test.py

# import gradio as gr # type: ignore
# from huggingface_hub import InferenceClient # type: ignore

# """
# 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()



# ###########################

# # app.py
# import gradio as gr # type: ignore
# import os

# # import openai # type: ignore
# # # openai.api_key = os.getenv("OPENAI_API_KEY")
# # client = openai.OpenAI()
# # def respond(
# #             message, 
# #             history: list[tuple[str, str]], 
# #             system_message, 
# #             max_tokens, 
# #             temperature, 
# #             top_p, 
# #             image_uploaded,
# #             file_uploaded
# #     ):
    
# #     #read system message
# #     messages = [{"role": "system", "content": system_message}]
    
# #     #read history
# #     for val in history:
# #         if val[0]:
# #             messages.append({"role": "user", "content": val[0]})
# #         if val[1]:
# #             messages.append({"role": "assistant", "content": val[1]})
    
# #     #read output
# #     messages.append({"role": "user", "content": message})
# #     print("## Messages: \n", messages) #debug output

# #     #create output
# #     response = client.responses.create(
# #         model="gpt-4.1-nano", 
# #         input=messages,
# #         temperature=temperature,
# #         top_p=top_p,
# #         max_output_tokens=max_tokens
# #     )

# #     #read output
# #     response = response.output_text
# #     print("## Response: ", response) #debug output
# #     print("\n")
# #     yield response #chat reply

# # import torch
# # from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
# # model_name = "deepseek-ai/deepseek-math-7b-base"
# # tokenizer = AutoTokenizer.from_pretrained(model_name)
# # model = AutoModelForCausalLM.from_pretrained(model_name)
# # # model.generation_config = GenerationConfig.from_pretrained(model_name)
# # # model.generation_config.pad_token_id = model.generation_config.eos_token_id
# # def deepseek(
# #             message, 
# #             history: list[tuple[str, str]], 
# #             system_message, 
# #             max_tokens, 
# #             temperature, 
# #             top_p):
    
    
# #     # messages = [
# #     #     {"role": "user", "content": "what is the integral of x^2 from 0 to 2?\nPlease reason step by step, and put your final answer within \\boxed{}."}
# #     # ]
# #     messages = [
# #         {"role": "user", "content": message}
# #     ]

# #     input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
# #     outputs = model.generate(input_tensor.to(model.device), max_new_tokens=100)
# #     print(outputs)
# #     print("\n")
# #     result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)
# #     print(result)
# #     return result

# # import replicate
# # def deepseek_api_replicate(
# #             user_message, 
# #             history: list[tuple[str, str]], 
# #             system_message, 
# #             max_new_tokens, 
# #             temperature, 
# #             top_p):
# #     """
# #     Gọi DeepSeek Math trên Replicate và trả ngay kết quả.

# #     Trả về:
# #         str hoặc [bytes]: output model sinh ra
# #     """
# #     # 1. Khởi tạo client và xác thực
# #     # token = os.getenv("REPLICATE_API_TOKEN")
# #     # if not token:
# #     #     raise RuntimeError("Missing REPLICATE_API_TOKEN")  # bảo mật bằng biến môi trường
# #     client = replicate.Client(api_token="REPLICATE_API_TOKEN")          

# #     # 2. Gọi model
# #     output = client.run(
# #         "deepseek-ai/deepseek-math-7b-base:61f572dae0985541cdaeb4a114fd5d2d16cb40dac3894da10558992fc60547c7",
# #         input={
# #             "system_prompt": system_message,
# #             "user_prompt":   user_message,
# #             "max_new_tokens": max_new_tokens,
# #             "temperature":    temperature,
# #             "top_p":          top_p
# #         }
# #     )

# #     # 3. Trả kết quả
# #     return output

# import call_api


# chat = gr.ChatInterface(
#     call_api.respond, #chat
#     title="Trợ lý Học Tập AI",
#     description="Nhập câu hỏi của bạn về Toán, Lý, Hóa, Văn… và nhận giải đáp chi tiết ngay lập tức!",
#     additional_inputs=[
#         gr.Textbox("Bạn là một chatbot tiếng Việt thân thiện.", label="System message"),
#         gr.Slider(1, 2048, value=200, step=1, label="Max new tokens"),
#         gr.Slider(0.1, 4.0, value=0.7, step=0.1, label="Temperature"),
#         gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
#         # gr.Image(type="pil", label="Attach an image (optional)"),
#         # gr.File(label="Upload a file (optional)"),
#     ],
#     examples=[
#         # Mỗi item: [message, system_message, max_tokens, temperature, top_p]
#         ["tích phân của x^2 từ 0 đến 2 là gì? vui lòng lập luận từng bước, và đặt kết quả cuối cùng trong \boxed{}", "bạn là nhà toán học", 100, 0.7, 0.95],
#     ],
# )

# if __name__ == "__main__":
#     chat.launch()