -> tuple[str, list]:
+    """Добавляет сообщение пользователя в историю чата"""
+    history.append(ChatMessage(role="user", content=msg))
+    return "", history
+    
 
-    response = ""
+# Создаем интерфейс Gradio
+with gr.Blocks(theme=gr.themes.Citrus(), fill_height=True) as demo:
+    gr.Markdown("# Chat with FractalGPT/RuQwen2.5-3B-Instruct-AWQ 💭")
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+    chatbot = gr.Chatbot(
+        type="messages",
+        label="FractalGPT Chatbot",
+        render_markdown=True,
+        scale=1,
+        avatar_images=(None, "https://huggingface.co/FractalGPT/RuQwen2.5-3B-Instruct-AWQ/resolve/main/avatar.png")
+    )
 
-        response += token
-        yield response
+    with gr.Row(equal_height=True):
+        input_box = gr.Textbox(
+            lines=1,
+            label="Chat Message",
+            placeholder="Type your message here...",
+            scale=4
+        )
 
+        clear_button = gr.Button("Clear Chat", scale=1)
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, 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)",
-        ),
-    ],
-)
+    # Настраиваем обработчики событий
+    msg_store = gr.State("")  # Хранилище для сохранения сообщения пользователя
+    
+    input_box.submit(
+        lambda msg: (msg, msg, ""),  # Сохраняем сообщение и очищаем поле ввода
+        inputs=[input_box],
+        outputs=[msg_store, input_box, input_box],
+        queue=False
+    ).then(
+        user_message,  # Добавляем сообщение пользователя в чат
+        inputs=[msg_store, chatbot],
+        outputs=[input_box, chatbot],
+        queue=False
+    ).then(
+        stream_model_response,  # Генерируем и передаем ответ модели
+        inputs=[msg_store, chatbot],
+        outputs=chatbot
+    )
 
+    clear_button.click(
+        lambda: ([], "", ""),
+        outputs=[chatbot, input_box, msg_store],
+        queue=False
+    )
 
+# Запускаем интерфейс
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(debug=True)