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import gradio as gr
#from huggingface_hub import InferenceClient
from llama_cpp import Llama
# Load the Phi-3 model
llm = Llama(
model_path="./Phi-3-mini-4k-instruct-q4.gguf", # Ensure this file is uploaded in your Space
n_ctx=4096, # Set the context window
n_threads=2, # Adjust based on available CPU cores
n_gpu_layers=35, # Use GPU acceleration if available
)
# Hugging Face Inference API client
#api_url = "https://api-inference.huggingface.co/models/microsoft/Phi-3-mini-4k-instruct-gguf"
#client = InferenceClient(api_url)
def generate_letter(date, time, purpose, place, sender, receiver):
prompt = (f"Write a formal letter with the following details:\n"
f"Date: {date}\nTime: {time}\nPurpose: {purpose}\nPlace: {place}\n"
f"Sender: {sender}\nReceiver: {receiver}\n\nLetter:")
#response = client.text_generation(prompt, max_new_tokens=100)
#return response
"""Generates a response using Phi-3 model."""
formatted_prompt = f"<|user|>\n{prompt}<|end|>\n<|assistant|>"
output = llm(
formatted_prompt,
max_tokens=256,
stop=["<|end|>"],
echo=False
)
return output['choices'][0]['text']
# Create the Gradio interface
iface = gr.Interface(
fn=generate_letter,
inputs=[
gr.Textbox(label="Date"),
gr.Textbox(label="Time"),
gr.Textbox(label="Purpose"),
gr.Textbox(label="Place"),
gr.Textbox(label="Sender"),
gr.Textbox(label="Receiver"),
],
outputs=gr.Textbox(label="Generated Letter"),
title="Letter Generator",
description="Enter the details and generate a formal letter automatically."
)
# Launch the app
if __name__ == "__main__":
iface.launch()
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