sync to remote

app.py

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+import gradio as gr
+import numpy as np
+import os
+from huggingface_hub import hf_hub_download
+import joblib  # Import joblib directly
+import pandas as pd
+
+# Load the pre-trained model from Hugging Face
+hf_token = os.getenv("HF_TOKEN")
+model_path = hf_hub_download(repo_id="wvsu-dti-aidev-team/advertising_knn_regressor_model", filename="decision_tree_regressor.pkl", use_auth_token=hf_token)
+model = joblib.load(model_path)
+
+def predict_sales(tv, radio, newspaper):
+    # Create a DataFrame with the same feature names as the training data
+    input_data = pd.DataFrame([[tv, radio, newspaper]], columns=['TV', 'Radio', 'Newspaper'])
+    
+    # Get the predicted sales
+    predicted_sales = model.predict(input_data)
+    
+    # Discussion on the projected sales result
+    discussion = f"Based on the advertising spending, the projected sales are approximately {predicted_sales[0] * 10000:.2f} Pesos. " \
+                 f"Investing more in TV advertising tends to have a significant impact on sales, followed by Radio and Newspaper. " \
+                 f"Optimizing the budget allocation across these channels can help maximize sales."
+
+    return predicted_sales[0], discussion
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict_sales,
+    inputs=[
+        gr.Number(label="TV Advertising Spend (x 10,000 Pesos)"),
+        gr.Number(label="Radio Advertising Spend (x 10,000 Pesos)"),
+        gr.Number(label="Newspaper Advertising Spend (x 10,000 Pesos)")
+    ],
+    outputs=[
+        gr.Textbox(label="Predicted Sales"),
+        gr.Textbox(label="Discussion")
+    ],
+    title="Advertising Spend to Sales Prediction",
+    description="Enter the advertising spending on TV, Radio, and Newspaper to predict the sales."
+)
+
+# Launch the app
+iface.launch()

requirements.txt

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