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# # 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()
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