Update app.py

app.py

@@ -3,8 +3,7 @@ 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
-
+from groq_api import summarize_match  # ✅ FIXED: was 'get_summary'
 
 # Global storage
 cv_texts = []
@@ -12,43 +11,54 @@ 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."
 
+    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)
+        
+        if not cv_vectors or len(cv_vectors) == 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):
-    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)
+    global faiss_index
 
-    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)
+    try:
+        if not faiss_index:
+            return "❌ Please upload CVs first."
 
-    return f"Top Matches: {matched_names}\n\nSummary: {summary}"
+        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)
+
+        matched_names = [cv_names[i] for i in top_k_indices]
+        matched_texts = [cv_texts[i] for i in top_k_indices]
+
+        summary = summarize_match(jd_text, matched_names)
+        return f"✅ Top Matches:\n{matched_names}\n\n📝 Summary:\n{summary}"
+
+    except Exception as e:
+        return f"❌ Error during matching: {e}"
 
 def clear_data():
     global cv_texts, cv_names, cv_vectors, faiss_index
     cv_texts, cv_names, cv_vectors, faiss_index = [], [], [], None
-    return "Data cleared."
-
+    return "🧹 Data cleared."
 
+# Gradio Interfaces
 iface = gr.Interface(
     fn=match_jd,
-    inputs=[
-        gr.Textbox(lines=10, label="Paste Job Description"),
-    ],
+    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."