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app.py

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+import gradio as gr
+import joblib
+import numpy as np
+from huggingface_hub import hf_hub_download
+
+# Download models from Hugging Face Hub
+svr_path = hf_hub_download(repo_id="iamomtiwari/Nutrition-regression-models", filename="svr_model.pkl")
+lr_path = hf_hub_download(repo_id="iamomtiwari/Nutrition-regression-models", filename="linear_regression.pkl")
+
+# Load models
+svr_model = joblib.load(svr_path)
+linear_reg = joblib.load(lr_path)
+
+# Selected 10 important features
+features = ['Caloric Value', 'Fat', 'Saturated Fats', 'Carbohydrates', 'Sugars',
+            'Protein', 'Cholesterol', 'Sodium', 'Calcium', 'Iron']
+
+# Define prediction function
+def predict(model_name, *inputs):
+    input_data = np.array([inputs]).reshape(1, -1)
+    
+    if model_name == "SVR":
+        prediction = svr_model.predict(input_data)[0]
+    else:
+        prediction = linear_reg.predict(input_data)[0]
+    
+    return round(prediction, 4)
+
+# Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Nutrition Density Prediction")
+    
+    model_choice = gr.Radio(["SVR", "Linear Regression"], label="Select Model")
+    input_widgets = [gr.Slider(minimum=0, maximum=100, step=0.1, label=feature) for feature in features]
+    predict_button = gr.Button("Predict")
+    clear_button = gr.Button("Clear")
+    output_label = gr.Textbox(label="Prediction")
+    
+    predict_button.click(predict, inputs=[model_choice] + input_widgets, outputs=output_label)
+    
+    # Reset sliders to their default value (0) on "Clear"
+    def reset_sliders():
+        return [0] * len(features)
+    
+    clear_button.click(reset_sliders, inputs=[], outputs=input_widgets)
+
+# Run the app
+demo.launch()

linear_regression.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:de35ed6ba09c115498091604224187d4e2bda199f8edd9b2027f562d6fabcc48
+size 1876

requirements.txt

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+gradio
+joblib
+pandas
+scikit-learn
+huggingface_hub

svr_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2fba68204dc51a2026d8541ba7132ba7cb47d11da4a25f6e292b33bf0e1769e0
+size 8370