Update app.py

app.py

@@ -44,7 +44,7 @@ def filter_relevant_resumes(files):
     os.makedirs("filtered_resumes", exist_ok=True)
 
     for file in files:
-        file_name = file.name.split("/")[-1]
+        file_name = os.path.basename(file.name)
         resume_text, error = extract_resume_text(file)
         if error:
             predictions[file_name] = {"error": error}
@@ -54,8 +54,8 @@ def filter_relevant_resumes(files):
         result = text_classifier(cleaned_text[:512])
         label = result[0]['label']
         score = round(result[0]['score'], 4)
-
         status = LABEL_MAP.get(label, "Unknown")
+
         predictions[file_name] = {
             "Relevance": status,
             "Confidence Score": score
@@ -63,13 +63,14 @@ def filter_relevant_resumes(files):
 
         if status == "Relevant":
             dest_path = f"filtered_resumes/{file_name}"
-            with open(dest_path, "wb") as f_out:
-                f_out.write(file.data)
+            with open(file.name, "rb") as f_in, open(dest_path, "wb") as f_out:
+                shutil.copyfileobj(f_in, f_out)
             relevant_files.append(dest_path)
 
     return predictions, relevant_files
 
 
+
 # Gradio UI
 with gr.Blocks(title="Resume Relevance Classifier & Filter") as demo:
     gr.Markdown("## 📂 Resume Relevance Filter using Hugging Face Model\nUpload PDF resumes and filter out only the relevant ones.")