Update app.py

app.py

@@ -1,64 +1,296 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-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")
+import os
+import spaces
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from threading import Thread
+import torch
+import time
 
+# Set environment variables
+HF_TOKEN = os.environ.get("HF_TOKEN", None)
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+# Apollo system prompt
+SYSTEM_PROMPT = "You are Apollo, a multilingual medical model. You communicate with people and assist them."
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+# Apollo model options
+APOLLO_MODELS = {
+    "Apollo": [
+        "FreedomIntelligence/Apollo-7B",
+        "FreedomIntelligence/Apollo-6B",
+        "FreedomIntelligence/Apollo-2B",
+        "FreedomIntelligence/Apollo-0.5B",
+        
+    ],
+    "Apollo2": [
+        "FreedomIntelligence/Apollo2-7B",
+        "FreedomIntelligence/Apollo2-3.8B",
+        "FreedomIntelligence/Apollo2-2B",
+    ],
+    "Apollo-MoE": [
+        "FreedomIntelligence/Apollo-MoE-7B",
+        "FreedomIntelligence/Apollo-MoE-1.5B",
+        "FreedomIntelligence/Apollo-MoE-0.5B",
+        
+    ]
+}
 
-    messages.append({"role": "user", "content": message})
+# CSS styles
+css = """
+h1 {
+  text-align: center;
+  display: block;
+}
+.gradio-container {
+  max-width: 1200px;
+  margin: auto;
+}
+"""
 
-    response = ""
+# Global variables to store currently loaded model and tokenizer
+current_model = None
+current_tokenizer = None
+current_model_path = None
 
-    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
+@spaces.GPU(duration=120)
+def load_model(model_path, progress=gr.Progress()):
+    """Load the selected model and tokenizer"""
+    global current_model, current_tokenizer, current_model_path
+    
+    # If the same model is already loaded, don't reload it
+    if current_model_path == model_path and current_model is not None:
+        return "Model already loaded, no need to reload."
+    
+    # Clean up previously loaded model (if any)
+    if current_model is not None:
+        del current_model
+        del current_tokenizer
+        torch.cuda.empty_cache()
+    
+    progress(0.1, desc=f"Starting to load model {model_path}...")
+    
+    try:
+        progress(0.3, desc="Loading tokenizer...")
+        current_tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
+        
+        progress(0.5, desc="Loading model...")
+        current_model = AutoModelForCausalLM.from_pretrained(
+            model_path, 
+            device_map="auto", 
+            torch_dtype=torch.float16
+        )
+        
+        current_model_path = model_path
+        progress(1.0, desc="Model loading complete!")
+        return f"Model {model_path} successfully loaded."
+    except Exception as e:
+        progress(1.0, desc="Model loading failed!")
+        return f"Model loading failed: {str(e)}"
 
-        response += token
-        yield response
+@spaces.GPU(duration=120)
+def generate_response_non_streaming(instruction, model_name, temperature=0.7, max_tokens=1024):
+    """Generate a response from the Apollo model (non-streaming)"""
+    global current_model, current_tokenizer, current_model_path
+    
+    # If model is not yet loaded, load it first
+    if current_model_path != model_name or current_model is None:
+        load_message = load_model(model_name)
+        if "failed" in load_message.lower():
+            return load_message
+    
+    try:
+        # 检查模型是否有聊天模板
+        if hasattr(current_tokenizer, 'chat_template') and current_tokenizer.chat_template:
+            # 使用模型的聊天模板
+            messages = [
+                {"role": "system", "content": SYSTEM_PROMPT},
+                {"role": "user", "content": instruction}
+            ]
+            
+            # 使用模型的聊天模板格式化输入
+            chat_input = current_tokenizer.apply_chat_template(
+                messages, 
+                tokenize=True, 
+                return_tensors="pt"
+            ).to(current_model.device)
+        else:
+            # 使用指定的提示格式
+            prompt = f"User:{instruction}\nAssistant:"
+            chat_input = current_tokenizer.encode(prompt, return_tensors="pt").to(current_model.device)
+            
+            # 获取<|endoftext|>的token id，用于停止生成
+            eos_token_id = current_tokenizer.eos_token_id
+        
+        # 生成响应
+        output = current_model.generate(
+            input_ids=chat_input,
+            max_new_tokens=max_tokens,
+            temperature=temperature,
+            do_sample=(temperature > 0),
+            eos_token_id=current_tokenizer.eos_token_id  # 使用<|endoftext|>作为停止标记
+        )
+        
+        # 解码并返回生成的文本
+        generated_text = current_tokenizer.decode(output[0][len(chat_input[0]):], skip_special_tokens=True)
+        return generated_text
+    except Exception as e:
+        return f"生成响应时出错: {str(e)}"
 
+def update_chat_with_response(chatbot, instruction, model_name, temperature, max_tokens):
+    """Updates the chatbot with non-streaming response"""
+    global current_model, current_tokenizer, current_model_path
+    
+    # If model is not yet loaded, load it first
+    if current_model_path != model_name or current_model is None:
+        load_result = load_model(model_name)
+        if "failed" in load_result.lower():
+            new_chat = list(chatbot)
+            new_chat[-1] = (instruction, load_result)
+            return new_chat
+    
+    # Generate response using the non-streaming function
+    response = generate_response_non_streaming(instruction, model_name, temperature, max_tokens)
+    
+    # Create a copy of the current chatbot and add the response
+    new_chat = list(chatbot)
+    new_chat[-1] = (instruction, response)
+    
+    return new_chat
 
-"""
-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)",
-        ),
-    ],
-)
+def on_model_series_change(model_series):
+    """Update available model list based on selected model series"""
+    if model_series in APOLLO_MODELS:
+        return gr.update(choices=APOLLO_MODELS[model_series], value=APOLLO_MODELS[model_series][0])
+    return gr.update(choices=[], value=None)
 
+# Create Gradio interface
+with gr.Blocks(css=css) as demo:
+    # Title and description
+    favicon = "🩺"
+    gr.Markdown(
+        f"""# {favicon} Apollo Playground
+        This is a demo of the multilingual medical model series **[Apollo](https://huggingface.co/FreedomIntelligence/Apollo-7B-GGUF)**.
+        [Apollo1](https://arxiv.org/abs/2403.03640) supports 6 languages. [Apollo2](https://arxiv.org/abs/2410.10626) supports 50 languages.
+        """
+    )
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            # Model selection controls
+            model_series = gr.Dropdown(
+                choices=list(APOLLO_MODELS.keys()),
+                value="Apollo",
+                label="Select Model Series",
+                info="First choose Apollo, Apollo2 or Apollo-MoE"
+            )
+            
+            model_name = gr.Dropdown(
+                choices=APOLLO_MODELS["Apollo"],
+                value=APOLLO_MODELS["Apollo"][0],
+                label="Select Model Size",
+                info="Select the specific model size based on the chosen model series"
+            )
+            
+            # Parameter settings
+            with gr.Accordion("Generation Parameters", open=False):
+                temperature = gr.Slider(
+                    minimum=0.0, 
+                    maximum=1.0, 
+                    value=0.7, 
+                    step=0.05, 
+                    label="Temperature"
+                )
+                max_tokens = gr.Slider(
+                    minimum=128, 
+                    maximum=2048, 
+                    value=1024, 
+                    step=32, 
+                    label="Maximum Tokens"
+                )
+            
+            # Load model button
+            load_button = gr.Button("Load Model")
+            model_status = gr.Textbox(label="Model Status", value="No model loaded yet")
+        
+        with gr.Column(scale=2):
+            # Chat interface
+            chatbot = gr.Chatbot(label="Conversation", height=500, value=[])  # Initialize with empty list
+            user_input = gr.Textbox(
+                label="Input Medical Question",
+                placeholder="Example: What are the symptoms of hypertension? 高血压有哪些症状？",
+                lines=3
+            )
+            submit_button = gr.Button("Submit")
+            clear_button = gr.Button("Clear Chat")
+    
+    # Event handling
+    # Update model selection when model series changes
+    model_series.change(
+        fn=on_model_series_change,
+        inputs=model_series,
+        outputs=model_name
+    )
+    
+    # Load model
+    load_button.click(
+        fn=load_model,
+        inputs=model_name,
+        outputs=model_status
+    )
+    
+    # Handle message submission
+    def user_message_submitted(message, chat_history):
+        """Handle user submitted message"""
+        # Ensure chat_history is a list
+        if chat_history is None:
+            chat_history = []
+            
+        if message.strip() == "":
+            return "", chat_history
+        
+        # Add user message to chat history
+        chat_history = list(chat_history)
+        chat_history.append((message, None))
+        return "", chat_history
+    
+    # Bind message submission
+    submit_event = user_input.submit(
+        fn=user_message_submitted,
+        inputs=[user_input, chatbot],
+        outputs=[user_input, chatbot]
+    ).then(
+        fn=update_chat_with_response,
+        inputs=[chatbot, user_input, model_name, temperature, max_tokens],
+        outputs=chatbot
+    )
+    
+    submit_button.click(
+        fn=user_message_submitted,
+        inputs=[user_input, chatbot],
+        outputs=[user_input, chatbot]
+    ).then(
+        fn=update_chat_with_response,
+        inputs=[chatbot, user_input, model_name, temperature, max_tokens],
+        outputs=chatbot
+    )
+    
+    # Clear chat
+    clear_button.click(
+        fn=lambda: [],
+        outputs=chatbot
+    )
+    
+    examples = [
+        ["Últimamente tengo la tensión un poco alta, ¿cómo debo adaptar mis hábitos?"],
+        ["What are the common side effects of metformin?"],
+        ["中医和西医在治疗高血压方面有什么不同的观点？"],
+        ["मेरा सिर दर्द कर रहा है, मुझे क्या करना चाहिए? "],
+        ["Comment savoir si je suis diabétique ?"],
+        ["ما الدواء الذي يمكنني تناوله إذا لم أستطع النوم ليلاً؟"]
+    ]
+    gr.Examples(
+        examples=examples,
+        inputs=user_input
+    )
 
 if __name__ == "__main__":
     demo.launch()