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import gradio as gr |
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from huggingface_hub import hf_hub_download |
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from llama_cpp import Llama |
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import html |
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def download_model(): |
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model_name = "bartowski/DeepSeek-Coder-V2-Lite-Instruct-GGUF" |
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model_file = "DeepSeek-Coder-V2-Lite-Instruct-Q6_K.gguf" |
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try: |
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return hf_hub_download(repo_id=model_name, filename=model_file) |
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except Exception as e: |
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print(f"Model download failed: {e}") |
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return None |
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def initialize_model(): |
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try: |
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model_path = download_model() |
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if not model_path: |
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return None |
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return Llama( |
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model_path=model_path, |
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n_ctx=4096, |
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n_threads=4, |
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n_gpu_layers=-1 |
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) |
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except Exception as e: |
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print(f"Error initializing model: {e}") |
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return None |
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llm = initialize_model() |
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system_prompt = ( |
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"You are a helpful AI coding assistant. Your mission is to help people with programming " |
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"and technical questions, providing clear and concise answers." |
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) |
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def respond(message, chat_history): |
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if not message or not llm: |
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return chat_history |
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messages = [{"role": "system", "content": system_prompt}] |
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for user_msg, bot_msg in chat_history: |
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messages.append({"role": "user", "content": user_msg}) |
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messages.append({"role": "assistant", "content": bot_msg}) |
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messages.append({"role": "user", "content": message}) |
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response = llm.create_chat_completion( |
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messages=messages, |
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max_tokens=1000, |
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stop=["User:"], |
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stream=False |
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) |
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ai_response = response['choices'][0]['message']['content'] |
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chat_history.append((message, ai_response)) |
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return chat_history |
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with gr.Blocks() as demo: |
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gr.Markdown("# AI Coding Assistant") |
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chatbot = gr.Chatbot(height=500) |
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msg = gr.Textbox(label="Your Message") |
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clear = gr.Button("Clear") |
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msg.submit(respond, [msg, chatbot], chatbot) |
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clear.click(lambda: None, None, chatbot, queue=False) |
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if __name__ == "__main__": |
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demo.launch(server_name="0.0.0.0", server_port=7860) |