Update app.py

app.py

@@ -1,9 +1,10 @@
 # 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  # ✅ FIXED: was 'get_summary'
+from groq_api import summarize_match
 
 # Global storage
 cv_texts = []
@@ -19,16 +20,15 @@ def upload_cvs(files):
         cv_names = [f.name for f in files]
         cv_vectors = get_embeddings(cv_texts)
 
-        if cv_vectors is None or len(cv_vectors) == 0:
+        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
 
@@ -42,16 +42,16 @@ def match_jd(jd_text):
         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]
+        # Get readable file names and snippets of CVs
+        matched_names = [os.path.basename(cv_names[i]) for i in top_k_indices]
+        matched_texts = [cv_texts[i][:500] for i in top_k_indices]  # limit for Groq prompt
 
-        summary = summarize_match(jd_text, matched_names)  # ✅ this is the fix!
+        summary = summarize_match(jd_text, matched_names, matched_texts)
         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