Update app.py

app.py

@@ -18,10 +18,10 @@ 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
+data = pd.read_excel("ResponseTest.xlsx")
+data_open = pd.read_excel("ResponseOpen.xlsx")
 
-# Preprocess text function
+# Define preprocessing function
 def preprocess_text(text):
     text = re.sub(r'\W', ' ', str(text))
     text = text.lower()
@@ -34,54 +34,31 @@ def preprocess_text(text):
     tokens = [lemmatizer.lemmatize(word) for word in tokens if word not in stop_words]
     return ' '.join(tokens)
 
-# Combine relevant text columns for processing
-question_columns = [f'Q{i}' for i in range(1, 37)]  # Adjust the range based on your data columns
-data['processed_text'] = data[['CV/Resume'] + question_columns].agg(lambda x: ', '.join(x), axis=1)
-#data['processed_text'] = data[['CV/Resume'] + question_columns].agg(lambda x: ', '.join(x), axis=1).apply(preprocess_text)
-#data_open['processed_text_open'] = data_open[['CV/Resume', 'Question']].agg(' '.join, axis=1).apply(preprocess_text)
-data_open['processed_text_open'] = data_open[['Demo_F', 'Question']].agg(' '.join, axis=1)
-data_open['processed_text_mopen'] = data_open[['Demo_M', 'Question']].agg(' '.join, axis=1)
+# Prepare the data for prediction
+data['processed_text'] = data[['CV/Resume'] + [f'Q{i}' for i in range(1, 37)]].agg(lambda x: ', '.join(x), axis=1).apply(preprocess_text)
+data_open['processed_text_open'] = data_open[['Demo_F', 'Question']].agg(' '.join, axis=1).apply(preprocess_text)
+data_open['processed_text_mopen'] = data_open[['Demo_M', 'Question']].agg(' '.join, axis=1).apply(preprocess_text)
 
 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 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
-        processed_text = row['processed_text']
-        result = classifier(processed_text, custom_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['Predicted'] = data.apply(get_predictions, axis=1)
-    st.dataframe(data[['True_label','MAX1','MAX2','MAX3', 'Predicted']])
-
-if st.button("Predict Personality by Open Question F"):
-    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_F'] = data_open.apply(get_predictions, axis=1)
-    st.dataframe(data_open[['True_label', 'Predicted_F']])
+# Automatic prediction on resume text input
+if resume_text:
+    processed_resume = preprocess_text(resume_text)
+    resume_prediction = classifier(processed_resume, labels)
+    highest_score_label = resume_prediction['labels'][0]
+    st.write("Predicted Personality for the given resume:", highest_score_label)
 
-if st.button("Predict Personality by Open Question M"):
-    
-        
-    def get_predictionsM(row):
-        processed_text = row['processed_text_mopen']
-        result = classifier(processed_text, labels)
-        highest_score_label = result['labels'][0]  # Assumes the labels are sorted by score, highest first
-        return highest_score_label
+# Automatic prediction for each row in DataFrame
+for index, row in data.iterrows():
+    result = classifier(row['processed_text'], labels)
+    data.at[index, 'Predicted'] = result['labels'][0]
+st.dataframe(data[['True_label', 'Predicted']])
 
-    # Apply predictions across all rows
-    data_open['Predicted_M'] = data_open.apply(get_predictionsM, axis=1)
-    st.dataframe(data_open[['True_label', 'Predicted_M']])
+# Separate predictions for Female and Male questions
+for index, row in data_open.iterrows():
+    result_f = classifier(row['processed_text_open'], labels)
+    result_m = classifier(row['processed_text_mopen'], labels)
+    data_open.at[index, 'Predicted_F'] = result_f['labels'][0]
+    data_open.at[index, 'Predicted_M'] = result_m['labels'][0]
+st.dataframe(data_open[['True_label', 'Predicted_F', 'Predicted_M']])