# 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="deepseek-ai/DeepSeek-R1-0528-Qwen3-8B", # 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="deepseek-ai/DeepSeek-R1-0528-Qwen3-8B", # 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="Website Building Assistant") # if __name__ == "__main__": # demo.launch() # import os # import gradio as gr # from huggingface_hub import InferenceClient # from dotenv import load_dotenv # # Load environment variables # load_dotenv() # HF_TOKEN = os.getenv("HF_TOKEN") # Ensure this is set in .env # # Initialize Hugging Face Inference Client # client = InferenceClient( # model="deepseek-ai/DeepSeek-R1-0528-Qwen3-8B", # token=HF_TOKEN # ) # # Define system instructions for the chatbot # 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." # ) # # Define the response generation function # def respond(message, history): # max_tokens = 2048 # temperature = 0.7 # top_p = 0.95 # # Convert chat history into OpenAI-style format # messages = [{"role": "system", "content": system_message}] # for item in history: # role = item["role"] # content = item["content"] # messages.append({"role": role, "content": content}) # # Add the latest user message # messages.append({"role": "user", "content": message}) # response = "" # # Streaming response from the Hugging Face Inference API # for chunk in client.chat.completions.create( # model="deepseek-ai/DeepSeek-R1-0528-Qwen3-8B", # messages=messages, # max_tokens=max_tokens, # stream=True, # temperature=temperature, # top_p=top_p, # ): # token = chunk.choices[0].delta.get("content") # if token is not None: # response += token # yield response # # Create Gradio Chat Interface # demo = gr.ChatInterface( # fn=respond, # title="Website Building Assistant", # chatbot=gr.Chatbot(show_label=False), # type="openai", # Use OpenAI-style message format # ) # if __name__ == "__main__": # demo.launch()# app.py # app.py # app.py # import os # import gradio as gr # from huggingface_hub import InferenceClient # from dotenv import load_dotenv # # Load environment variables # load_dotenv() # HF_TOKEN = os.getenv("HF_TOKEN") # # Initialize Hugging Face Inference Client # client = InferenceClient( # model="mistralai/Codestral-22B-v0.1", # token=HF_TOKEN # ) # # System prompt for coding assistant # 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." # ) # # Streaming chatbot logic using chat.completions # def respond(message, history): # # Prepare messages with system prompt # messages = [{"role": "system", "content": system_message}] # for msg in history: # messages.append(msg) # messages.append({"role": "user", "content": message}) # # Stream response from the model # response = "" # for chunk in client.chat.completions.create( # model="mistralai/Codestral-22B-v0.1", # messages=messages, # max_tokens=1024, # temperature=0.7, # top_p=0.95, # stream=True, # ): # token = chunk.choices[0].delta.get("content", "") or "" # response += token # yield response # # Create Gradio interface # with gr.Blocks() as demo: # chatbot = gr.Chatbot(type='messages') # Use modern message format # gr.ChatInterface(fn=respond, chatbot=chatbot, type="messages") # Match format # # Launch app # if __name__ == "__main__": # demo.launch() # app.py import os import gradio as gr from huggingface_hub import InferenceClient from dotenv import load_dotenv # Load environment variables load_dotenv() HF_TOKEN = os.getenv("HF_TOKEN") # Initialize Hugging Face Inference Client client = InferenceClient( model="mistralai/Mistral-7B-Instruct-v0.3", token=HF_TOKEN ) # System prompt for coding assistant 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." ) # Streaming chatbot logic def respond(message, history): # Prepare messages with system prompt messages = [{"role": "system", "content": system_message}] for msg in history: messages.append(msg) messages.append({"role": "user", "content": message}) # Stream response from the model response = "" for chunk in client.chat.completions.create( model="mistralai/Mistral-7B-Instruct-v0.3", messages=messages, max_tokens=1024, temperature=0.7, top_p=0.95, stream=True, ): token = chunk.choices[0].delta.get("content", "") or "" response += token yield response # Create Gradio interface with gr.Blocks() as demo: chatbot = gr.Chatbot(type='messages') # Use modern message format gr.ChatInterface(fn=respond, chatbot=chatbot, type="messages") # Match format # Launch app if __name__ == "__main__": demo.launch()