Update README.md

README.md

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 ---
+title: News Source Classifier
+emoji: 📰
+colorFrom: blue
+colorTo: red
+sdk: streamlit
+app_file: eval_pipeline.py
 library_name: transformers
-tags: []
+pinned: false
+language: en
+license: mit
+tags:
+- text-classification
+- news-classification
+- BERT
+- pytorch
+- transformers
+pipeline_tag: text-classification
+widget:
+- example_title: "Politics News Headline"
+  text: "Trump's campaign rival decides between voting for him or Biden"
+- example_title: "International News Headline"
+  text: "World Food Programme Director Cindy McCain: Northern Gaza is in a 'full-blown famine'"
+- example_title: "Domestic News Headline"
+  text: "Ohio sheriff suggests residents keep a list of homes with Harris yard signs"
+model-index:
+- name: News Source Classifier
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Custom FOX-NBC Dataset
+      type: Custom
+    metrics:
+    - name: F1 Score
+      type: f1
+      value: 0.85
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
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-### Model Description
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-<!-- Provide a longer summary of what this model is. -->
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
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-## Uses
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-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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-### Direct Use
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-## Bias, Risks, and Limitations
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-### Recommendations
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
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-Use the code below to get started with the model.
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-## Training Details
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-### Training Data
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-## Evaluation
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-### Testing Data, Factors & Metrics
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-#### Factors
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-#### Metrics
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-### Results
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-#### Summary
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-## Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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+# News Source Classifier - BERT Model
+
+## Model Overview
+This repository contains a fine-tuned BERT model that classifies news headlines between Fox News and NBC News, along with an evaluation pipeline for assessing model performance using Streamlit.
+
+### Model Details
+- **Base Model**: BERT (bert-base-uncased)
+- **Task**: Binary classification (Fox News vs NBC News)
+- **Model ID**: CIS519PG/News_Classifier_Demo
+- **Training Data**: News headlines from Fox News and NBC News
+- **Input**: News article headlines (text)
+- **Output**: Binary classification with probability scores
+
+## Evaluation Pipeline Setup
+
+### Prerequisites
+- Python 3.8+
+- pip package manager
+
+### Required Dependencies
+Install the required packages using pip:
+```bash
+pip install streamlit pandas torch transformers scikit-learn numpy plotly tqdm
+```
+
+### Running the Evaluation Pipeline
+
+1. Save the following provided evaluation code as `eval_pipeline.py`, also downloadable in files.
+
+```bash
+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["News 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):
+        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()
+```
+
+2. Run the Streamlit application:
+```bash
+streamlit run eval_pipeline.py
+```
+
+3. The web interface will automatically open in your default browser
+
+### Using the Web Interface
+
+1. **Upload Test Data**:
+   - Prepare your test data in CSV format
+   - Required columns:
+     - Index column (automatic numbering)
+     - "title": The news headline text
+     - "label": Binary label (0 for Fox News, 1 for NBC News)
+     - "News Outlet": The source ("Fox News" or "NBC News")
+
+2. **View Evaluation Results**:
+   The pipeline will display:
+   - Data preview
+   - ROC curve with AUC score
+   - Metrics vs threshold plot
+   - Best threshold and F1 score
+   - Confusion matrix visualization
+   - Detailed classification report
+
+### Sample Data Format
+```csv
+,title,label,News Outlet
+0,"Jack Carr's take on the late Tom Clancy, born on this day in 1947",0,Fox News
+1,"Feeding America CEO asks community to help others amid today's high inflation",0,Fox News
+2,"World Food Programme Director Cindy McCain: Northern Gaza is in a 'full-blown famine'",1,NBC News
+3,"Ohio sheriff suggests residents keep a list of homes with Harris yard signs",1,NBC News
+```
+
+## Model Architecture
+- Base model: BERT (bert-base-uncased)
+- Fine-tuned for binary classification
+- Uses PyTorch and Hugging Face Transformers
+
+## Limitations and Bias
+This model has been trained on news headlines from specific sources (Fox News and NBC News) and time periods, which may introduce certain biases:
+- Limited to two specific news sources
+- Temporal bias based on training data collection period
+- May not generalize well to other news sources or formats
+
+## Evaluation Metrics
+The pipeline provides comprehensive evaluation metrics:
+- AUC-ROC Score
+- F1 Score
+- Precision & Recall
+- Confusion Matrix
+- Detailed Classification Report
+
+## Troubleshooting
+
+Common issues and solutions:
+
+1. **CUDA/GPU Error**:
+   - The pipeline automatically falls back to CPU if CUDA is not available
+   - No action needed from user
+
+2. **Memory Issues**:
+   - Default batch size is 16
+   - Reduce batch size if memory constraints exist
+
+3. **File Format Error**:
+   - Ensure CSV file has exact column names: "title", "label", "News Outlet"
+   - Verify label values are 0 or 1
+   - Confirm "News Outlet" values are exactly "Fox News" or "NBC News"
+
+## License
+This project is licensed under the MIT License.