Update app.py

app.py

@@ -8,10 +8,9 @@ import uuid
 from datetime import datetime
 from docx import Document
 import tempfile
-print("NotLoaded")
+
 # Load model and vectorizer
 classifier_model = joblib.load('resume_classifier')
-print("NotLoaded")
 resume_vectorizer = joblib.load('resume_vectorizer')
 
 
@@ -41,7 +40,6 @@ def read_file(file_path):
             except Exception as e:
                 return f"Error reading Word file with textract: {str(e)}"
 
-
         else:
             return "Unsupported file type."
 
@@ -54,7 +52,7 @@ def clean_resume(text):
 
 
 def log_or_update(serial_id, timestamp, resume_text, model_prediction, corrected_prediction):
-    log_file = "corrections_log.csv"
+    log_file = "/tmp/corrections_log.csv"
     resume_text_short = resume_text[:500]  # Truncate for privacy/log size
 
     new_row = {
@@ -85,33 +83,20 @@ uploaded_file = st.file_uploader(
     type=["pdf", "txt", "doc", "docx"]
 )
 
-print("Somethinguploaded")
-
 if uploaded_file:
-    # Ensure /tmp directory exists
-    upload_dir = "/tmp/uploads"
-    os.makedirs(upload_dir, exist_ok=True)
-
-    # Save the uploaded file to /tmp
-    file_path = os.path.join(upload_dir, uploaded_file.name)
-    with open(file_path, "wb") as f:
-        f.write(uploaded_file.getbuffer())
+    # Save uploaded file to a temp file in /tmp
+    with tempfile.NamedTemporaryFile(delete=False, dir="/tmp", suffix=os.path.splitext(uploaded_file.name)[1]) as temp_file:
+        temp_file.write(uploaded_file.read())
+        temp_path = temp_file.name
 
-    # Handle session state tracking
+    # Track upload session
     if (
         "uploaded_file_name" not in st.session_state
         or st.session_state.uploaded_file_name != uploaded_file.name
     ):
         st.session_state.uploaded_file_name = uploaded_file.name
         st.session_state.serial_id = str(uuid.uuid4())
-        st.session_state.corrected_prediction = None  # Reset correction state
-
-    st.success(f"File uploaded and saved to: {file_path}")
-    
-    # Save uploaded file to temp and extract text (same as your code)
-    with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(uploaded_file.name)[1]) as temp_file:
-        temp_file.write(uploaded_file.read())
-        temp_path = temp_file.name
+        st.session_state.corrected_prediction = None
 
     extracted_text = read_file(temp_path)
     os.remove(temp_path)
@@ -130,15 +115,15 @@ if uploaded_file:
         corrected_prediction = prediction
 
         if feedback == "No":
-            # Use session state to keep corrected prediction during session
-            corrected_prediction = st.text_input("Please provide the correct role:", 
-                                                 value=st.session_state.get("corrected_prediction", ""), 
-                                                 key="correction_input")
+            corrected_prediction = st.text_input(
+                "Please provide the correct role:",
+                value=st.session_state.get("corrected_prediction", ""),
+                key="correction_input"
+            )
             st.session_state.corrected_prediction = corrected_prediction
         else:
             st.session_state.corrected_prediction = prediction
 
-        # Log/update only if user made a choice (Yes or No + correction if No)
         if (feedback == "Yes") or (feedback == "No" and corrected_prediction):
             now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
             log_or_update(