Update app.py

app.py

@@ -0,0 +1,68 @@
+# 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()