import gradio as gr

gr.load("models/microsoft/Phi-3.5-mini-instruct",  max_batch_size=1000).launch(share=True)




# def generate_responce(user_input):
#     gr.load("models/microsoft/Phi-3.5-mini-instruct")

#     inputs = tokenize(user_input, return_tensor="pt")
#     outputs = 


# gradio_app = gr.Interface(
#     fn=generate_responce,
#     inputs="text",
#     outputs="text",
#     max_batch_size=50,
#     title="Advertisment companion",
# )



# from transformers import AutoTokenizer, AutoModelForCausalLM
# import torch

# # Load the model and tokenizer
# tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3.5-mini-instruct", trust_remote_code=True)
# model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3.5-mini-instruct", trust_remote_code=True)

# # Define the role prompt for advertisement assistance
# # role_prompt = "You are an advertisement assistant. Respond professionally and helpfully to advertising-related questions.\n\n"

# # Function to generate responses
# def generate_response(user_input):
#     # Prepend role information to user input
#     # input_text =  user_input
    
#     # Tokenize and generate response
#     inputs = tokenizer(user_input, return_tensors="pt")
#     outputs = model.generate(
#         **inputs, 
#         max_new_tokens=100,   # Increase this if you want longer responses
#                  # Nucleus sampling to control randomness
#     )
    
#     # Decode and return the response
#     response = tokenizer.batch_decode(outputs, skip_special_tokens=True)
#     return response

# # Set up Gradio interface
# interface = gr.Interface(
#     fn=generate_response,
#     inputs="text",
#     outputs="text",
#     title="Advertisement Assistant Chatbot",
#     description="Ask me anything related to advertising. I'm here to help!"
# )

# # Launch the Gradio app with sharing enabled
# interface.launch(share=True)


# import gradio as gr
# from transformers import pipeline

# # Load the model pipeline for text generation
# generator = pipeline("text-generation", model="microsoft/Phi-3.5-mini-instruct")

# # Define the role prompt for advertisement assistance
# role_prompt = "You are an advertisement assistant. Respond professionally and helpfully to advertising-related questions.\n\n"

# # Function to generate responses
# def generate_response(user_input):
#     input_text = role_prompt + user_input
#     response = generator(input_text, max_new_tokens=50, temperature=0.7, top_p=0.9)
#     return response[0]["generated_text"]

# # Set up Gradio interface
# interface = gr.Interface(
#     fn=generate_response,
#     inputs="text",
#     outputs="text",
#     title="Advertisement Assistant Chatbot",
#     description="Ask me anything related to advertising. I'm here to help!"
# )

# # Launch the Gradio app with sharing enabled
# interface.launch(share=True)


# import gradio as gr

# # Load the model using gr.load()
# model_interface = gr.load("models/microsoft/Phi-3.5-mini-instruct")

# # Create a wrapper interface to customize the appearance
# interface = gr.Interface(
#     fn=model_interface,
#     inputs="text",
#     outputs="text",
#     title="Advertisement Assistant Chatbot",
#     description="Ask me anything related to advertising. I'm here to help! This assistant provides professional guidance on advertising queries.",
#     theme="default",  # Optional: Choose a theme or style
# )

# # Launch with sharing enabled
# interface.launch(share=True)





# import gradio as gr
# from transformers import pipeline
# huggingface-cli login

# text_generator = pipeline("text-generation", model="meta-llama/Llama-3.2-1B")

# def predict(input_text):
#     predictions = text_generator(input_text, max_new_tokens=50, num_return_sequences=1)
#     return predictions[0]["generated_text"]

# gradio_app = gr.Interface(
#     predict,
#     inputs=gr.Textbox(label="Enter text for generation"),
#     outputs=gr.Textbox(label="Generated Text"),
#     title="Text Generation Model",
#     description="This app generates text based on input prompts."
# )

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