Update app.py

app.py

@@ -10,15 +10,15 @@ model = SentenceTransformer("all-MiniLM-L6-v2")
 
 
 # ---- Text extraction ----
-def extract_text(file_bytes, filename):
+def extract_text(file_path, filename):
     if filename.endswith(".pdf"):
         text = ""
-        with fitz.open(stream=file_bytes, filetype="pdf") as doc:
+        with fitz.open(file_path) as doc:
             for page in doc:
                 text += page.get_text("text") + "\n"
         return text
     elif filename.endswith(".docx"):
-        docf = docx.Document(file_bytes)
+        docf = docx.Document(file_path)
         return "\n".join(p.text for p in docf.paragraphs)
     return ""
 
@@ -38,12 +38,11 @@ def rank_cvs(job_description, files):
     job_emb = get_embedding(job_description)
     scores, names = [], []
 
-    for f in files:
-        # Extract filename and file bytes
-        filename = f.name
-        file_bytes = f.read()
+    for file_path in files:
+        # Get filename from file path
+        filename = os.path.basename(file_path)
         
-        text = extract_text(file_bytes, filename)
+        text = extract_text(file_path, filename)
         if not text.strip():
             continue
         cv_emb = get_embedding(text[:4000])  # limit text length
@@ -64,7 +63,7 @@ demo = gr.Interface(
     fn=rank_cvs,
     inputs=[
         gr.Textbox(label="💼 Job Description", lines=5),
-        gr.File(label="📁 Upload CVs (PDF/DOCX)", file_count="multiple", type="file"),
+        gr.File(label="📁 Upload CVs (PDF/DOCX)", file_count="multiple", type="filepath"),
     ],
     outputs=gr.Markdown(),
     title="📄 AI CV Ranker (Local Model)",