Update app.py

app.py

@@ -15,9 +15,11 @@ classifier = pipeline("zero-shot-classification", model="Fralet/personality")
 
 # Streamlit interface setup
 st.title("Resume-based Personality Prediction by Serikov Ayanbek")
+resume_text = st.text_area("Enter Resume Text Here", height=300)
 
 # Load data from Excel
 data = pd.read_excel("ResponseTest.xlsx")  # Replace 'ResponseTest.xlsx' with your actual file name
+data_open = pd.read_excel("ResponseOpen.xlsx")  # Replace 'ResponseTest.xlsx' with your actual file name
 
 # Preprocess text function
 def preprocess_text(text):
@@ -34,17 +36,18 @@ def preprocess_text(text):
 
 # Combine relevant text columns for processing
 question_columns = [f'Q{i}' for i in range(1, 37)]  # Adjust range if needed
-data['combined_text'] = data[['CV/Resume'] + question_columns].agg(' '.join, axis=1)
-data['processed_text'] = data['combined_text'].apply(preprocess_text)
+data['processed_text'] = data[['CV/Resume'] + question_columns].agg(' '.join, axis=1)
+data_open['processed_text_open'] = data_open[['CV/Resume'] + ['Question']]
+
 labels = ["Peacemaker", "Loyalist", "Achiever", "Reformer", "Individualist", "Helper", "Challenger", "Investigator", "Enthusiast"]
 
 # Prediction confidence threshold
 confidence_threshold = st.slider("Confidence Threshold", 0.0, 1.0, 0.5)
 
-if st.button("Predict Personality"):
+if st.button("Predict Personality by Test"):
     # Function to apply predictions using dynamic labels from MAX1, MAX2, MAX3 and only return the highest scored label
     def get_predictions(row):
-        custom_labels = [row['MAX1'], row['MAX2'], row['MAX3']]  # Get labels from each row
+        #custom_labels = [row['MAX1'], row['MAX2'], row['MAX3']]  # Get labels from each row
         processed_text = row['processed_text']
         result = classifier(processed_text, labels)
         highest_score_label = result['labels'][0]  # Assumes the labels are sorted by score, highest first
@@ -53,3 +56,14 @@ if st.button("Predict Personality"):
     # Apply predictions across all rows
     data['Predicted'] = data.apply(get_predictions, axis=1)
     st.dataframe(data[['True_label','MAX1','MAX2','MAX3', 'Predicted']])
+
+if st.button("Predict Personality by Open Question"):
+    def get_predictions(row):
+        processed_text = row['processed_text_open']
+        result = classifier(processed_text, labels)
+        highest_score_label = result['labels'][0]  # Assumes the labels are sorted by score, highest first
+        return highest_score_label
+
+    # Apply predictions across all rows
+    data_open['Predicted'] = data_open.apply(get_predictions, axis=1)
+    st.dataframe(data_open[['True_label', 'Predicted']])