Update app.py

app.py

@@ -1,111 +1,64 @@
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
+from huggingface_hub import InferenceClient
 
-# Initialize model and tokenizer
-tokenizer = AutoTokenizer.from_pretrained("diabolic6045/ELN-Llama-1B-base")
-model = AutoModelForCausalLM.from_pretrained("diabolic6045/ELN-Llama-1B-base")
+"""
+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("diabolic6045/ELN-Llama-1B-base")
 
-class ChatBot:
-    def __init__(self, model, tokenizer):
-        self.model = model
-        self.tokenizer = tokenizer
-        self.chat_history = []
-        
-    def generate_response(self, message, temperature=0.7, max_length=512):
-        # Format the conversation history
-        conversation = ""
-        for turn in self.chat_history:
-            conversation += f"User: {turn[0]}\nAssistant: {turn[1]}\n"
-        conversation += f"User: {message}\nAssistant:"
-        
-        # Tokenize and generate
-        inputs = self.tokenizer(conversation, return_tensors="pt", truncation=True)
-        
-        with torch.no_grad():
-            outputs = self.model.generate(
-                inputs["input_ids"],
-                max_length=max_length,
-                temperature=temperature,
-                do_sample=True,
-                pad_token_id=self.tokenizer.eos_token_id,
-                num_return_sequences=1,
-            )
-        
-        response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
-        response = response.split("Assistant:")[-1].strip()
-        
-        # Update chat history
-        self.chat_history.append((message, response))
-        return response, self.chat_history
 
-    def clear_history(self):
-        self.chat_history = []
-        return [], []
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+):
+    messages = [{"role": "system", "content": system_message}]
 
-# Initialize chatbot
-chatbot = ChatBot(model, tokenizer)
+    for val in history:
+        if val[0]:
+            messages.append({"role": "user", "content": val[0]})
+        if val[1]:
+            messages.append({"role": "assistant", "content": val[1]})
 
-# Example conversations
-examples = [
-    ["Hello! How are you today?"],
-    ["Can you explain what machine learning is?"],
-    ["Write a short poem about artificial intelligence."],
-]
+    messages.append({"role": "user", "content": message})
+
+    response = ""
+
+    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
+
+        response += token
+        yield response
+
+
+"""
+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)",
+        ),
+    ],
+)
 
-# Create the Gradio interface
-with gr.Blocks(css="footer {visibility: hidden}") as demo:
-    gr.Markdown("# LLaMA Chatbot")
-    gr.Markdown("Chat with the ELN-Llama-1B model. Try asking questions or having a conversation!")
-    
-    with gr.Row():
-        with gr.Column(scale=4):
-            chatbot_component = gr.Chatbot(
-                label="Chat History",
-                height=400
-            )
-            message = gr.Textbox(
-                label="Your message",
-                placeholder="Type your message here...",
-                lines=2
-            )
-            
-        with gr.Column(scale=1):
-            temperature = gr.Slider(
-                minimum=0.1,
-                maximum=1.0,
-                value=0.7,
-                step=0.1,
-                label="Temperature",
-                info="Higher values make output more random"
-            )
-            max_length = gr.Slider(
-                minimum=64,
-                maximum=1024,
-                value=512,
-                step=64,
-                label="Max Length",
-                info="Maximum length of generated response"
-            )
-            clear = gr.Button("Clear Conversation")
-    
-    gr.Examples(
-        examples=examples,
-        inputs=message,
-        label="Example prompts"
-    )
-    
-    # Handle interactions
-    message.submit(
-        chatbot.generate_response,
-        inputs=[message, temperature, max_length],
-        outputs=[chatbot_component]
-    )
-    clear.click(
-        chatbot.clear_history,
-        outputs=[chatbot_component, message]
-    )
 
-# Launch the interface
 if __name__ == "__main__":
-    demo.launch(share=True)
+    demo.launch()