Update app.py

app.py

@@ -14,6 +14,13 @@ LABEL_MAP = {
     "LABEL_1": "Relevant"
 }
 
+# Global variable to store the filtered files per label
+classified_files = {
+    "Relevant": [],
+    "Irrelevant": []
+}
+
+
 def clean_resume_text(text):
     text = re.sub(r'http\S+', ' ', text)
     text = re.sub(r'#\S+', '', text)
@@ -22,6 +29,7 @@ def clean_resume_text(text):
     text = re.sub(r'[^\x00-\x7f]', ' ', text)
     return re.sub(r'\s+', ' ', text).strip()
 
+
 def extract_resume_text(file):
     try:
         reader = PyPDF2.PdfReader(file)
@@ -34,11 +42,12 @@ def extract_resume_text(file):
     except Exception as e:
         return None, f"Error reading PDF: {str(e)}"
 
-def filter_relevant_resumes(files):
+
+def classify_and_store(files):
     predictions = {}
-    relevant_files = []
+    classified_files["Relevant"] = []
+    classified_files["Irrelevant"] = []
 
-    # Create temp dir for filtered files
     if os.path.exists("filtered_resumes"):
         shutil.rmtree("filtered_resumes")
     os.makedirs("filtered_resumes", exist_ok=True)
@@ -61,29 +70,33 @@ def filter_relevant_resumes(files):
             "Confidence Score": score
         }
 
-        if status == "Relevant":
-            dest_path = f"filtered_resumes/{file_name}"
-            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)
+        dest_path = f"filtered_resumes/{file_name}"
+        with open(file.name, "rb") as f_in, open(dest_path, "wb") as f_out:
+            shutil.copyfileobj(f_in, f_out)
 
-    return predictions, relevant_files
+        classified_files[status].append(dest_path)
 
+    return predictions
+
+
+def get_resumes_by_category(category):
+    return classified_files.get(category, [])
 
 
 # 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.")
+with gr.Blocks(title="Resume Classifier & Category Filter") as demo:
+    gr.Markdown("## 📂 Resume Relevance Classifier\nUpload resumes and view based on relevance category.")
 
     file_input = gr.File(file_types=[".pdf"], file_count="multiple", label="Upload Resume PDFs")
-
-    with gr.Row():
-        classify_button = gr.Button("🧠 Classify and Filter Relevant Resumes")
+    classify_button = gr.Button("🧠 Classify Resumes")
 
     relevance_output = gr.JSON(label="Classification Results")
-    relevant_resume_gallery = gr.File(label="Download Relevant Resumes", file_types=[".pdf"], file_count="multiple")
 
-    classify_button.click(fn=filter_relevant_resumes, inputs=[file_input], outputs=[relevance_output, relevant_resume_gallery])
+    category_dropdown = gr.Dropdown(choices=["Relevant", "Irrelevant"], label="Select Resume Category to View")
+    filtered_files_output = gr.File(label="Filtered Resumes", file_types=[".pdf"], file_count="multiple")
+
+    classify_button.click(fn=classify_and_store, inputs=[file_input], outputs=[relevance_output])
+    category_dropdown.change(fn=get_resumes_by_category, inputs=[category_dropdown], outputs=[filtered_files_output])
 
 if __name__ == "__main__":
     demo.launch()