lint app.py and move gDrive version to A3

A3/app.py

@@ -0,0 +1,401 @@
+import gradio as gr
+import pandas as pd
+import pickle
+import os
+import gdown
+
+# Get directory where this script is located
+SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
+
+# Google Drive file IDs for model downloads
+MODEL_GDRIVE_ID = "1ORlU0OOCBkWXVO2UFAkXaKtXfkOH7w1t"
+CLASSIFICATION_MODEL_GDRIVE_ID = "1qU6Q37CoToMxzBwori5V3_bonBIIb-K0"
+
+# Local paths - models loaded from A3/models/ directory
+MODEL_PATH = os.path.join(SCRIPT_DIR, "A3/models/champion_model_final_2.pkl")
+CLASSIFICATION_MODEL_PATH = os.path.join(SCRIPT_DIR, "A3/models/final_champion_model_A3.pkl")
+DATA_PATH = os.path.join(SCRIPT_DIR, "A3/A3_Data/train_dataset.csv")
+
+
+def download_from_gdrive(file_id, destination):
+    """Download a file from Google Drive using gdown."""
+    os.makedirs(os.path.dirname(destination), exist_ok=True)
+    url = f"https://drive.google.com/uc?id={file_id}"
+    gdown.download(url, destination, quiet=False)
+    print(f"Downloaded to {destination}")
+    return True
+
+model = None
+FEATURE_NAMES = None
+MODEL_METRICS = None
+
+# Classification model
+classification_model = None
+CLASSIFICATION_FEATURE_NAMES = None
+CLASSIFICATION_CLASSES = None
+CLASSIFICATION_METRICS = None
+
+BODY_REGION_RECOMMENDATIONS = {
+    'Upper Body': "Focus on shoulder mobility, thoracic spine extension, and keeping your head neutral.",
+    'Lower Body': "Work on hip mobility, ankle dorsiflexion, and knee tracking over toes."
+}
+
+
+def load_champion_model():
+    global model, FEATURE_NAMES, MODEL_METRICS
+    
+    # Download from Google Drive if not exists locally
+    if not os.path.exists(MODEL_PATH):
+        print(f"Model not found locally, downloading from Google Drive...")
+        try:
+            download_from_gdrive(MODEL_GDRIVE_ID, MODEL_PATH)
+        except Exception as e:
+            print(f"Failed to download model: {e}")
+            return False
+    
+    if os.path.exists(MODEL_PATH):
+        print(f"Loading champion model from {MODEL_PATH}")
+        with open(MODEL_PATH, "rb") as f:
+            artifact = pickle.load(f)
+        
+        model = artifact["model"]
+        FEATURE_NAMES = artifact["feature_columns"]
+        MODEL_METRICS = artifact.get("test_metrics", {})
+        
+        print(f"Model loaded: {len(FEATURE_NAMES)} features")
+        print(f"Test R2: {MODEL_METRICS.get('r2', 'N/A')}")
+        return True
+    
+    print(f"Champion model not found at {MODEL_PATH}")
+    return False
+
+
+def load_classification_model():
+    global classification_model, CLASSIFICATION_FEATURE_NAMES, CLASSIFICATION_CLASSES, CLASSIFICATION_METRICS
+    
+    # Download from Google Drive if not exists locally
+    if not os.path.exists(CLASSIFICATION_MODEL_PATH):
+        print(f"Classification model not found locally, downloading from Google Drive...")
+        try:
+            download_from_gdrive(CLASSIFICATION_MODEL_GDRIVE_ID, CLASSIFICATION_MODEL_PATH)
+        except Exception as e:
+            print(f"Failed to download classification model: {e}")
+            return False
+    
+    if os.path.exists(CLASSIFICATION_MODEL_PATH):
+        print(f"Loading classification model from {CLASSIFICATION_MODEL_PATH}")
+        with open(CLASSIFICATION_MODEL_PATH, "rb") as f:
+            artifact = pickle.load(f)
+        
+        classification_model = artifact["model"]
+        CLASSIFICATION_FEATURE_NAMES = artifact["feature_columns"]
+        CLASSIFICATION_CLASSES = artifact["classes"]
+        CLASSIFICATION_METRICS = artifact.get("test_metrics", {})
+        
+        print(f"Classification model loaded: {len(CLASSIFICATION_FEATURE_NAMES)} features")
+        print(f"Classes: {CLASSIFICATION_CLASSES}")
+        return True
+    
+    print(f"Classification model not found at {CLASSIFICATION_MODEL_PATH}")
+    return False
+
+
+load_champion_model()
+load_classification_model()
+
+
+def predict_score(*feature_values):
+    if model is None:
+        return "Error", "Model not loaded", ""
+    
+    features_df = pd.DataFrame([feature_values], columns=FEATURE_NAMES)
+    raw_score = model.predict(features_df)[0]
+    score = max(0, min(1, raw_score)) * 100
+
+    if score >= 80:
+        interpretation = "Excellent, great squat form"
+    elif score >= 60:
+        interpretation = "Good, minor improvements needed"
+    elif score >= 40:
+        interpretation = "Average, a lot of areas to work on"
+    else:
+        interpretation = "Needs work, focus on proper form"
+
+    r2 = MODEL_METRICS.get('r2', 'N/A')
+    correlation = MODEL_METRICS.get('correlation', 'N/A')
+    r2_str = f"{r2:.4f}" if isinstance(r2, (int, float)) else str(r2)
+    corr_str = f"{correlation:.4f}" if isinstance(correlation, (int, float)) else str(correlation)
+    
+    details = f"""
+### Prediction Details
+- **Raw Model Output:** {raw_score:.4f}
+- **Normalized Score:** {score:.1f}%
+- **Assessment:** {interpretation}
+
+### Model Performance
+- **Test R-squared:** {r2_str}
+- **Test Correlation:** {corr_str}
+
+*Lower deviation values = better form*
+    """
+
+    return f"{score:.1f}%", interpretation, details
+
+
+def predict_weakest_link(*feature_values):
+    if classification_model is None:
+        return "Error", "Model not loaded", ""
+    
+    features_df = pd.DataFrame([feature_values], columns=CLASSIFICATION_FEATURE_NAMES)
+    
+    prediction = classification_model.predict(features_df)[0]
+    probabilities = classification_model.predict_proba(features_df)[0]
+    
+    class_probs = list(zip(CLASSIFICATION_CLASSES, probabilities))
+    class_probs.sort(key=lambda x: x[1], reverse=True)
+    
+    confidence = max(probabilities) * 100
+    recommendation = BODY_REGION_RECOMMENDATIONS.get(prediction, "Focus on exercises that strengthen this region.")
+    
+    accuracy = CLASSIFICATION_METRICS.get('accuracy', 'N/A')
+    f1_weighted = CLASSIFICATION_METRICS.get('f1_weighted', 'N/A')
+    acc_str = f"{accuracy:.2%}" if isinstance(accuracy, (int, float)) else str(accuracy)
+    f1_str = f"{f1_weighted:.2%}" if isinstance(f1_weighted, (int, float)) else str(f1_weighted)
+    
+    predictions_list = "\n".join([f"{i+1}. **{cp[0]}** - {cp[1]*100:.1f}%" for i, cp in enumerate(class_probs)])
+    
+    details = f"""
+### Prediction Details
+- **Predicted Body Region:** {prediction}
+- **Confidence:** {confidence:.1f}%
+
+### Probability Distribution
+{predictions_list}
+
+### Recommendation
+{recommendation}
+
+### Model Performance
+- **Test Accuracy:** {acc_str}
+- **Test F1 (weighted):** {f1_str}
+    """
+    
+    return prediction, f"Confidence: {confidence:.1f}%", details
+
+
+def load_example():
+    if FEATURE_NAMES is None:
+        return [0.5] * 35
+    
+    try:
+        df = pd.read_csv(DATA_PATH, sep=';', decimal=',')
+        sample_row = df.sample(1)
+        # Return value for each feature, using 0.5 as default if feature not in dataset
+        result = []
+        for f in FEATURE_NAMES:
+            if f in df.columns:
+                val = float(sample_row[f].values[0])
+                # Clamp to valid slider range [0, 1]
+                val = max(0.0, min(1.0, val))
+                result.append(val)
+            else:
+                result.append(0.5)
+        return result
+    except Exception as e:
+        print(f"Error loading example: {e}")
+        return [0.5] * len(FEATURE_NAMES)
+
+
+def load_classification_example():
+    if CLASSIFICATION_FEATURE_NAMES is None:
+        return [0.5] * 40
+    
+    try:
+        df = pd.read_csv(DATA_PATH, sep=';', decimal=',')
+        sample_row = df.sample(1)
+        # Return value for each feature, using 0.5 as default if feature not in dataset
+        result = []
+        for f in CLASSIFICATION_FEATURE_NAMES:
+            if f in df.columns:
+                val = float(sample_row[f].values[0])
+                # Clamp to valid slider range [0, 1]
+                val = max(0.0, min(1.0, val))
+                result.append(val)
+            else:
+                result.append(0.5)
+        return result
+    except Exception as e:
+        print(f"Error loading classification example: {e}")
+        return [0.5] * len(CLASSIFICATION_FEATURE_NAMES)
+
+
+def create_interface():
+    if FEATURE_NAMES is None:
+        return gr.Interface(
+            fn=lambda: "Model not loaded",
+            inputs=[],
+            outputs="text",
+            title="Error: Model not loaded"
+        )
+    
+    inputs = []
+    for name in FEATURE_NAMES:
+        slider = gr.Slider(minimum=0, maximum=1, value=0.5, step=0.01, label=name.replace("_", " "))
+        inputs.append(slider)
+
+    classification_inputs = []
+    if CLASSIFICATION_FEATURE_NAMES is not None:
+        for name in CLASSIFICATION_FEATURE_NAMES:
+            slider = gr.Slider(minimum=0, maximum=1, value=0.5, step=0.01, label=name.replace("_", " "))
+            classification_inputs.append(slider)
+
+    description = """
+## Deep Squat Movement Assessment
+
+**How to use:**
+1. Adjust the sliders to input deviation values (0 = no deviation, 1 = maximum deviation)
+2. Click "Submit" to get your predicted score
+3. Or click "Load Random Example" to test with real data
+
+**Score Interpretation:**
+- 80-100%: Excellent form
+- 60-79%: Good form  
+- 40-59%: Average form
+- 0-39%: Needs improvement
+"""
+
+    classification_description = """
+## Body Region Classification
+
+**How to use:**
+1. Adjust the sliders to input deviation values (0 = no deviation, 1 = maximum deviation)
+2. Click "Predict Body Region" to identify where to focus improvements
+3. Or click "Load Random Example" to test with real data
+
+**Body Regions:** Upper Body, Lower Body
+"""
+
+    angle_features = [n for n in FEATURE_NAMES if "Angle" in n]
+    nasm_features = [n for n in FEATURE_NAMES if "NASM" in n]
+    time_features = [n for n in FEATURE_NAMES if "Time" in n]
+    other_features = [n for n in FEATURE_NAMES if "Angle" not in n and "NASM" not in n and "Time" not in n]
+    
+    angle_indices = [FEATURE_NAMES.index(f) for f in angle_features]
+    nasm_indices = [FEATURE_NAMES.index(f) for f in nasm_features]
+    time_indices = [FEATURE_NAMES.index(f) for f in time_features]
+    other_indices = [FEATURE_NAMES.index(f) for f in other_features]
+
+    if CLASSIFICATION_FEATURE_NAMES is not None:
+        class_angle_features = [n for n in CLASSIFICATION_FEATURE_NAMES if "Angle" in n]
+        class_nasm_features = [n for n in CLASSIFICATION_FEATURE_NAMES if "NASM" in n]
+        class_time_features = [n for n in CLASSIFICATION_FEATURE_NAMES if "Time" in n]
+        class_other_features = [n for n in CLASSIFICATION_FEATURE_NAMES if "Angle" not in n and "NASM" not in n and "Time" not in n]
+        class_angle_indices = [CLASSIFICATION_FEATURE_NAMES.index(f) for f in class_angle_features]
+        class_nasm_indices = [CLASSIFICATION_FEATURE_NAMES.index(f) for f in class_nasm_features]
+        class_time_indices = [CLASSIFICATION_FEATURE_NAMES.index(f) for f in class_time_features]
+        class_other_indices = [CLASSIFICATION_FEATURE_NAMES.index(f) for f in class_other_features]
+
+    with gr.Blocks(title="Deep Squat Assessment") as demo:
+        gr.Markdown("# Deep Squat Movement Assessment")
+        
+        with gr.Tabs():
+            with gr.TabItem("Movement Scoring"):
+                gr.Markdown(description)
+
+                with gr.Row():
+                    with gr.Column(scale=2):
+                        gr.Markdown("### Input Features")
+                        gr.Markdown(f"*{len(FEATURE_NAMES)} features loaded from champion model*")
+                        gr.Markdown("*Deviation values: 0 = perfect, 1 = maximum deviation*")
+
+                        with gr.Tabs():
+                            with gr.TabItem(f"Angle Deviations ({len(angle_indices)})"):
+                                for idx in angle_indices:
+                                    inputs[idx].render()
+
+                            with gr.TabItem(f"NASM Deviations ({len(nasm_indices)})"):
+                                for idx in nasm_indices:
+                                    inputs[idx].render()
+
+                            with gr.TabItem(f"Time Deviations ({len(time_indices)})"):
+                                for idx in time_indices:
+                                    inputs[idx].render()
+
+                            if other_indices:
+                                with gr.TabItem(f"Other ({len(other_indices)})"):
+                                    for idx in other_indices:
+                                        inputs[idx].render()
+
+                    with gr.Column(scale=1):
+                        gr.Markdown("### Results")
+                        score_output = gr.Textbox(label="Predicted Score")
+                        interp_output = gr.Textbox(label="Assessment")
+                        details_output = gr.Markdown(label="Details")
+
+                with gr.Row():
+                    submit_btn = gr.Button("Submit", variant="primary")
+                    example_btn = gr.Button("Load Random Example")
+                    clear_btn = gr.Button("Clear")
+
+                submit_btn.click(fn=predict_score, inputs=inputs, outputs=[score_output, interp_output, details_output])
+                example_btn.click(fn=load_example, inputs=[], outputs=inputs)
+                clear_btn.click(
+                    fn=lambda: [0.5] * len(FEATURE_NAMES) + ["", "", ""],
+                    inputs=[],
+                    outputs=inputs + [score_output, interp_output, details_output],
+                )
+
+            if CLASSIFICATION_FEATURE_NAMES is not None:
+                with gr.TabItem("Body Region Classification"):
+                    gr.Markdown(classification_description)
+
+                    with gr.Row():
+                        with gr.Column(scale=2):
+                            gr.Markdown("### Input Features")
+                            gr.Markdown(f"*{len(CLASSIFICATION_FEATURE_NAMES)} features for classification*")
+                            gr.Markdown("*Deviation values: 0 = perfect, 1 = maximum deviation*")
+
+                            with gr.Tabs():
+                                with gr.TabItem(f"Angle Deviations ({len(class_angle_indices)})"):
+                                    for idx in class_angle_indices:
+                                        classification_inputs[idx].render()
+
+                                with gr.TabItem(f"NASM Deviations ({len(class_nasm_indices)})"):
+                                    for idx in class_nasm_indices:
+                                        classification_inputs[idx].render()
+
+                                with gr.TabItem(f"Time Deviations ({len(class_time_indices)})"):
+                                    for idx in class_time_indices:
+                                        classification_inputs[idx].render()
+
+                                if class_other_indices:
+                                    with gr.TabItem(f"Other ({len(class_other_indices)})"):
+                                        for idx in class_other_indices:
+                                            classification_inputs[idx].render()
+
+                        with gr.Column(scale=1):
+                            gr.Markdown("### Results")
+                            class_output = gr.Textbox(label="Predicted Body Region")
+                            class_interp_output = gr.Textbox(label="Confidence")
+                            class_details_output = gr.Markdown(label="Details")
+
+                    with gr.Row():
+                        class_submit_btn = gr.Button("Predict Body Region", variant="primary")
+                        class_example_btn = gr.Button("Load Random Example")
+                        class_clear_btn = gr.Button("Clear")
+
+                    class_submit_btn.click(fn=predict_weakest_link, inputs=classification_inputs, outputs=[class_output, class_interp_output, class_details_output])
+                    class_example_btn.click(fn=load_classification_example, inputs=[], outputs=classification_inputs)
+                    class_clear_btn.click(
+                        fn=lambda: [0.5] * len(CLASSIFICATION_FEATURE_NAMES) + ["", "", ""],
+                        inputs=[],
+                        outputs=classification_inputs + [class_output, class_interp_output, class_details_output],
+                    )
+    
+    return demo
+
+
+demo = create_interface()
+
+if __name__ == "__main__":
+    demo.launch(share=False, server_name="0.0.0.0", server_port=7860)

app.py

@@ -2,28 +2,23 @@ import gradio as gr
 import pandas as pd
 import pickle
 import os
-import gdown
 
 # Get directory where this script is located
 SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
 
-# Google Drive file IDs for model downloads
-MODEL_GDRIVE_ID = "1ORlU0OOCBkWXVO2UFAkXaKtXfkOH7w1t"
-CLASSIFICATION_MODEL_GDRIVE_ID = "1qU6Q37CoToMxzBwori5V3_bonBIIb-K0"
-
-# Local paths - models loaded from A3/models/ directory
-MODEL_PATH = os.path.join(SCRIPT_DIR, "A3/models/champion_model_final_2.pkl")
-CLASSIFICATION_MODEL_PATH = os.path.join(SCRIPT_DIR, "A3/models/final_champion_model_A3.pkl")
-DATA_PATH = os.path.join(SCRIPT_DIR, "A3/A3_Data/train_dataset.csv")
-
-
-def download_from_gdrive(file_id, destination):
-    """Download a file from Google Drive using gdown."""
-    os.makedirs(os.path.dirname(destination), exist_ok=True)
-    url = f"https://drive.google.com/uc?id={file_id}"
-    gdown.download(url, destination, quiet=False)
-    print(f"Downloaded to {destination}")
-    return True
+# Local paths - models loaded from A4/models/ directory
+MODEL_PATH = os.path.join(
+    SCRIPT_DIR,
+    "A4/models/gDriveVersion/champion_model_final_2.pkl"
+)
+CLASSIFICATION_MODEL_PATH = os.path.join(
+    SCRIPT_DIR,
+    "A4/models/gDriveVersion/final_champion_model_A3.pkl"
+)
+DATA_PATH = os.path.join(
+    SCRIPT_DIR,
+    "A3/A3_Data/train_dataset.csv"
+)
 
 model = None
 FEATURE_NAMES = None
@@ -36,66 +31,57 @@ CLASSIFICATION_CLASSES = None
 CLASSIFICATION_METRICS = None
 
 BODY_REGION_RECOMMENDATIONS = {
-    'Upper Body': "Focus on shoulder mobility, thoracic spine extension, and keeping your head neutral.",
-    'Lower Body': "Work on hip mobility, ankle dorsiflexion, and knee tracking over toes."
+    'Upper Body': (
+        "Focus on shoulder mobility, thoracic spine extension, "
+        "and keeping your head neutral."),
+    'Lower Body': (
+        "Work on hip mobility, ankle dorsiflexion, "
+        "and knee tracking over toes.")
 }
 
 
 def load_champion_model():
     global model, FEATURE_NAMES, MODEL_METRICS
-    
-    # Download from Google Drive if not exists locally
-    if not os.path.exists(MODEL_PATH):
-        print(f"Model not found locally, downloading from Google Drive...")
-        try:
-            download_from_gdrive(MODEL_GDRIVE_ID, MODEL_PATH)
-        except Exception as e:
-            print(f"Failed to download model: {e}")
-            return False
-    
+
     if os.path.exists(MODEL_PATH):
         print(f"Loading champion model from {MODEL_PATH}")
         with open(MODEL_PATH, "rb") as f:
             artifact = pickle.load(f)
-        
+
         model = artifact["model"]
         FEATURE_NAMES = artifact["feature_columns"]
         MODEL_METRICS = artifact.get("test_metrics", {})
-        
+
         print(f"Model loaded: {len(FEATURE_NAMES)} features")
         print(f"Test R2: {MODEL_METRICS.get('r2', 'N/A')}")
         return True
-    
+
     print(f"Champion model not found at {MODEL_PATH}")
     return False
 
 
 def load_classification_model():
-    global classification_model, CLASSIFICATION_FEATURE_NAMES, CLASSIFICATION_CLASSES, CLASSIFICATION_METRICS
-    
-    # Download from Google Drive if not exists locally
-    if not os.path.exists(CLASSIFICATION_MODEL_PATH):
-        print(f"Classification model not found locally, downloading from Google Drive...")
-        try:
-            download_from_gdrive(CLASSIFICATION_MODEL_GDRIVE_ID, CLASSIFICATION_MODEL_PATH)
-        except Exception as e:
-            print(f"Failed to download classification model: {e}")
-            return False
-    
+    global classification_model
+    global CLASSIFICATION_FEATURE_NAMES
+    global CLASSIFICATION_CLASSES
+    global CLASSIFICATION_METRICS
+
     if os.path.exists(CLASSIFICATION_MODEL_PATH):
         print(f"Loading classification model from {CLASSIFICATION_MODEL_PATH}")
         with open(CLASSIFICATION_MODEL_PATH, "rb") as f:
             artifact = pickle.load(f)
-        
+
         classification_model = artifact["model"]
         CLASSIFICATION_FEATURE_NAMES = artifact["feature_columns"]
         CLASSIFICATION_CLASSES = artifact["classes"]
         CLASSIFICATION_METRICS = artifact.get("test_metrics", {})
-        
-        print(f"Classification model loaded: {len(CLASSIFICATION_FEATURE_NAMES)} features")
+
+        len_features = len(CLASSIFICATION_FEATURE_NAMES)
+        print(
+            f"Classification model loaded: {len_features} features")
         print(f"Classes: {CLASSIFICATION_CLASSES}")
         return True
-    
+
     print(f"Classification model not found at {CLASSIFICATION_MODEL_PATH}")
     return False
 
@@ -107,7 +93,7 @@ load_classification_model()
 def predict_score(*feature_values):
     if model is None:
         return "Error", "Model not loaded", ""
-    
+
     features_df = pd.DataFrame([feature_values], columns=FEATURE_NAMES)
     raw_score = model.predict(features_df)[0]
     score = max(0, min(1, raw_score)) * 100
@@ -124,8 +110,9 @@ def predict_score(*feature_values):
     r2 = MODEL_METRICS.get('r2', 'N/A')
     correlation = MODEL_METRICS.get('correlation', 'N/A')
     r2_str = f"{r2:.4f}" if isinstance(r2, (int, float)) else str(r2)
-    corr_str = f"{correlation:.4f}" if isinstance(correlation, (int, float)) else str(correlation)
-    
+    corr_str = f"{correlation:.4f}" if isinstance(
+        correlation, (int, float)) else str(correlation)
+
     details = f"""
 ### Prediction Details
 - **Raw Model Output:** {raw_score:.4f}
@@ -145,25 +132,31 @@ def predict_score(*feature_values):
 def predict_weakest_link(*feature_values):
     if classification_model is None:
         return "Error", "Model not loaded", ""
-    
-    features_df = pd.DataFrame([feature_values], columns=CLASSIFICATION_FEATURE_NAMES)
-    
+
+    features_df = pd.DataFrame(
+        [feature_values], columns=CLASSIFICATION_FEATURE_NAMES)
+
     prediction = classification_model.predict(features_df)[0]
     probabilities = classification_model.predict_proba(features_df)[0]
-    
+
     class_probs = list(zip(CLASSIFICATION_CLASSES, probabilities))
     class_probs.sort(key=lambda x: x[1], reverse=True)
-    
+
     confidence = max(probabilities) * 100
-    recommendation = BODY_REGION_RECOMMENDATIONS.get(prediction, "Focus on exercises that strengthen this region.")
-    
+    recommendation = BODY_REGION_RECOMMENDATIONS.get(
+        prediction, "Focus on exercises that strengthen this region.")
+
     accuracy = CLASSIFICATION_METRICS.get('accuracy', 'N/A')
     f1_weighted = CLASSIFICATION_METRICS.get('f1_weighted', 'N/A')
-    acc_str = f"{accuracy:.2%}" if isinstance(accuracy, (int, float)) else str(accuracy)
-    f1_str = f"{f1_weighted:.2%}" if isinstance(f1_weighted, (int, float)) else str(f1_weighted)
-    
-    predictions_list = "\n".join([f"{i+1}. **{cp[0]}** - {cp[1]*100:.1f}%" for i, cp in enumerate(class_probs)])
-    
+    acc_str = f"{accuracy:.2%}" if isinstance(
+        accuracy, (int, float)) else str(accuracy)
+    f1_str = f"{f1_weighted:.2%}" if isinstance(
+        f1_weighted, (int, float)) else str(f1_weighted)
+
+    predictions_list = "\n".join(
+        [f"{i+1}. **{cp[0]}** - {cp[1]*100:.1f}%" for i, cp in enumerate(class_probs)]
+    )
+
     details = f"""
 ### Prediction Details
 - **Predicted Body Region:** {prediction}
@@ -179,18 +172,18 @@ def predict_weakest_link(*feature_values):
 - **Test Accuracy:** {acc_str}
 - **Test F1 (weighted):** {f1_str}
     """
-    
+
     return prediction, f"Confidence: {confidence:.1f}%", details
 
 
 def load_example():
     if FEATURE_NAMES is None:
         return [0.5] * 35
-    
+
     try:
         df = pd.read_csv(DATA_PATH, sep=';', decimal=',')
         sample_row = df.sample(1)
-        # Return value for each feature, using 0.5 as default if feature not in dataset
+        # Return value for each feature
         result = []
         for f in FEATURE_NAMES:
             if f in df.columns:
@@ -198,6 +191,7 @@ def load_example():
                 # Clamp to valid slider range [0, 1]
                 val = max(0.0, min(1.0, val))
                 result.append(val)
+            # using 0.5 as default if feature not in dataset
             else:
                 result.append(0.5)
         return result
@@ -209,11 +203,11 @@ def load_example():
 def load_classification_example():
     if CLASSIFICATION_FEATURE_NAMES is None:
         return [0.5] * 40
-    
+
     try:
         df = pd.read_csv(DATA_PATH, sep=';', decimal=',')
         sample_row = df.sample(1)
-        # Return value for each feature, using 0.5 as default if feature not in dataset
+        # Return value for each feature
         result = []
         for f in CLASSIFICATION_FEATURE_NAMES:
             if f in df.columns:
@@ -221,6 +215,7 @@ def load_classification_example():
                 # Clamp to valid slider range [0, 1]
                 val = max(0.0, min(1.0, val))
                 result.append(val)
+            # using 0.5 as default if feature not in dataset
             else:
                 result.append(0.5)
         return result
@@ -237,29 +232,32 @@ def create_interface():
             outputs="text",
             title="Error: Model not loaded"
         )
-    
+
     inputs = []
     for name in FEATURE_NAMES:
-        slider = gr.Slider(minimum=0, maximum=1, value=0.5, step=0.01, label=name.replace("_", " "))
+        slider = gr.Slider(minimum=0, maximum=1, value=0.5,
+                           step=0.01, label=name.replace("_", " "))
         inputs.append(slider)
 
     classification_inputs = []
     if CLASSIFICATION_FEATURE_NAMES is not None:
         for name in CLASSIFICATION_FEATURE_NAMES:
-            slider = gr.Slider(minimum=0, maximum=1, value=0.5, step=0.01, label=name.replace("_", " "))
+            slider = gr.Slider(minimum=0, maximum=1, value=0.5,
+                               step=0.01, label=name.replace("_", " "))
             classification_inputs.append(slider)
 
     description = """
 ## Deep Squat Movement Assessment
 
 **How to use:**
-1. Adjust the sliders to input deviation values (0 = no deviation, 1 = maximum deviation)
+1. Adjust the sliders to input deviation values
+    (0 = no deviation, 1 = maximum deviation)
 2. Click "Submit" to get your predicted score
 3. Or click "Load Random Example" to test with real data
 
 **Score Interpretation:**
 - 80-100%: Excellent form
-- 60-79%: Good form  
+- 60-79%: Good form
 - 40-59%: Average form
 - 0-39%: Needs improvement
 """
@@ -278,26 +276,35 @@ def create_interface():
     angle_features = [n for n in FEATURE_NAMES if "Angle" in n]
     nasm_features = [n for n in FEATURE_NAMES if "NASM" in n]
     time_features = [n for n in FEATURE_NAMES if "Time" in n]
-    other_features = [n for n in FEATURE_NAMES if "Angle" not in n and "NASM" not in n and "Time" not in n]
-    
+    other_features = [
+        n for n in FEATURE_NAMES if "Angle" not in n and "NASM" not in n and "Time" not in n]
+
     angle_indices = [FEATURE_NAMES.index(f) for f in angle_features]
     nasm_indices = [FEATURE_NAMES.index(f) for f in nasm_features]
     time_indices = [FEATURE_NAMES.index(f) for f in time_features]
     other_indices = [FEATURE_NAMES.index(f) for f in other_features]
 
     if CLASSIFICATION_FEATURE_NAMES is not None:
-        class_angle_features = [n for n in CLASSIFICATION_FEATURE_NAMES if "Angle" in n]
-        class_nasm_features = [n for n in CLASSIFICATION_FEATURE_NAMES if "NASM" in n]
-        class_time_features = [n for n in CLASSIFICATION_FEATURE_NAMES if "Time" in n]
-        class_other_features = [n for n in CLASSIFICATION_FEATURE_NAMES if "Angle" not in n and "NASM" not in n and "Time" not in n]
-        class_angle_indices = [CLASSIFICATION_FEATURE_NAMES.index(f) for f in class_angle_features]
-        class_nasm_indices = [CLASSIFICATION_FEATURE_NAMES.index(f) for f in class_nasm_features]
-        class_time_indices = [CLASSIFICATION_FEATURE_NAMES.index(f) for f in class_time_features]
-        class_other_indices = [CLASSIFICATION_FEATURE_NAMES.index(f) for f in class_other_features]
+        class_angle_features = [
+            n for n in CLASSIFICATION_FEATURE_NAMES if "Angle" in n]
+        class_nasm_features = [
+            n for n in CLASSIFICATION_FEATURE_NAMES if "NASM" in n]
+        class_time_features = [
+            n for n in CLASSIFICATION_FEATURE_NAMES if "Time" in n]
+        class_other_features = [
+            n for n in CLASSIFICATION_FEATURE_NAMES if "Angle" not in n and "NASM" not in n and "Time" not in n]
+        class_angle_indices = [CLASSIFICATION_FEATURE_NAMES.index(
+            f) for f in class_angle_features]
+        class_nasm_indices = [CLASSIFICATION_FEATURE_NAMES.index(
+            f) for f in class_nasm_features]
+        class_time_indices = [CLASSIFICATION_FEATURE_NAMES.index(
+            f) for f in class_time_features]
+        class_other_indices = [CLASSIFICATION_FEATURE_NAMES.index(
+            f) for f in class_other_features]
 
     with gr.Blocks(title="Deep Squat Assessment") as demo:
         gr.Markdown("# Deep Squat Movement Assessment")
-        
+
         with gr.Tabs():
             with gr.TabItem("Movement Scoring"):
                 gr.Markdown(description)
@@ -305,8 +312,10 @@ def create_interface():
                 with gr.Row():
                     with gr.Column(scale=2):
                         gr.Markdown("### Input Features")
-                        gr.Markdown(f"*{len(FEATURE_NAMES)} features loaded from champion model*")
-                        gr.Markdown("*Deviation values: 0 = perfect, 1 = maximum deviation*")
+                        gr.Markdown(
+                            f"*{len(FEATURE_NAMES)} features loaded from champion model*")
+                        gr.Markdown(
+                            "*Deviation values: 0 = perfect, 1 = maximum deviation*")
 
                         with gr.Tabs():
                             with gr.TabItem(f"Angle Deviations ({len(angle_indices)})"):
@@ -334,15 +343,17 @@ def create_interface():
 
                 with gr.Row():
                     submit_btn = gr.Button("Submit", variant="primary")
-                    example_btn = gr.Button("Load Random Example")
+                    example_btn = gr.Button("Load Sample")
                     clear_btn = gr.Button("Clear")
 
-                submit_btn.click(fn=predict_score, inputs=inputs, outputs=[score_output, interp_output, details_output])
+                submit_btn.click(fn=predict_score, inputs=inputs, outputs=[
+                                 score_output, interp_output, details_output])
                 example_btn.click(fn=load_example, inputs=[], outputs=inputs)
                 clear_btn.click(
                     fn=lambda: [0.5] * len(FEATURE_NAMES) + ["", "", ""],
                     inputs=[],
-                    outputs=inputs + [score_output, interp_output, details_output],
+                    outputs=inputs + [score_output,
+                                      interp_output, details_output],
                 )
 
             if CLASSIFICATION_FEATURE_NAMES is not None:
@@ -352,8 +363,10 @@ def create_interface():
                     with gr.Row():
                         with gr.Column(scale=2):
                             gr.Markdown("### Input Features")
-                            gr.Markdown(f"*{len(CLASSIFICATION_FEATURE_NAMES)} features for classification*")
-                            gr.Markdown("*Deviation values: 0 = perfect, 1 = maximum deviation*")
+                            gr.Markdown(
+                                f"*{len(CLASSIFICATION_FEATURE_NAMES)} features for classification*")
+                            gr.Markdown(
+                                "*Deviation values: 0 = perfect, 1 = maximum deviation*")
 
                             with gr.Tabs():
                                 with gr.TabItem(f"Angle Deviations ({len(class_angle_indices)})"):
@@ -375,23 +388,30 @@ def create_interface():
 
                         with gr.Column(scale=1):
                             gr.Markdown("### Results")
-                            class_output = gr.Textbox(label="Predicted Body Region")
-                            class_interp_output = gr.Textbox(label="Confidence")
+                            class_output = gr.Textbox(
+                                label="Predicted Body Region")
+                            class_interp_output = gr.Textbox(
+                                label="Confidence")
                             class_details_output = gr.Markdown(label="Details")
 
                     with gr.Row():
-                        class_submit_btn = gr.Button("Predict Body Region", variant="primary")
+                        class_submit_btn = gr.Button(
+                            "Predict Body Region", variant="primary")
                         class_example_btn = gr.Button("Load Random Example")
                         class_clear_btn = gr.Button("Clear")
 
-                    class_submit_btn.click(fn=predict_weakest_link, inputs=classification_inputs, outputs=[class_output, class_interp_output, class_details_output])
-                    class_example_btn.click(fn=load_classification_example, inputs=[], outputs=classification_inputs)
+                    class_submit_btn.click(fn=predict_weakest_link, inputs=classification_inputs, outputs=[
+                                           class_output, class_interp_output, class_details_output])
+                    class_example_btn.click(fn=load_classification_example, inputs=[
+                    ], outputs=classification_inputs)
+                    output_list = [class_output, class_interp_output, class_details_output]
                     class_clear_btn.click(
-                        fn=lambda: [0.5] * len(CLASSIFICATION_FEATURE_NAMES) + ["", "", ""],
+                        fn=lambda: [
+                            0.5] * len(CLASSIFICATION_FEATURE_NAMES) + ["", "", ""],
                         inputs=[],
-                        outputs=classification_inputs + [class_output, class_interp_output, class_details_output],
+                        outputs=classification_inputs + output_list,
                     )
-    
+
     return demo