app.py

import gradio as gr
from transformers import pipeline

# Load your text-generation model from HF
# (change "YourUsername/YourModel" to whichever model you want to use)
generator = pipeline("text-generation", model="YourUsername/DeepSeek-R1")

def generate_cv(name, education, experience):
    # Build a "prompt" for your model
    prompt = (
        f"Generate a CV based on these details:\n"
        f"Name: {name}\n"
        f"Education: {education}\n"
        f"Experience: {experience}\n"
        "CV:\n"
    )

    # Call the pipeline to generate text (you can tweak max_length, etc.)
    outputs = generator(prompt, max_length=300)
    cv_text = outputs[0]["generated_text"]
    return cv_text

# Create a Gradio interface with 3 textboxes as input
demo = gr.Interface(
    fn=generate_cv, 
    inputs=["text", "text", "text"],  # or gr.Textbox(…), etc.
    outputs="text",
    title="Automated CV Generator"
)

demo.launch()