"""import streamlit as st
import pandas as pd
from transformers import pipeline
import re
import nltk
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
import logging

# Setup logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

# Download necessary NLTK resources
nltk.download('stopwords')
nltk.download('wordnet')

# Initialize the zero-shot classification pipeline
classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")

# 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")
data_open = pd.read_excel("ResponseOpen.xlsx")

# Define preprocessing function
def preprocess_text(text):
    text = re.sub(r'\W', ' ', str(text))
    text = text.lower()
    text = re.sub(r'\s+[a-z]\s+', ' ', text)
    text = re.sub(r'^[a-z]\s+', ' ', text)
    text = re.sub(r'\s+', ' ', text)
    stop_words = set(stopwords.words('english'))
    lemmatizer = WordNetLemmatizer()
    tokens = text.split()
    tokens = [lemmatizer.lemmatize(word) for word in tokens if word not in stop_words]
    return ' '.join(tokens)

# 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"]

# Function to predict personality and log the predictions
def predict_and_log(data, prediction_column, process_text_column, true_label_column=None, custom_labels=None):
    for index, row in data.iterrows():
        processed_text = row[process_text_column]
        if custom_labels:
            result = classifier(processed_text, [row[label] for label in custom_labels])
        else:
            result = classifier(processed_text, labels)
        highest_score_label = result['labels'][0]
        data.at[index, prediction_column] = highest_score_label
        true_label = row[true_label_column] if true_label_column else 'Not available'
        data_id = row['id']
        logging.info(f"Row {data_id}: True Label - {true_label}, {prediction_column} - {highest_score_label}")

# Predict and log results for each DataFrame
# predict_and_log(data, 'Predicted', 'processed_text', true_label_column='True_label', custom_labels=['MAX1', 'MAX2', 'MAX3'])
predict_and_log(data_open, 'Predicted_F', 'processed_text_open', true_label_column='True_label')
predict_and_log(data_open, 'Predicted_M', 'processed_text_mopen', true_label_column='True_label')

# 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()