eval_pipeline.py

import streamlit as st
import pandas as pd
import torch
from transformers import BertTokenizer, AutoModelForSequenceClassification
from sklearn.metrics import roc_auc_score, roc_curve, confusion_matrix, classification_report, f1_score, precision_recall_fscore_support
import numpy as np
import plotly.graph_objects as go
import plotly.express as px
from tqdm import tqdm

def load_model_and_tokenizer():
    try:
        tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
        model = AutoModelForSequenceClassification.from_pretrained("CIS519PG/News_Classifier_Demo")
        device = "cuda" if torch.cuda.is_available() else "cpu"
        model = model.to(device)
        model.eval()
        return model, tokenizer, device
    except Exception as e:
        st.error(f"Error loading model or tokenizer: {str(e)}")
        return None, None, None

def preprocess_data(df):
    try:
        processed_data = []
        for _, row in df.iterrows():
            outlet = row["outlet"].strip().upper()
            if outlet == "FOX NEWS":
                outlet = "FOXNEWS"
            elif outlet == "NBC NEWS":
                outlet = "NBC"
            
            processed_data.append({
                "title": row["title"],
                "outlet": outlet
            })
        return processed_data
    except Exception as e:
        st.error(f"Error preprocessing data: {str(e)}")
        return None

def evaluate_model(model, tokenizer, device, test_dataset):
    label2id = {"FOXNEWS": 0, "NBC": 1}
    all_logits = []
    references = []
    
    batch_size = 16
    progress_bar = st.progress(0)
    
    for i in range(0, len(test_dataset), batch_size):
        # Update progress
        progress = min(i / len(test_dataset), 1.0)
        progress_bar.progress(progress)
        
        batch = test_dataset[i:i + batch_size]
        texts = [item['title'] for item in batch]

        encoded = tokenizer(
            texts,
            padding=True,
            truncation=True,
            max_length=128,
            return_tensors="pt"
        )

        inputs = {k: v.to(device) for k, v in encoded.items()}
        with torch.no_grad():
            outputs = model(**inputs)
            logits = outputs.logits.cpu().numpy()

        true_labels = [label2id[item['outlet']] for item in batch]
        all_logits.extend(logits)
        references.extend(true_labels)
    progress_bar.progress(1.0)
    probabilities = torch.softmax(torch.tensor(all_logits), dim=1).numpy()
    return references, probabilities

def plot_roc_curve(references, probabilities):
    fpr, tpr, _ = roc_curve(references, probabilities[:, 1])
    auc_score = roc_auc_score(references, probabilities[:, 1])
    fig = go.Figure()
    fig.add_trace(go.Scatter(x=fpr, y=tpr, name=f'ROC Curve (AUC = {auc_score:.4f})'))
    fig.add_trace(go.Scatter(x=[0, 1], y=[0, 1], name='Random Guess', line=dict(dash='dash')))
    fig.update_layout(
        title='ROC Curve',
        xaxis_title='False Positive Rate',
        yaxis_title='True Positive Rate',
        showlegend=True
    )
    return fig, auc_score

def plot_metrics_by_threshold(references, probabilities):
    thresholds = np.arange(0.0, 1.0, 0.01)
    metrics = {
        'threshold': thresholds,
        'f1': [],
        'precision': [],
        'recall': []
    }
    best_f1 = 0
    best_threshold = 0
    best_metrics = {}
    for threshold in thresholds:
        preds = (probabilities[:, 1] > threshold).astype(int)
        f1 = f1_score(references, preds)
        precision, recall, _, _ = precision_recall_fscore_support(references, preds, average='binary')
        metrics['f1'].append(f1)
        metrics['precision'].append(precision)
        metrics['recall'].append(recall)
        if f1 > best_f1:
            best_f1 = f1
            best_threshold = threshold
            cm = confusion_matrix(references, preds)
            report = classification_report(references, preds, target_names=['FOXNEWS', 'NBC'], digits=4)
            best_metrics = {
                'threshold': threshold,
                'f1_score': f1,
                'confusion_matrix': cm,
                'classification_report': report
            }
    fig = go.Figure()
    fig.add_trace(go.Scatter(x=thresholds, y=metrics['f1'], name='F1 Score'))
    fig.add_trace(go.Scatter(x=thresholds, y=metrics['precision'], name='Precision'))
    fig.add_trace(go.Scatter(x=thresholds, y=metrics['recall'], name='Recall'))
    fig.update_layout(
        title='Metrics by Threshold',
        xaxis_title='Threshold',
        yaxis_title='Score',
        showlegend=True
    )
    return fig, best_metrics

def plot_confusion_matrix(cm):
    labels = ['FOXNEWS', 'NBC']
    annotations = []
    for i in range(len(labels)):
        for j in range(len(labels)):
            annotations.append(
                dict(
                    text=str(cm[i, j]),
                    x=labels[j],
                    y=labels[i],
                    showarrow=False,
                    font=dict(color='white' if cm[i, j] > cm.max()/2 else 'black')
                )
            )
    fig = go.Figure(data=go.Heatmap(
        z=cm,
        x=labels,
        y=labels,
        colorscale='Blues',
        showscale=True
    ))
    fig.update_layout(
        title='Confusion Matrix',
        xaxis_title='Predicted Label',
        yaxis_title='True Label',
        annotations=annotations
    )
    return fig

def main():
    st.title("News Classifier Model Evaluation")
    uploaded_file = st.file_uploader("Upload your test dataset (CSV)", type=['csv']) 
    if uploaded_file is not None:
        df = pd.read_csv(uploaded_file)
        st.write("Preview of uploaded data:")
        st.dataframe(df.head())
        model, tokenizer, device = load_model_and_tokenizer()
        if model and tokenizer:
            test_dataset = preprocess_data(df)
            if test_dataset:
                st.write(f"Total examples: {len(test_dataset)}")
                with st.spinner('Evaluating model...'):
                    references, probabilities = evaluate_model(model, tokenizer, device, test_dataset)
                roc_fig, auc_score = plot_roc_curve(references, probabilities)
                st.plotly_chart(roc_fig)
                st.metric("AUC-ROC Score", f"{auc_score:.4f}")
                metrics_fig, best_metrics = plot_metrics_by_threshold(references, probabilities)
                st.plotly_chart(metrics_fig)
                st.subheader("Best Threshold Evaluation")
                col1, col2 = st.columns(2)
                with col1:
                    st.metric("Best Threshold", f"{best_metrics['threshold']:.2f}")
                with col2:
                    st.metric("Best F1 Score", f"{best_metrics['f1_score']:.4f}")
                st.subheader("Confusion Matrix")
                cm_fig = plot_confusion_matrix(best_metrics['confusion_matrix'])
                st.plotly_chart(cm_fig)
                st.subheader("Classification Report")
                st.text(best_metrics['classification_report'])
if __name__ == "__main__":
    main()