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app.py

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+# app.py
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from scipy import stats
+from sklearn.preprocessing import StandardScaler
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import classification_report, roc_auc_score
+from tabulate import tabulate
+import warnings
+import traceback
+import gradio as gr
+import os
+import git
+
+# --- Main Class (Slightly Refactored for Interactivity) ---
+# The core logic remains, but we separate data loading from analysis functions.
+warnings.filterwarnings('ignore')
+plt.style.use('default')
+sns.set_palette("husl")
+
+
+class EnhancedAIvsRealGazeAnalyzer:
+    def __init__(self):
+        self.questions = ['Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6']
+        self.correct_answers = {'Pair1': 'B', 'Pair2': 'B', 'Pair3': 'B', 'Pair4': 'B', 'Pair5': 'B', 'Pair6': 'B'}
+        self.combined_data = None
+        self.response_data = None
+        self.numeric_cols = []
+        self.time_metrics = []
+
+    def load_and_process_data(self, base_path, response_file):
+        """Loads all data and preprocesses it once."""
+        print("Loading and processing data...")
+        # Load response data
+        self.response_data = pd.read_excel(response_file) if response_file.endswith('.xlsx') else pd.read_csv(
+            response_file)
+        self.response_data.columns = self.response_data.columns.str.strip()
+        for pair, correct_answer in self.correct_answers.items():
+            if pair in self.response_data.columns:
+                self.response_data[f'{pair}_Correct'] = (
+                            self.response_data[pair].astype(str).str.strip().str.upper() == correct_answer)
+
+        # Load eye-tracking data
+        all_data = {}
+        for question in self.questions:
+            file_path = f"{base_path}/Filtered_GenAI_Metrics_cleaned_{question}.xlsx"
+            if os.path.exists(file_path):
+                xls = pd.ExcelFile(file_path)
+                all_data[question] = {sheet_name: pd.read_excel(xls, sheet_name) for sheet_name in xls.sheet_names}
+
+        # Combine and merge
+        all_dfs = [df.copy().assign(Question=q, Metric_Type=m) for q, qd in all_data.items() for m, df in qd.items()]
+        if not all_dfs:
+            raise ValueError("No eye-tracking data files were found or loaded.")
+
+        self.combined_data = pd.concat(all_dfs, ignore_index=True)
+        self.combined_data.columns = self.combined_data.columns.str.strip()
+
+        # Merge with responses
+        et_id_col = next((c for c in self.combined_data.columns if 'participant' in c.lower()), None)
+        resp_id_col = next((c for c in self.response_data.columns if 'participant' in c.lower()), None)
+
+        response_long = self.response_data.melt(id_vars=[resp_id_col], value_vars=self.correct_answers.keys(),
+                                                var_name='Pair', value_name='Response')
+        correctness_long = self.response_data.melt(id_vars=[resp_id_col],
+                                                   value_vars=[f'{p}_Correct' for p in self.correct_answers.keys()],
+                                                   var_name='Pair_Correct_Col', value_name='Correct')
+        correctness_long['Pair'] = correctness_long['Pair_Correct_Col'].str.replace('_Correct', '')
+        response_long = response_long.merge(correctness_long[[resp_id_col, 'Pair', 'Correct']],
+                                            on=[resp_id_col, 'Pair'])
+
+        q_to_pair = {f'Q{i + 1}': f'Pair{i + 1}' for i in range(6)}
+        self.combined_data['Pair'] = self.combined_data['Question'].map(q_to_pair)
+        self.combined_data = self.combined_data.merge(response_long, left_on=[et_id_col, 'Pair'],
+                                                      right_on=[resp_id_col, 'Pair'], how='left')
+        self.combined_data['Answer_Correctness'] = self.combined_data['Correct'].map(
+            {True: 'Correct', False: 'Incorrect'})
+
+        # Identify numeric and time columns for later use
+        self.numeric_cols = self.combined_data.select_dtypes(include=np.number).columns.tolist()
+        self.time_metrics = [c for c in self.numeric_cols if
+                             any(k in c.lower() for k in ['time', 'duration', 'fixation'])]
+        print("Data loading complete.")
+        return self  # Return self for chaining
+
+    def analyze_rq1_metric(self, metric):
+        """Analyzes a single metric for RQ1."""
+        if metric not in self.combined_data.columns:
+            return None, "Metric not found."
+
+        correct = self.combined_data.loc[self.combined_data['Answer_Correctness'] == 'Correct', metric].dropna()
+        incorrect = self.combined_data.loc[self.combined_data['Answer_Correctness'] == 'Incorrect', metric].dropna()
+
+        # Perform t-test
+        t_stat, p_val = stats.ttest_ind(incorrect, correct, equal_var=False, nan_policy='omit')
+
+        # Create plot
+        fig, ax = plt.subplots(figsize=(8, 6))
+        sns.boxplot(data=self.combined_data, x='Answer_Correctness', y=metric, ax=ax, palette=['#66b3ff', '#ff9999'])
+        ax.set_title(f'Comparison of "{metric}" by Answer Correctness', fontsize=14)
+        ax.set_xlabel("Answer Correctness")
+        ax.set_ylabel(metric)
+        plt.tight_layout()
+
+        # Create summary text
+        summary = f"""
+        ### Analysis for: **{metric}**
+        - **Mean (Correct Answers):** {correct.mean():.4f}
+        - **Mean (Incorrect Answers):** {incorrect.mean():.4f}
+        - **T-test p-value:** {p_val:.4f}
+
+        **Conclusion:**
+        - {'There is a **statistically significant** difference between the groups (p < 0.05).' if p_val < 0.05 else 'There is **no statistically significant** difference between the groups (p >= 0.05).'}
+        """
+        return fig, summary
+
+    def run_prediction_model(self, test_size, n_estimators):
+        """Runs the RandomForest model with given parameters for RQ2."""
+        leaky_features = ['Total_Correct', 'Overall_Accuracy', 'Correct']
+        features_to_use = [col for col in self.numeric_cols if col not in leaky_features]
+
+        features = self.combined_data[features_to_use].copy()
+        target = self.combined_data['Answer_Correctness'].map({'Correct': 1, 'Incorrect': 0})
+
+        valid_indices = target.notna()
+        features, target = features[valid_indices], target[valid_indices]
+        features = features.fillna(features.median()).fillna(0)
+
+        if len(target.unique()) < 2:
+            return "Not enough classes to train the model.", None
+
+        X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=test_size, random_state=42,
+                                                            stratify=target)
+        scaler = StandardScaler()
+        X_train_scaled, X_test_scaled = scaler.fit_transform(X_train), scaler.transform(X_test)
+
+        model = RandomForestClassifier(n_estimators=n_estimators, random_state=42, class_weight='balanced')
+        model.fit(X_train_scaled, y_train)
+        y_pred_proba = model.predict_proba(X_test_scaled)[:, 1]
+        y_pred = model.predict(X_test_scaled)
+
+        # Generate results
+        report = classification_report(y_test, y_pred, target_names=['Incorrect', 'Correct'], output_dict=True)
+        auc_score = roc_auc_score(y_test, y_pred_proba)
+
+        report_df = pd.DataFrame(report).transpose().round(3)
+        report_md = f"""
+        ### Model Performance
+        - **AUC Score:** **{auc_score:.4f}**
+        - **Overall Accuracy:** {report['accuracy']:.3f}
+
+        **Classification Report:**
+        {report_df.to_markdown()}
+        """
+
+        # Feature importance plot
+        feature_importance = pd.DataFrame({'Feature': features.columns, 'Importance': model.feature_importances_})
+        feature_importance = feature_importance.sort_values('Importance', ascending=False).head(15)
+
+        fig, ax = plt.subplots(figsize=(10, 8))
+        sns.barplot(data=feature_importance, x='Importance', y='Feature', ax=ax, palette='viridis')
+        ax.set_title(f'Top 15 Predictive Features (n_estimators={n_estimators})', fontsize=14)
+        plt.tight_layout()
+
+        return report_md, fig
+
+
+# --- DATA SETUP (RUNS ONCE AT STARTUP) ---
+def setup_and_load_data():
+    """Clones the repo if not present and loads data."""
+    repo_url = "https://github.com/RextonRZ/GenAIEyeTrackingCleanedDataset"
+    repo_dir = "GenAIEyeTrackingCleanedDataset"
+
+    if not os.path.exists(repo_dir):
+        print(f"Cloning data repository from {repo_url}...")
+        git.Repo.clone_from(repo_url, repo_dir)
+    else:
+        print("Data repository already exists.")
+
+    base_path = os.path.join(repo_dir, "cleaned_dataset")
+    response_file = os.path.join(repo_dir, "response_sheet", "GenAI_Response_Sheet.xlsx")
+
+    analyzer = EnhancedAIvsRealGazeAnalyzer().load_and_process_data(base_path, response_file)
+    return analyzer
+
+
+print("Starting application setup...")
+analyzer = setup_and_load_data()
+print("Application setup complete. Ready for interaction.")
+
+
+# --- GRADIO INTERACTIVE FUNCTIONS ---
+def update_rq1_visuals(metric_choice):
+    """Called by Gradio when the dropdown for RQ1 changes."""
+    if not metric_choice:
+        return None, "Please select a metric from the dropdown."
+    plot, summary = analyzer.analyze_rq1_metric(metric_choice)
+    return plot, summary
+
+
+def update_rq2_model(test_size, n_estimators):
+    """Called by Gradio when sliders for RQ2 change."""
+    n_estimators = int(n_estimators)  # Ensure it's an integer
+    report, plot = analyzer.run_prediction_model(test_size, n_estimators)
+    return report, plot
+
+
+# --- GRADIO INTERFACE DEFINITION ---
+description = """
+# Interactive Dashboard: AI vs. Real Gaze Analysis
+Explore the eye-tracking dataset by interacting with the controls below. The data is automatically loaded from the public GitHub repository.
+"""
+
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown(description)
+
+    with gr.Tabs():
+        with gr.TabItem("RQ1: Viewing Time vs. Correctness"):
+            gr.Markdown("### Does viewing time differ based on whether a participant's answer was correct?")
+            with gr.Row():
+                with gr.Column(scale=1):
+                    rq1_metric_dropdown = gr.Dropdown(
+                        choices=analyzer.time_metrics,
+                        label="Select a Time-Based Metric to Analyze",
+                        value=analyzer.time_metrics[0] if analyzer.time_metrics else None
+                    )
+                    rq1_summary_output = gr.Markdown(label="Statistical Summary")
+                with gr.Column(scale=2):
+                    rq1_plot_output = gr.Plot(label="Metric Comparison")
+
+        with gr.TabItem("RQ2: Predicting Correctness from Gaze"):
+            gr.Markdown("### Can we build a model to predict answer correctness from gaze patterns?")
+            with gr.Row():
+                with gr.Column(scale=1):
+                    gr.Markdown("#### Tune Model Hyperparameters")
+                    rq2_test_size_slider = gr.Slider(
+                        minimum=0.1, maximum=0.5, step=0.05, value=0.3, label="Test Set Size"
+                    )
+                    rq2_estimators_slider = gr.Slider(
+                        minimum=10, maximum=200, step=10, value=100, label="Number of Trees (n_estimators)"
+                    )
+                    rq2_report_output = gr.Markdown(label="Model Performance Report")
+                with gr.Column(scale=2):
+                    rq2_plot_output = gr.Plot(label="Feature Importance")
+
+    # Wire up the interactive components
+    rq1_metric_dropdown.change(
+        fn=update_rq1_visuals,
+        inputs=[rq1_metric_dropdown],
+        outputs=[rq1_plot_output, rq1_summary_output]
+    )
+
+    # Use .release to only update when the user lets go of the slider
+    rq2_test_size_slider.release(
+        fn=update_rq2_model,
+        inputs=[rq2_test_size_slider, rq2_estimators_slider],
+        outputs=[rq2_report_output, rq2_plot_output]
+    )
+    rq2_estimators_slider.release(
+        fn=update_rq2_model,
+        inputs=[rq2_test_size_slider, rq2_estimators_slider],
+        outputs=[rq2_report_output, rq2_plot_output]
+    )
+
+    # Load initial state
+    demo.load(
+        fn=update_rq1_visuals,
+        inputs=[rq1_metric_dropdown],
+        outputs=[rq1_plot_output, rq1_summary_output]
+    )
+    demo.load(
+        fn=update_rq2_model,
+        inputs=[rq2_test_size_slider, rq2_estimators_slider],
+        outputs=[rq2_report_output, rq2_plot_output]
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+pandas
+numpy
+matplotlib
+seaborn
+scipy
+scikit-learn
+gradio
+openpyxl
+GitPython