| # # 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("HuggingFaceH4/zephyr-7b-beta") | |
| # # 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=512, 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 torch | |
| # from transformers import AutoTokenizer, AutoModelForCausalLM | |
| # import gradio as gr | |
| # model_id = "Asit03/AI_Agent_V2_Merged" | |
| # # Load tokenizer | |
| # tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| # # Load model with 4-bit quantization | |
| # model = AutoModelForCausalLM.from_pretrained( | |
| # model_id, | |
| # device_map="auto", | |
| # load_in_4bit=True, | |
| # torch_dtype=torch.bfloat16, # fallback to torch.float16 if needed | |
| # trust_remote_code=True | |
| # ) | |
| # # Generation function | |
| # def chat(prompt): | |
| # inputs = tokenizer(prompt, return_tensors="pt").to(model.device) | |
| # outputs = model.generate(**inputs, max_new_tokens=200, pad_token_id=tokenizer.eos_token_id) | |
| # return tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| # # Launch Gradio app | |
| # gr.Interface(fn=chat, inputs="text", outputs="text", title="💬 AI Agent V2").launch() | |
| from huggingface_hub import InferenceClient | |
| import gradio as gr | |
| # Create inference client for your model | |
| client = InferenceClient("Asit03/AI_Agent_V2_Merged") # or private repo with token | |
| def chat(prompt): | |
| response = client.text_generation(prompt, max_new_tokens=1500) | |
| return response.strip() | |
| gr.Interface(fn=chat, inputs="text", outputs="text", title="💬 AI Agent via Inference API").launch() | |