updated main app for hf

app.py

@@ -1,12 +1,234 @@
 import gradio as gr
+import pandas as pd
+import numpy as np
+import pickle
+import os
 
-similar_app = "https://huggingface.co/spaces/sems/pose-think"
+# Paths for HuggingFace deployment (running from root folder)
+MODEL_PATH = "A2/models/champion_model_final.pkl"
+DATA_PATH = "A2/A2_dataset.csv"
 
-def greet(name):
-    return "Hello " + name + "!!"
+model = None
+FEATURE_NAMES = None
+MODEL_METRICS = None
 
-description_string = """
-Simple Demo app that should be developed into a Movement Assessment similar to the [posture analysis](https://huggingface.co/spaces/sems/pose-think).
+
+def load_champion_model():
+    global model, FEATURE_NAMES, MODEL_METRICS
+    
+    possible_paths = [
+        MODEL_PATH,
+        "A2/models/champion_model_final.pkl",
+        "../A2/models/champion_model_final.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
+    
+    print("champion model not found")
+    return False
+
+
+load_champion_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
+
+
+# load example for tesitng
+def load_example():
+    if FEATURE_NAMES is None:
+        return [0.5] * 35
+    
+    try:
+        possible_paths = [
+            DATA_PATH,
+            "A2/A2_dataset.csv",
+            "../A2/A2_dataset.csv",
+            "../Datasets_all/A2_dataset_80.csv",
+        ]
+        
+        df = None
+        for path in possible_paths:
+            if os.path.exists(path):
+                df = pd.read_csv(path)
+                break
+
+        # Get a random row with only the features we need
+        available_features = [f for f in FEATURE_NAMES if f in df.columns]
+        sample = df[available_features].sample(1).values[0]
+        # Convert to float list to ensure proper types for Gradio sliders
+        return [float(x) for x in sample]
+    except Exception as e:
+        print(f"Error loading example: {e}")
+        return [0.5] * len(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 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)
+
+    # 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
 """
-iface = gr.Interface(fn=greet, inputs="text", outputs="text", description=description_string)
-iface.launch()
+
+    # features into categories
+    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]
+
+    # Create the main interface
+    with gr.Blocks(title="Deep Squat Assessment") as demo:
+        gr.Markdown("# Deep Squat Movement Assessment")
+        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],
+        )
+    
+    return demo
+
+
+# Create the interface
+demo = create_interface()
+
+if __name__ == "__main__":
+    demo.launch(
+        share=False,
+        server_name="0.0.0.0",
+        server_port=7860,
+    )