app.py

import gradio as gr
import requests
import json
import os
from sentence_transformers import util

# Set up API endpoint and API Key
api_key = os.getenv("GOOGLE_API_KEY")  # Store your API Key in environment variables
api_url = "https://generativelanguage.googleapis.com/v1beta/models/gemini-1-2:embedText"  # Adjust as per your model version

headers = {
    "Authorization": f"Bearer {api_key}",
    "Content-Type": "application/json"
}

def get_gemini_embeddings(text):
    data = {
        "model": "gemini-1-2",  # Replace with the actual model you are using
        "text": text
    }

    # Send POST request to Gemini API
    response = requests.post(api_url, headers=headers, json=data)
    
    if response.status_code == 200:
        response_data = response.json()
        return response_data.get("embeddings", [])
    else:
        print(f"Error: {response.status_code} - {response.text}")
        return []

def extract_text_from_resume(resume_file):
    # Extract text from resume (you can use libraries like PyPDF2 or textract for PDFs)
    return "Sample resume text"

def extract_leadership_experience(resume_text):
    # Logic to extract leadership experience from resume text
    return "Leadership Experience Example"

def extract_entities_via_gemini(resume_text):
    # Logic to extract named entities (e.g., Name, Email, Contact) using Gemini API
    return {"name": "John Doe", "email": "john.doe@example.com", "contact": "123-456-7890"}

def save_results_to_csv(results):
    import csv
    csv_file_path = "/tmp/results.csv"
    with open(csv_file_path, mode='w', newline='') as file:
        writer = csv.writer(file)
        writer.writerow(["Resume Name", "Similarity Score (%)", "Eligibility", "Name", "Leadership Experience", "Email", "Contact"])
        for result in results:
            writer.writerow(result)
    return csv_file_path

def check_similarity(job_description, resume_files):
    results = []
    
    # Get embeddings for the job description using Gemini
    job_emb = get_gemini_embeddings(job_description)
    
    if not job_emb:
        return "Error in embedding job description using Gemini API."
    
    for resume_file in resume_files:
        resume_text = extract_text_from_resume(resume_file)
        if not resume_text:
            results.append((resume_file.name, 0, "Not Eligible", None, "No leadership experience", "No Email", "No Contact"))
            continue
        
        # Get embeddings for the resume using Gemini
        resume_emb = get_gemini_embeddings(resume_text)
        
        if not resume_emb:
            results.append((resume_file.name, 0, "Not Eligible", None, "No leadership experience", "No Email", "No Contact"))
            continue
        
        # Calculate similarity score between job description and resume
        similarity_score = util.pytorch_cos_sim(job_emb, resume_emb)[0][0].item()
        similarity_percentage = similarity_score * 100
        
        leadership_experience = extract_leadership_experience(resume_text)
        contact_info = extract_entities_via_gemini(resume_text)

        if similarity_score >= 0.50:
            candidate_name = contact_info.get('name', 'Unknown Candidate')
            results.append((
                resume_file.name, 
                similarity_percentage, 
                "Eligible", 
                candidate_name, 
                leadership_experience,
                contact_info.get('email', 'No Email'), 
                contact_info.get('contact', 'No Contact')
            ))
        else:
            results.append((
                resume_file.name, 
                similarity_percentage, 
                "Not Eligible", 
                None, 
                leadership_experience,
                contact_info.get('email', 'No Email'), 
                contact_info.get('contact', 'No Contact')
            ))

    csv_file_path = save_results_to_csv(results)
    return results, csv_file_path

def download_results(results):
    return save_results_to_csv(results)

# Gradio UI
with gr.Blocks() as demo:
    with gr.Row():
        job_desc_input = gr.Textbox(label="Job Description", lines=3)
        resume_input = gr.Files(label="Upload Resumes", file_count="multiple", file_types=[".pdf", ".txt"])

    results_output = gr.Dataframe(headers=["Resume Name", "Similarity Score (%)", "Eligibility", "Name", "Leadership Experience", "Email", "Contact"])

    check_button = gr.Button("Check Similarity")

    # Set up button's action
    check_button.click(
        check_similarity, 
        inputs=[job_desc_input, resume_input], 
        outputs=[results_output, gr.File(label="Download CSV", value=download_results)]
    )

demo.launch()