report and app

A3/app.py

@@ -0,0 +1,399 @@
+import gradio as gr
+import pandas as pd
+import pickle
+import os
+
+# Get directory where this script is located
+SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
+
+MODEL_PATH = os.path.join(SCRIPT_DIR, "models/champion_model_final_2.pkl")
+CLASSIFICATION_MODEL_PATH = os.path.join(SCRIPT_DIR, "models/classification_champion.pkl")
+DATA_PATH = os.path.join(SCRIPT_DIR, "A3_Data/train_dataset.csv")
+
+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
+    
+    possible_paths = [
+        MODEL_PATH,
+        os.path.join(SCRIPT_DIR, "../A2/models/champion_model_final_2.pkl"),
+    ]
+    
+    for path in possible_paths:
+        if os.path.exists(path):
+            print(f"Loading champion model from {path}")
+            with open(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 successfully")
+            print(f"Features: {len(FEATURE_NAMES)} columns")
+            print(f"Test R2: {MODEL_METRICS.get('r2', 'N/A')}")
+            return True
+    return False
+
+
+def load_classification_model():
+    global classification_model, CLASSIFICATION_FEATURE_NAMES, CLASSIFICATION_CLASSES, 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")
+        print(f"Classes: {CLASSIFICATION_CLASSES}")
+        return True
+    
+    print("Classification model not found")
+    return False
+
+
+load_champion_model()
+load_classification_model()
+
+
+# prediction function
+def predict_score(*feature_values):
+    if model is None:
+        return "Error", "Model not loaded"
+    
+    # Convert inputs to dataframe with correct feature names
+    features_df = pd.DataFrame([feature_values], columns=FEATURE_NAMES)
+    
+    raw_score = model.predict(features_df)[0]
+
+    # score to valid range and change to %
+    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"
+
+    # Create output
+    r2 = MODEL_METRICS.get('r2', 'N/A')
+    correlation = MODEL_METRICS.get('correlation', 'N/A')
+    
+    # Format metrics
+    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
+
+
+# classification prediction function
+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]
+    
+    # Get top predictions
+    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)
+    
+    # Build prediction list
+    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=',')
+        available_features = [f for f in FEATURE_NAMES if f in df.columns]
+        sample = df[available_features].sample(1).values[0]
+        return [float(x) for x in sample]
+    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=',')
+        available_features = [f for f in CLASSIFICATION_FEATURE_NAMES if f in df.columns]
+        sample = df[available_features].sample(1).values[0]
+        return [float(x) for x in sample]
+    except Exception as e:
+        print(f"Error loading classification example: {e}")
+        return [0.5] * len(CLASSIFICATION_FEATURE_NAMES)
+
+
+# create gradio interface
+def create_interface():
+    if FEATURE_NAMES is None:
+        return gr.Interface(
+            fn=lambda: "Model not loaded",
+            inputs=[],
+            outputs="text",
+            title="Error: Model not loaded"
+        )
+    
+    # Create input sliders for scoring features
+    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)
+
+    # Create input sliders for classification features
+    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)
+
+    # Build the interface
+    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
+"""
+
+    # features into categories for scoring
+    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]
+    
+    # Get indices for each category
+    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]
+
+    # Classification feature categories
+    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_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]
+
+    # Create the main interface
+    with gr.Blocks(title="Deep Squat Assessment") as demo:
+        gr.Markdown("# Deep Squat Movement Assessment")
+        
+        with gr.Tabs():
+            # Tab 1: Movement Scoring (original A2 functionality)
+            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()
+
+                    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],
+                )
+
+            # weakest link classification
+            if CLASSIFICATION_FEATURE_NAMES is not None:
+                with gr.TabItem("Weakest Link 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()
+
+                        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
+
+
+# Create the interface
+demo = create_interface()
+
+if __name__ == "__main__":
+    demo.launch(
+        share=False,
+        server_name="0.0.0.0",
+        server_port=7860,
+    )