Update app.py

app.py

@@ -1,64 +1,111 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+from transformers import AutoTokenizer, AutoModelForCausalLM
+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")
+# Initialize model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("diabolic6045/ELN-Llama-1B-base")
+model = AutoModelForCausalLM.from_pretrained("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 respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+    def clear_history(self):
+        self.chat_history = []
+        return [], []
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+# Initialize chatbot
+chatbot = ChatBot(model, tokenizer)
 
-    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)",
-        ),
-    ],
-)
+# Example conversations
+examples = [
+    ["Hello! How are you today?"],
+    ["Can you explain what machine learning is?"],
+    ["Write a short poem about artificial intelligence."],
+]
 
+# 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()
+    demo.launch(share=True)