| # import gradio as gr | |
| # from huggingface_hub import InferenceClient | |
| # """ | |
| # 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("Qwen/Qwen2.5-Coder-32B-Instruct") | |
| # def respond( | |
| # message, | |
| # history: list[tuple[str, str]], | |
| # system_message, | |
| # max_tokens, | |
| # temperature, | |
| # top_p, | |
| # ): | |
| # messages = [{"role": "system", "content": system_message}] | |
| # for val in history: | |
| # if val[0]: | |
| # messages.append({"role": "user", "content": val[0]}) | |
| # if val[1]: | |
| # messages.append({"role": "assistant", "content": val[1]}) | |
| # 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=2048, 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)", | |
| # ), | |
| # ], | |
| # ) | |
| # if __name__ == "__main__": | |
| # demo.launch() | |
| # import gradio as gr | |
| # from huggingface_hub import InferenceClient | |
| """ | |
| 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("Qwen/Qwen2.5-Coder-32B-Instruct") | |
| # def respond(message, history: list[tuple[str, str]]): | |
| # system_message = ( | |
| # "You are a helpful and experienced coding assistant specialized in web development. " | |
| # "Help the user by generating complete and functional code for building websites. " | |
| # "You can provide HTML, CSS, JavaScript, and backend code (like Flask, Node.js, etc.) based on their requirements. " | |
| # "Break down the tasks clearly if needed, and be friendly and supportive in your responses.") | |
| # max_tokens = 2048 | |
| # temperature = 0.7 | |
| # top_p = 0.95 | |
| # messages = [{"role": "system", "content": system_message}] | |
| # for val in history: | |
| # if val[0]: | |
| # messages.append({"role": "user", "content": val[0]}) | |
| # if val[1]: | |
| # messages.append({"role": "assistant", "content": val[1]}) | |
| # 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) | |
| # if __name__ == "__main__": | |
| # demo.launch() | |
| # import gradio as gr | |
| # from huggingface_hub import InferenceClient | |
| # """ | |
| # 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("Qwen/Qwen2.5-Coder-32B-Instruct") | |
| # def respond(message, history: list[tuple[str, str]]): | |
| # system_message = ( | |
| # "You are a helpful and experienced coding assistant specialized in web development. " | |
| # "Help the user by generating complete and functional code for building websites. " | |
| # "You can provide HTML, CSS, JavaScript, and backend code (like Flask, Node.js, etc.) based on their requirements. " | |
| # "Break down the tasks clearly if needed, and be friendly and supportive in your responses." | |
| # ) | |
| # max_tokens = 2048 | |
| # temperature = 0.7 | |
| # top_p = 0.95 | |
| # messages = [{"role": "system", "content": system_message}] | |
| # for val in history: | |
| # if val[0]: | |
| # messages.append({"role": "user", "content": val[0]}) | |
| # if val[1]: | |
| # messages.append({"role": "assistant", "content": val[1]}) | |
| # 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) | |
| # if __name__ == "__main__": | |
| # demo.launch() | |
| # import gradio as gr | |
| # from huggingface_hub import InferenceClient | |
| # # 1. Instantiate with named model param | |
| # client = InferenceClient(model="Qwen/Qwen2.5-Coder-32B-Instruct") | |
| # def respond(message, history: list[tuple[str, str]]): | |
| # system_message = ( | |
| # "You are a helpful and experienced coding assistant specialized in web development. " | |
| # "Help the user by generating complete and functional code for building websites. " | |
| # "You can provide HTML, CSS, JavaScript, and backend code (like Flask, Node.js, etc.) " | |
| # "based on their requirements." | |
| # ) | |
| # max_tokens = 2048 | |
| # temperature = 0.7 | |
| # top_p = 0.95 | |
| # # Build messages in OpenAI-compatible format | |
| # 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}) | |
| # response = "" | |
| # # 2. Use named parameters and alias if desired | |
| # for chunk in client.chat.completions.create( | |
| # model="Qwen/Qwen2.5-Coder-32B-Instruct", | |
| # messages=messages, | |
| # max_tokens=max_tokens, | |
| # stream=True, | |
| # temperature=temperature, | |
| # top_p=top_p, | |
| # ): | |
| # # 3. Extract token content | |
| # token = chunk.choices[0].delta.content or "" | |
| # response += token | |
| # yield response | |
| # # 4. Wire up Gradio chat interface | |
| # demo = gr.ChatInterface(respond, type="messages") | |
| # if __name__ == "__main__": | |
| # demo.launch() | |
| # import gradio as gr | |
| # from huggingface_hub import InferenceClient | |
| # hf_token = "HF_TOKEN" | |
| # # Ensure token is available | |
| # if hf_token is None: | |
| # raise ValueError("HUGGINGFACEHUB_API_TOKEN is not set in .env file or environment.") | |
| # # Instantiate Hugging Face Inference Client with token | |
| # client = InferenceClient( | |
| # model="Qwen/Qwen2.5-Coder-32B-Instruct", | |
| # token=hf_token | |
| # ) | |
| # def respond(message, history: list[tuple[str, str]]): | |
| # system_message = ( | |
| # "You are a helpful and experienced coding assistant specialized in web development. " | |
| # "Help the user by generating complete and functional code for building websites. " | |
| # "You can provide HTML, CSS, JavaScript, and backend code (like Flask, Node.js, etc.) " | |
| # "based on their requirements." | |
| # ) | |
| # max_tokens = 2048 | |
| # temperature = 0.7 | |
| # top_p = 0.95 | |
| # # Build conversation history | |
| # 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}) | |
| # response = "" | |
| # # Stream the response from the model | |
| # for chunk in client.chat.completions.create( | |
| # model="Qwen/Qwen2.5-Coder-32B-Instruct", | |
| # messages=messages, | |
| # max_tokens=max_tokens, | |
| # stream=True, | |
| # temperature=temperature, | |
| # top_p=top_p, | |
| # ): | |
| # token = chunk.choices[0].delta.content or "" | |
| # response += token | |
| # yield response | |
| # # Gradio UI | |
| # demo = gr.ChatInterface(respond, type="messages") | |
| # if __name__ == "__main__": | |
| # demo.launch() | |
| # import gradio as gr | |
| # from transformers import AutoTokenizer, AutoModelForCausalLM | |
| # import torch | |
| # # Load once globally | |
| # tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Coder-32B-Instruct") | |
| # model = AutoModelForCausalLM.from_pretrained( | |
| # "Qwen/Qwen2.5-Coder-32B-Instruct", | |
| # device_map="auto", | |
| # torch_dtype=torch.float16, | |
| # ) | |
| # def respond(message, history): | |
| # system_prompt = ( | |
| # "You are a helpful coding assistant specialized in web development. " | |
| # "Provide complete code snippets for HTML, CSS, JS, Flask, Node.js etc." | |
| # ) | |
| # # Build input prompt including chat history | |
| # chat_history = "" | |
| # for user_msg, bot_msg in history: | |
| # chat_history += f"User: {user_msg}\nAssistant: {bot_msg}\n" | |
| # prompt = f"{system_prompt}\n{chat_history}User: {message}\nAssistant:" | |
| # inputs = tokenizer(prompt, return_tensors="pt").to(model.device) | |
| # outputs = model.generate( | |
| # **inputs, | |
| # max_new_tokens=512, | |
| # temperature=0.7, | |
| # do_sample=True, | |
| # top_p=0.95, | |
| # eos_token_id=tokenizer.eos_token_id, | |
| # ) | |
| # generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| # # Extract only the new response part after the prompt | |
| # response = generated_text[len(prompt):].strip() | |
| # # Append current Q/A to history | |
| # history.append((message, response)) | |
| # return "", history | |
| # demo = gr.ChatInterface(respond, type="messages") | |
| # if __name__ == "__main__": | |
| # demo.launch() | |
| import os | |
| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| from dotenv import load_dotenv | |
| # Load .env variables (make sure to have HF_TOKEN in .env or set as env var) | |
| load_dotenv() | |
| HF_TOKEN = os.getenv("HF_TOKEN") # or directly assign your token here as string | |
| # Initialize InferenceClient with Hugging Face API token | |
| client = InferenceClient( | |
| model="Qwen/Qwen2.5-Coder-32B-Instruct", | |
| token=HF_TOKEN | |
| ) | |
| def respond(message, history): | |
| """ | |
| Chat response generator function streaming from Hugging Face Inference API. | |
| """ | |
| system_message = ( | |
| "You are a helpful and experienced coding assistant specialized in web development. " | |
| "Help the user by generating complete and functional code for building websites. " | |
| "You can provide HTML, CSS, JavaScript, and backend code (like Flask, Node.js, etc.) " | |
| "based on their requirements." | |
| ) | |
| max_tokens = 2048 | |
| temperature = 0.7 | |
| top_p = 0.95 | |
| # Prepare messages in OpenAI chat format | |
| 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}) | |
| response = "" | |
| # Stream response tokens from Hugging Face Inference API | |
| for chunk in client.chat.completions.create( | |
| model="Qwen/Qwen2.5-Coder-32B-Instruct", | |
| messages=messages, | |
| max_tokens=max_tokens, | |
| stream=True, | |
| temperature=temperature, | |
| top_p=top_p, | |
| ): | |
| token = chunk.choices[0].delta.get("content", "") | |
| response += token | |
| yield response | |
| # Create Gradio chat interface | |
| demo = gr.ChatInterface(fn=respond, title="Coding Assistant", | |
| description="Ask for web development code help!") | |
| if __name__ == "__main__": | |
| demo.launch() | |