Update app.py

app.py

@@ -141,109 +141,3 @@ with gr.Blocks(fill_height=True, css=css) as demo:
 if __name__ == "__main__":
     demo.launch()
 
-
-
-    
-import spaces
-import os
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
-from transformers import TextIteratorStreamer
-from threading import Thread
-
-import gradio as gr
-
-text_generator = None
-is_hugging_face = True
-model_id = "AXCXEPT/phi-4-deepseek-R1K-RL-EZO"
-model_id = "AXCXEPT/phi-4-open-R1-Distill-EZOv1"
-
-huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
-huggingface_token = None
-device = "auto" # torch.device("cuda" if torch.cuda.is_available() else "cpu")
-device = "cuda"
-dtype = torch.bfloat16
-dtype = torch.float16
-
-if not huggingface_token:
-    pass
-    print("no HUGGINGFACE_TOKEN if you need set secret ")
-    #raise ValueError("HUGGINGFACE_TOKEN environment variable is not set")
-        
-
-    
-    
-    
-
-
-
-tokenizer = AutoTokenizer.from_pretrained(model_id, token=huggingface_token)
-
-print(model_id,device,dtype)
-histories = []
-#model = None
-
-
-
-if not is_hugging_face:
-    model = AutoModelForCausalLM.from_pretrained(
-        model_id, token=huggingface_token ,torch_dtype=dtype,device_map=device
-    )
-    text_generator = pipeline("text-generation", model=model, tokenizer=tokenizer,torch_dtype=dtype,device_map=device,stream=True ) #pipeline has not to(device)
-    
-    if next(model.parameters()).is_cuda:
-        print("The model is on a GPU")
-    else:
-        print("The model is on a CPU")
-
-    #print(f"text_generator.device='{text_generator.device}")
-    if str(text_generator.device).strip() == 'cuda':
-        print("The pipeline is using a GPU")
-    else:
-        print("The pipeline is using a CPU")
-
-print("initialized")
-
-
-def generate_text(messages):
-    if is_hugging_face:#need everytime initialize for ZeroGPU
-        model = AutoModelForCausalLM.from_pretrained(
-                model_id, token=huggingface_token ,torch_dtype=dtype,device_map=device
-            )
-        model.to(device)
-    question = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-    question = tokenizer(question, return_tensors="pt").to(device)
-
-    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True)
-    generation_kwargs = dict(question, streamer=streamer, max_new_tokens=200)
-    thread = Thread(target=model.generate, kwargs=generation_kwargs)
-    
-    generated_output = ""
-    thread.start()
-    for new_text in streamer:
-        generated_output += new_text
-        yield generated_output            
-generate_text.zerogpu = True
-    
-       
-
-@spaces.GPU(duration=60)
-def call_generate_text(message, history):
-   # history.append({"role": "user", "content": message})
-    #print(message)
-    #print(history)
-   
-    messages = history+[{"role":"user","content":message}]
-    try:
-        
-        for text in generate_text(messages):
-            yield text
-    except RuntimeError  as e:
-        print(f"An unexpected error occurred: {e}")
-        yield ""
-
-demo = gr.ChatInterface(call_generate_text,type="messages")
-
-#if __name__ == "__main__":
-demo.queue()
-demo.launch()