app.py

# app.py

import os
import gradio as gr
from text_extractor import extract_text_from_file
from embedder import get_embeddings
from vector_store import create_faiss_index, search_similar_cvs
from groq_api import summarize_match

# Global state
cv_texts = []
cv_names = []
cv_vectors = []
faiss_index = None

def upload_cvs(files):
    global cv_texts, cv_names, cv_vectors, faiss_index
    try:
        cv_texts = [extract_text_from_file(f.name) for f in files]
        cv_names = [f.name for f in files]
        cv_vectors = get_embeddings(cv_texts)

        import numpy as np
        if cv_vectors is None or np.array(cv_vectors).size == 0:
            return "❌ No valid CVs extracted or embedded."

        faiss_index = create_faiss_index(cv_vectors)
        return f"✅ Uploaded and indexed {len(files)} CVs."
    except Exception as e:
        return f"❌ Error during upload: {e}"

def match_jd(jd_text):
    global faiss_index
    try:
        if not faiss_index:
            return "❌ Please upload CVs first."
        if not jd_text.strip():
            return "❌ Job description is empty."

        jd_vector = get_embeddings([jd_text])[0]
        top_k_indices = search_similar_cvs(jd_vector, faiss_index, k=3)

        import os
        matched_names = [os.path.basename(cv_names[i]) for i in top_k_indices]
        matched_texts = [
            cv_texts[i][:500] if cv_texts[i].strip() else "[No CV content]"
            for i in top_k_indices
        ]

        summary = summarize_match(jd_text, matched_names, matched_texts)
        return f"""
✅ <span style='color:#16a34a; font-weight:bold;'>Top Matches:</span><br>{matched_names}<br><br>
📝 <span style='color:#3b82f6; font-weight:bold;'>Summary:</span><br>{summary}
"""
    except Exception as e:
        return f"<span style='color:red;'>❌ Error during matching: {e}</span>"

def clear_data():
    global cv_texts, cv_names, cv_vectors, faiss_index
    cv_texts, cv_names, cv_vectors, faiss_index = [], [], [], None
    return "🧹 All data cleared. You can now start fresh."

# ======================
# TABBED PROFESSIONAL UI
# ======================
with gr.Blocks(css="""
    .gr-button { background-color: #2563eb; color: white; font-weight: bold; }
    .gr-button:hover { background-color: #1d4ed8; }
    textarea, input[type='file'] { border: 2px solid #3b82f6 !important; }
    .gr-textbox label { color: #111827; font-weight: 600; }
""") as upload_tab:
    gr.Markdown("## 📤 Upload CVs")
    gr.Markdown("Upload candidate CVs in PDF or DOCX format.")
    cv_upload = gr.File(label="Upload CVs", file_types=[".pdf", ".docx"], file_count="multiple")
    upload_button = gr.Button("📁 Upload & Index CVs")
    upload_status = gr.Textbox(label="Status", interactive=False)
    upload_button.click(upload_cvs, inputs=[cv_upload], outputs=[upload_status])

with gr.Blocks() as match_tab:
    gr.Markdown("## 📋 Match Job Description to CVs")
    jd_input = gr.Textbox(label="Paste Job Description", lines=8, placeholder="e.g. Looking for a Python Data Analyst...")
    match_button = gr.Button("🔍 Match CVs")
    match_result = gr.HTML()
    match_button.click(match_jd, inputs=[jd_input], outputs=[match_result])

with gr.Blocks() as reset_tab:
    gr.Markdown("## 🧹 Clear All Data")
    gr.Markdown("Click below to reset the app and upload new CVs.")
    clear_button = gr.Button("Reset App")
    clear_output = gr.Textbox(label="Reset Status", interactive=False)
    clear_button.click(clear_data, inputs=[], outputs=[clear_output])

# Menu Bar Style Tabs
app = gr.TabbedInterface(
    interface_list=[upload_tab, match_tab, reset_tab],
    tab_names=["📤 Upload CVs", "📋 Match JD", "🧹 Reset"]
)

if __name__ == "__main__":
    app.launch()