Delete app.py

app.py

@@ -1,108 +0,0 @@
-import spaces
-import gradio as gr
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
-from threading import Thread
-import traceback
-
-model_path = 'infly/OpenCoder-8B-Instruct'
-
-# Loading the tokenizer and model from Hugging Face's model hub.
-tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
-model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, torch_dtype=torch.bfloat16)
-
-# using CUDA for an optimal experience
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-model = model.to(device)
-
-# Defining a custom stopping criteria class for the model's text generation.
-class StopOnTokens(StoppingCriteria):
-    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
-        stop_ids = [96539]  # IDs of tokens where the generation should stop.
-        for stop_id in stop_ids:
-            if input_ids[0][-1] == stop_id:  # Checking if the last generated token is a stop token.
-                return True
-        return False
-
-
-system_role= 'system'
-user_role = 'user'
-assistant_role = "assistant"
-
-sft_start_token =  "<|im_start|>"
-sft_end_token = "<|im_end|>"
-ct_end_token = "<|endoftext|>"
-
-# system_prompt= 'You are a CodeLLM developed by INF.'
-
-
-# Function to generate model predictions.
-
-@spaces.GPU()
-def predict(message, history):
-
-    try:
-        stop = StopOnTokens()
-    
-        model_messages = []
-        # print(f'history: {history}')
-
-        for i, item in enumerate(history):
-            model_messages.append({"role": user_role, "content": item[0]})
-            model_messages.append({"role": assistant_role, "content": item[1]})
-    
-        model_messages.append({"role": user_role, "content": message})
-        
-        print(f'model_messages: {model_messages}')
-    
-        # print(f'model_final_inputs: {tokenizer.apply_chat_template(model_messages, add_generation_prompt=True, tokenize=False)}', flush=True)
-        model_inputs = tokenizer.apply_chat_template(model_messages, add_generation_prompt=True, return_tensors="pt").to(device)
-        # model_inputs = tokenizer([messages], return_tensors="pt").to(device)
-        
-        streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
-        generate_kwargs = dict(
-            input_ids=model_inputs,
-            streamer=streamer,
-            max_new_tokens=1024,
-            do_sample=False,
-            stopping_criteria=StoppingCriteriaList([stop])
-        )
-        
-        t = Thread(target=model.generate, kwargs=generate_kwargs)
-        t.start()  # Starting the generation in a separate thread.
-        partial_message = ""
-        for new_token in streamer:
-            partial_message += new_token
-            if sft_end_token in partial_message:  # Breaking the loop if the stop token is generated.
-                break
-            yield partial_message
-
-    except Exception as e:
-        print(traceback.format_exc())
-
-
-css = """
-full-height {
-    height: 100%;
-}
-"""
-
-prompt_examples = [
-    'Write a quick sort algorithm in python.',
-    'Write a greedy snake game using pygame.',
-    'How to use numpy?'
-]
-
-placeholder = """
-<div style="opacity: 0.5;">
-    <img src="https://raw.githubusercontent.com/OpenCoder-llm/opencoder-llm.github.io/refs/heads/main/static/images/opencoder_icon.jpg" style="width:20%;">
-</div>
-"""
-
-
-chatbot = gr.Chatbot(label='OpenCoder', placeholder=placeholder) 
-with gr.Blocks(theme=gr.themes.Soft(), fill_height=True) as demo:
-    
-    gr.ChatInterface(predict, chatbot=chatbot, fill_height=True, examples=prompt_examples, css=css)
-
-    demo.launch()  # Launching the web interface.