first commit app.py and the content

.gitignore

@@ -0,0 +1,3 @@
+venv/
+__pycache__/
+.gradio/

app.py

@@ -0,0 +1,203 @@
+import gradio as gr
+import pandas as pd
+import numpy as np
+import joblib
+import os
+
+# ==============================================================================
+# 1. LOAD MODELS AND SCALER (This part runs once when the script starts)
+# ==============================================================================
+
+# Dictionary to hold the loaded model objects and a list of their names
+all_models = {}
+model_names = [
+    'Linear Regression', 'Ridge Regression', 'Lasso Regression',
+    'Random Forest', 'Gradient Boosting'
+]
+BEST_MODEL_NAME = 'Random Forest' # Define the best model to be highlighted
+
+try:
+    # Load all the regression models
+    for name in model_names:
+        # Construct the filename, e.g., 'models/random_forest.joblib'
+        filename = f"models/{name.lower().replace(' ', '_')}.joblib"
+        if os.path.exists(filename):
+            all_models[name] = joblib.load(filename)
+        else:
+            raise FileNotFoundError(f"Model file not found: {filename}")
+
+    # Load the scaler
+    scaler_path = 'models/scaler.joblib'
+    if os.path.exists(scaler_path):
+        scaler = joblib.load(scaler_path)
+    else:
+        raise FileNotFoundError(f"Scaler file not found: {scaler_path}")
+
+    models_loaded = True
+    print("✅ All models and scaler loaded successfully!")
+
+    # Get the feature names the model was trained on from the scaler
+    expected_columns = scaler.feature_names_in_
+    print(f"Models expect {len(expected_columns)} features.")
+
+except Exception as e:
+    print(f"❌ ERROR: Could not load models. {e}")
+    print("Please ensure all '.joblib' files are in the 'models/' directory.")
+    models_loaded = False
+    all_models = {}
+    scaler = None
+    expected_columns = []
+
+# ==============================================================================
+# 2. PREDICTION FUNCTION
+# ==============================================================================
+
+def predict_shares_all_models(likes, generation_time, gpu_usage, file_size_kb,
+                              width, height, style_accuracy_score,
+                              is_hand_edited, ethical_concerns_flag,
+                              day_of_week, month, hour, platform):
+    """
+    Performs feature engineering, predicts shares using all loaded models,
+    and returns formatted outputs for the Gradio interface.
+    """
+    if not models_loaded:
+        error_message = "Models are not loaded. Please check the console for errors."
+        return 0, error_message, error_message
+
+    # --- Step A: Perform feature engineering ---
+    sample_data = {
+        'likes': likes,
+        'style_accuracy_score': style_accuracy_score,
+        'generation_time': generation_time,
+        'gpu_usage': gpu_usage,
+        'file_size_kb': file_size_kb,
+        'is_hand_edited': int(is_hand_edited),
+        'ethical_concerns_flag': int(ethical_concerns_flag),
+        'width': width,
+        'height': height,
+        'day_of_week': day_of_week,
+        'month': month,
+        'hour': hour
+    }
+
+    sample_data['aspect_ratio'] = width / height if height > 0 else 0
+    sample_data['total_pixels'] = width * height
+    sample_data['is_square'] = int(width == height)
+    sample_data['is_weekend'] = int(day_of_week >= 5)
+
+    for p in ['Twitter', 'TikTok', 'Reddit', 'Instagram']:
+        sample_data[f'platform_{p}'] = 1 if platform == p else 0
+
+    sample_data['engagement_rate'] = likes / (sample_data['total_pixels'] / 1000000 + 1)
+    sample_data['quality_engagement'] = style_accuracy_score * likes / 100
+    sample_data['file_density'] = file_size_kb / (sample_data['total_pixels'] / 1000 + 1)
+    sample_data['gpu_efficiency'] = generation_time / (gpu_usage + 1)
+
+    for p in ['Twitter', 'TikTok', 'Reddit', 'Instagram']:
+         sample_data[f'{p.lower()}_likes'] = likes * sample_data[f'platform_{p}']
+
+    sample_data['month_sin'] = np.sin(2 * np.pi * month / 12)
+    sample_data['month_cos'] = np.cos(2 * np.pi * month / 12)
+    sample_data['day_sin'] = np.sin(2 * np.pi * day_of_week / 7)
+    sample_data['day_cos'] = np.cos(2 * np.pi * day_of_week / 7)
+
+    # --- Step B: Align columns and Scale ---
+    sample_df = pd.DataFrame([sample_data])
+    sample_df = sample_df.reindex(columns=expected_columns, fill_value=0)
+    sample_scaled = scaler.transform(sample_df)
+
+    # --- Step C: Predict with all models ---
+    predictions = {}
+    for name, model in all_models.items():
+        pred_value = model.predict(sample_scaled)[0]
+        predictions[name] = max(0, int(pred_value))
+
+    # --- Step D: Format the outputs for Gradio ---
+
+    # 1. Get the single best model prediction
+    best_model_prediction = predictions.get(BEST_MODEL_NAME, 0)
+
+    # 2. Create a Markdown table for all model predictions
+    all_results_df = pd.DataFrame(list(predictions.items()), columns=['Model', 'Predicted Shares'])
+    all_results_df = all_results_df.sort_values('Predicted Shares', ascending=False)
+    all_models_table = all_results_df.to_markdown(index=False)
+
+    # 3. Create a Markdown table for the engineered features
+    features_df = sample_df.T.reset_index()
+    features_df.columns = ['Feature', 'Value']
+    features_df['Value'] = features_df['Value'].apply(lambda x: f"{x:.4f}" if isinstance(x, float) else x)
+    features_table = features_df.to_markdown(index=False)
+
+    return best_model_prediction, all_models_table, features_table
+
+# ==============================================================================
+# 3. GRADIO INTERFACE
+# ==============================================================================
+
+with gr.Blocks(theme=gr.themes.Soft(), title="AI Image Virality Predictor") as demo:
+    gr.Markdown("# 🎨 AI Ghibli Image Virality Predictor")
+    gr.Markdown("Enter image features to get a virality prediction from multiple regression models.")
+
+    with gr.Row():
+        # --- INPUTS COLUMN ---
+        with gr.Column(scale=2):
+            gr.Markdown("### 1. Input Features")
+            with gr.Accordion("Core Engagement & Image Metrics", open=True):
+                likes = gr.Slider(minimum=0, maximum=10000, value=500, step=10, label="Likes")
+                style_accuracy_score = gr.Slider(minimum=0, maximum=100, value=85, step=1, label="Style Accuracy Score (%)")
+                width = gr.Slider(minimum=256, maximum=2048, value=1024, step=64, label="Width (px)")
+                height = gr.Slider(minimum=256, maximum=2048, value=1024, step=64, label="Height (px)")
+                file_size_kb = gr.Slider(minimum=100, maximum=5000, value=1500, step=100, label="File Size (KB)")
+
+            with gr.Accordion("Technical & Posting Details", open=True):
+                generation_time = gr.Slider(minimum=1, maximum=30, value=8, step=0.5, label="Generation Time (s)")
+                gpu_usage = gr.Slider(minimum=10, maximum=100, value=70, step=5, label="GPU Usage (%)")
+                platform = gr.Radio(["Instagram", "Twitter", "TikTok", "Reddit"], label="Platform", value="Instagram")
+                day_of_week = gr.Slider(minimum=0, maximum=6, value=4, step=1, label="Day of Week (0=Mon, 6=Sun)")
+                month = gr.Slider(minimum=1, maximum=12, value=7, step=1, label="Month (1-12)")
+                hour = gr.Slider(minimum=0, maximum=23, value=18, step=1, label="Hour of Day (0-23)")
+                is_hand_edited = gr.Checkbox(label="Was it Hand Edited?", value=False)
+                ethical_concerns_flag = gr.Checkbox(label="Any Ethical Concerns?", value=False)
+
+            predict_btn = gr.Button("Predict Virality", variant="primary")
+
+        # --- OUTPUTS COLUMN ---
+        with gr.Column(scale=3):
+            gr.Markdown("### 2. Prediction Results")
+
+            # Highlighted Best Model Output
+            best_model_output = gr.Number(
+                label=f"🏆 Best Model Prediction ({BEST_MODEL_NAME})",
+                interactive=False
+            )
+
+            # Table for All Model Predictions
+            with gr.Accordion("Comparison of All Models", open=True):
+                all_models_output = gr.Markdown(label="All Model Predictions")
+
+            # Table for Feature Engineering Details
+            with gr.Accordion("View Engineered Features", open=False):
+                features_output = gr.Markdown(label="Feature Engineering Details")
+
+    # Connect the button to the function
+    predict_btn.click(
+        fn=predict_shares_all_models,
+        inputs=[
+            likes, generation_time, gpu_usage, file_size_kb,
+            width, height, style_accuracy_score,
+            is_hand_edited, ethical_concerns_flag,
+            day_of_week, month, hour, platform
+        ],
+        outputs=[
+            best_model_output,
+            all_models_output,
+            features_output
+        ]
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    if not models_loaded:
+        print("\nCannot launch Gradio app because models failed to load.")
+    else:
+        demo.launch()

requirements.txt

@@ -0,0 +1,7 @@
+pandas>=2.2.0
+numpy>=1.26.0
+matplotlib>=3.8.0
+seaborn>=0.13.0
+scikit-learn>=1.4.0
+gradio>=5.3.0
+tabulate

results/model_comparison.csv

@@ -0,0 +1,6 @@
+Model,R² Score,MAE,RMSE
+Random Forest,-0.08503617106593597,518.7684998150959,593.7506113831865
+Ridge Regression,-0.08676700700361528,528.5892908496174,594.223994396894
+Lasso Regression,-0.08764565917116918,528.573248905534,594.4641611976449
+Linear Regression,-0.09914175984109797,531.4132783380423,597.5975604762958
+Gradient Boosting,-0.2357883805937282,537.630957194942,633.6566769562837

results/regression_analysis_results.json

@@ -0,0 +1,83 @@
+{
+  "dataset_info": {
+    "total_samples": 500,
+    "features": 29,
+    "target_mean": 1040.182,
+    "target_median": 1092.0,
+    "target_std": 562.6687383302794
+  },
+  "model_comparison": [
+    {
+      "Model": "Random Forest",
+      "R\u00b2 Score": -0.08503617106593597,
+      "MAE": 518.7684998150959,
+      "RMSE": 593.7506113831865
+    },
+    {
+      "Model": "Ridge Regression",
+      "R\u00b2 Score": -0.08676700700361528,
+      "MAE": 528.5892908496174,
+      "RMSE": 594.223994396894
+    },
+    {
+      "Model": "Lasso Regression",
+      "R\u00b2 Score": -0.08764565917116918,
+      "MAE": 528.573248905534,
+      "RMSE": 594.4641611976449
+    },
+    {
+      "Model": "Linear Regression",
+      "R\u00b2 Score": -0.09914175984109797,
+      "MAE": 531.4132783380423,
+      "RMSE": 597.5975604762958
+    },
+    {
+      "Model": "Gradient Boosting",
+      "R\u00b2 Score": -0.2357883805937282,
+      "MAE": 537.630957194942,
+      "RMSE": 633.6566769562837
+    }
+  ],
+  "feature_correlations": [
+    {
+      "feature": "platform_Twitter",
+      "correlation": -0.11310486794074195
+    },
+    {
+      "feature": "platform_Instagram",
+      "correlation": 0.07096989443791954
+    },
+    {
+      "feature": "total_pixels",
+      "correlation": 0.05340067376167711
+    },
+    {
+      "feature": "width",
+      "correlation": 0.050954190148673084
+    },
+    {
+      "feature": "height",
+      "correlation": 0.050954190148673084
+    },
+    {
+      "feature": "platform_Reddit",
+      "correlation": 0.030824709708669493
+    },
+    {
+      "feature": "likes",
+      "correlation": -0.029318071149881914
+    },
+    {
+      "feature": "is_hand_edited",
+      "correlation": 0.02824023580536551
+    },
+    {
+      "feature": "day_of_week",
+      "correlation": 0.02490306263783807
+    },
+    {
+      "feature": "file_size_kb",
+      "correlation": -0.020748477243945303
+    }
+  ]
+}