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# app.py
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 get_summary
# Global storage
cv_texts = []
cv_names = []
cv_vectors = []
faiss_index = None
def upload_cvs(files):
global cv_texts, cv_names, cv_vectors, faiss_index
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)
faiss_index = create_faiss_index(cv_vectors)
return f"Uploaded and indexed {len(files)} CVs."
def match_jd(jd_text):
if not faiss_index:
return "Please upload CVs first."
jd_vector = get_embeddings([jd_text])[0]
top_k_indices = search_similar_cvs(jd_vector, faiss_index, k=3)
matched_names = [cv_names[i] for i in top_k_indices]
matched_texts = [cv_texts[i] for i in top_k_indices]
summary = get_summary(jd_text, matched_texts)
return f"Top Matches: {matched_names}\n\nSummary: {summary}"
def clear_data():
global cv_texts, cv_names, cv_vectors, faiss_index
cv_texts, cv_names, cv_vectors, faiss_index = [], [], [], None
return "Data cleared."
iface = gr.Interface(
fn=match_jd,
inputs=[
gr.Textbox(lines=10, label="Paste Job Description"),
],
outputs="text",
title="CV Matcher with Groq",
description="Upload CVs, enter a Job Description, and get top matches and summary."
)
upload = gr.Interface(
fn=upload_cvs,
inputs=gr.File(file_types=[".pdf", ".docx"], file_count="multiple"),
outputs="text",
title="Upload CVs"
)
clear = gr.Interface(fn=clear_data, inputs=[], outputs="text", title="Reset Data")
app = gr.TabbedInterface([upload, iface, clear], ["Upload CVs", "Match JD", "Clear Data"])
if __name__ == "__main__":
app.launch()
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