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final_app.py

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+import gradio as gr
+import pickle
+import pandas as pd
+from sklearn.compose import ColumnTransformer
+from sklearn.pipeline import Pipeline
+import numpy as np
+import pandas as pd
+from sklearn.preprocessing import StandardScaler
+from sklearn.preprocessing import OneHotEncoder
+
+# Load the model and preprocessor
+model = pickle.load(open('model.pkl', 'rb'))
+preprocessor = pickle.load(open('preprocess_pipeline.pkl', 'rb'))
+
+
+
+def call_gradio(Sex, Equipment, Age, BodyweightKg, BestSquatKg, Bestbenchkg):
+    # Convert inputs to appropriate data types
+    Age = float(Age)
+    BodyweightKg = float(BodyweightKg)
+    BestSquatKg = float(BestSquatKg)
+    Bestbenchkg = float(Bestbenchkg)
+    
+    # Create a DataFrame with the input data
+    df_x = pd.DataFrame({
+        'Sex': [Sex],
+        'Equipment': [Equipment],
+        'Age': [Age],
+        'BodyweightKg': [BodyweightKg],
+        'BestSquatKg': [BestSquatKg],
+        'Bestbench(kg)': [Bestbenchkg],
+    })
+    
+    # Define the categorical and numerical feature lists
+    categorical_features = ['Sex', 'Equipment']
+    numerical_features = df_x.drop(categorical_features, axis=1).columns.tolist()
+    
+    # Ensure that the preprocessor is fitted
+    if not hasattr(preprocessor, 'transformers_'):
+        raise RuntimeError("The preprocessor is not fitted yet. Fit the preprocessor before calling this function.")
+    
+    
+    # Transform the data using the preprocessor
+    X_processed = preprocessor.transform(df_x)
+    
+    # Access the OneHotEncoder directly to get feature names
+    ohe = preprocessor.named_transformers_['cat'].named_steps['onehot']
+    cat_feature_names = ohe.get_feature_names_out(categorical_features)
+    
+    # Combine numerical features and one-hot encoded feature names
+    all_feature_names = numerical_features + list(cat_feature_names)
+    
+    # Create a DataFrame with processed features
+    X_processed_df = pd.DataFrame(X_processed, columns=all_feature_names)
+    
+    # Predict using the model
+    y_pred = model.predict(X_processed_df)
+    
+    max_kg = int(y_pred[0])
+    return max_kg
+
+# Define Gradio inputs and outputs
+sex_dropdown = gr.Dropdown(choices=['M', 'F'], label="Sex", info="Select Male or Female")
+equipment_dropdown = gr.Dropdown(choices=['Raw', 'Wraps', 'Single-ply', 'Multi-ply'], label="Equipment", info="Select the equipment used for the competition.")
+age_textbox = gr.Textbox(lines=1, label="Age", info="Enter your Age")
+bodyweight_kg_textbox = gr.Textbox(lines=1, label="BodyweightKg", info="Enter your Bodyweight in Kg")
+best_squat_kg_textbox = gr.Textbox(lines=1, label="BestSquatKg", info="Enter your Best Squat in Kg")
+best_bench_kg_textbox = gr.Textbox(lines=1, label="BestbenchKg", info="Enter your Best Bench in Kg")
+
+# Custom description with image and footer
+description = """
+<div style='text-align: center;'>
+    <h1 style='font-size: 50px;'>PowerLift Muscle Map</h1> <!-- Title with increased font size -->
+    <p>Use this model to estimate your best Deadlift (kg) based on your selected features. Input your details and see the predicted weight (kg) you could lift.</p>
+    <p><strong>Output:</strong> Estimated Best Deadlift (kg)</p>
+    <br>
+    <p style='font-size: 10px; color: #555;'>❤️ PDS</p>
+</div>
+"""
+
+# Create and launch Gradio interface
+iface = gr.Interface(
+    fn=call_gradio,
+    inputs=[sex_dropdown, equipment_dropdown, age_textbox, bodyweight_kg_textbox, best_squat_kg_textbox, best_bench_kg_textbox],
+    outputs="number",
+    description=description,
+)
+
+iface.launch()

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b07ccdd1ad7e48ecfdaee76e7d8ac51374809937f03178c0c82d49d6ede0df1f
+size 239405

preprocess_pipeline.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9ab474759d5dceeecd966e7ec35eb4839f07c6e4f43a2929872ad75f001c170b
+size 1995