app.py

import os
import gradio as gr
import tensorflow as tf
import spacy
from transformers import InferenceClient
from jinja2 import Environment, FileSystemLoader

# Load spaCy model
nlp = spacy.load("en_core_web_sm")

# Load your TensorFlow model
model = tf.keras.models.load_model("resume_generator_model.h5")

# Initialize the InferenceClient for LLaMA
llama_client = InferenceClient(model="meta-llama/Meta-Llama-3.1-8B-Instruct")

# Configure Jinja2 for template rendering
env = Environment(loader=FileSystemLoader("templates"))

# Helper function to enhance the resume content using spaCy and LLaMA
def enhance_resume(resume_text, job_title):
    # 1. Analyze the resume using spaCy
    doc = nlp(resume_text)
    entities = [(ent.text, ent.label_) for ent in doc.ents]
    
    # 2. Use LLaMA model to generate new content based on the job title
    prompt = f"Enhance the following resume text for a {job_title} position: {resume_text}"
    llama_response = llama_client(prompt)
    
    # 3. Use your custom model for additional predictions (e.g., key skills, professional summary)
    # Here, we simulate predictions based on your model's capabilities
    predictions = model.predict([[job_title]])  # Adjust this based on your model's input requirements
    enhanced_skills = ["Skill A", "Skill B", "Skill C"]  # Replace with model-predicted skills

    # 4. Populate the HTML template with the enhanced content
    template = env.get_template("resume_template.html")
    rendered_resume = template.render(
        name="John Doe",
        email="john.doe@example.com",
        phone="123-456-7890",
        location="San Francisco, CA",
        summary=llama_response["generated_text"],
        skills=enhanced_skills,
        experiences=["Experience 1", "Experience 2"],
        educations=["Bachelor's in Computer Science", "Master's in Data Science"]
    )

    return rendered_resume

# Define the Gradio interface
iface = gr.Interface(
    fn=enhance_resume,
    inputs=["text", "text"],  # Upload resume text and specify job title
    outputs="html",  # Rendered HTML output
    title="Resume Enhancer",
    description="Upload a resume and specify a job title to enhance it.",
)

# Launch the Gradio app
iface.launch()