Create AI Size Advisor.py

AI Size Advisor.py

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+import gradio as gr
+import numpy as np
+from sklearn.linear_model import LinearRegression
+from sklearn.preprocessing import LabelEncoder
+
+# Sample dataset (body measurements and corresponding size)
+# You will replace this with your actual dataset
+data = {
+    'chest': [34, 36, 38, 40, 42],
+    'waist': [28, 30, 32, 34, 36],
+    'hip': [36, 38, 40, 42, 44],
+    'size': ['S', 'M', 'L', 'XL', 'XXL']
+}
+
+# Train a LabelEncoder for sizes
+label_encoder = LabelEncoder()
+data['size_encoded'] = label_encoder.fit_transform(data['size'])
+
+# Prepare features and target
+X = np.array([data['chest'], data['waist'], data['hip']]).T
+y = data['size_encoded']  # Using encoded sizes as the target
+
+# Initialize and train the model
+model = LinearRegression()
+model.fit(X, y)
+
+# Function to predict size based on measurements
+def predict_size(chest, waist, hip):
+    input_features = np.array([[chest, waist, hip]])
+    predicted_size_encoded = model.predict(input_features)
+
+    # Clamp the predicted size to ensure it's within the valid range of labels
+    predicted_size_encoded_clamped = np.clip(predicted_size_encoded, 0, len(label_encoder.classes_) - 1)
+
+    # Convert the numeric prediction back to the original size
+    predicted_size = label_encoder.inverse_transform(predicted_size_encoded_clamped.astype(int))
+
+    return predicted_size[0]
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=predict_size, 
+    inputs=[
+        gr.Slider(minimum=30, maximum=50, step=1, label="Chest (inches)"),
+        gr.Slider(minimum=20, maximum=40, step=1, label="Waist (inches)"),
+        gr.Slider(minimum=30, maximum=50, step=1, label="Hip (inches)")
+    ],
+    outputs="text",
+    live=True,
+    title="AI Size Advisor",
+    description="Enter your body measurements to get an accurate clothing size recommendation based on past purchase data."
+)
+
+# Launch the interface
+interface.launch()