Delete app.py

app.py

@@ -1,112 +0,0 @@
-import gradio as gr
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
-from threading import Thread
-import spaces
-
-class ChatInterface:
-    def __init__(self, model_name="deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"):
-        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
-        self.model = AutoModelForCausalLM.from_pretrained(
-            model_name,
-            torch_dtype=torch.float16,
-            device_map="auto"
-        )
-        
-    def format_chat_prompt(self, message, history, system_message):
-        messages = [{"role": "system", "content": system_message}]
-        
-        for user_msg, assistant_msg in history:
-            if user_msg:
-                messages.append({"role": "user", "content": user_msg})
-            if assistant_msg:
-                messages.append({"role": "assistant", "content": assistant_msg})
-                
-        messages.append({"role": "user", "content": message})
-        
-        # Format messages according to model's expected chat template
-        prompt = self.tokenizer.apply_chat_template(
-            messages,
-            tokenize=False,
-            add_generation_prompt=True
-        )
-        return prompt
-
-    @spaces.GPU
-    def generate_response(
-        self,
-        message,
-        history,
-        system_message,
-        max_tokens,
-        temperature,
-        top_p,
-    ):
-        prompt = self.format_chat_prompt(message, history, system_message)
-        inputs = self.tokenizer(prompt, return_tensors="pt").to(self.model.device)
-        
-        # Setup streamer
-        streamer = TextIteratorStreamer(
-            self.tokenizer,
-            timeout=10.0,
-            skip_prompt=True,
-            skip_special_tokens=True
-        )
-        
-        # Generate in a separate thread to enable streaming
-        generation_kwargs = dict(
-            inputs=inputs,
-            streamer=streamer,
-            max_new_tokens=max_tokens,
-            temperature=temperature,
-            top_p=top_p,
-            do_sample=True,
-        )
-        
-        thread = Thread(target=self.model.generate, kwargs=generation_kwargs)
-        thread.start()
-        
-        # Stream the response
-        response = ""
-        for new_text in streamer:
-            response += new_text
-            yield response
-            
-def create_demo():
-    chat_interface = ChatInterface()
-    
-    demo = gr.ChatInterface(
-        chat_interface.generate_response,
-        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)"
-            ),
-        ],
-    )
-    return demo
-
-if __name__ == "__main__":
-    demo = create_demo()
-    demo.launch()