Update app.py

app.py

@@ -1,4 +1,4 @@
-import streamlit as st
+"""import streamlit as st
 import pandas as pd
 from transformers import pipeline
 import re
@@ -66,3 +66,46 @@ predict_and_log(data_open, 'Predicted_M', 'processed_text_mopen', true_label_col
 
 # Optionally display a confirmation message 
 st.write("Predictions have been logged. Check your logs for details.")
+"""
+import pandas as pd
+from transformers import pipeline
+from sklearn.metrics import accuracy_score, precision_recall_fscore_support, confusion_matrix
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+# Load data
+data = pd.read_excel("ResponseOpenPredicted.xlsx")
+
+# Calculate metrics
+def calculate_metrics(true_labels, predicted_labels):
+    accuracy = accuracy_score(true_labels, predicted_labels)
+    precision, recall, f1_score, _ = precision_recall_fscore_support(true_labels, predicted_labels, average='weighted')
+    return accuracy, precision, recall, f1_score
+
+accuracy_f, precision_f, recall_f, f1_score_f = calculate_metrics(data['True_label'], data['Predicted_F'])
+accuracy_m, precision_m, recall_m, f1_score_m = calculate_metrics(data['True_label'], data['Predicted_M'])
+
+# Confusion matrices visualization
+conf_matrix_f = confusion_matrix(data['True_label'], data['Predicted_F'])
+conf_matrix_m = confusion_matrix(data['True_label'], data['Predicted_M'])
+
+fig, ax = plt.subplots(1, 2, figsize=(12, 6))
+sns.heatmap(conf_matrix_f, annot=True, fmt="d", cmap="Blues", ax=ax[0])
+ax[0].set_title('Confusion Matrix for Predicted_F')
+sns.heatmap(conf_matrix_m, annot=True, fmt="d", cmap="Purples", ax=ax[1])
+ax[1].set_title('Confusion Matrix for Predicted_M')
+
+# Distribution of prediction results
+fig, ax = plt.subplots(1, 2, figsize=(12, 6))
+data['Predicted_F'].value_counts().plot(kind='bar', ax=ax[0], color='blue')
+ax[0].set_title('Distribution of Predictions for Female Inputs')
+ax[0].set_xlabel('Predicted Labels')
+ax[0].set_ylabel('Frequency')
+
+data['Predicted_M'].value_counts().plot(kind='bar', ax=ax[1], color='purple')
+ax[1].set_title('Distribution of Predictions for Male Inputs')
+ax[1].set_xlabel('Predicted Labels')
+ax[1].set_ylabel('Frequency')
+
+plt.tight_layout()
+plt.show()