Add app.py for Gradio Space

app.py

@@ -0,0 +1,69 @@
+
+import gradio as gr
+import joblib
+from huggingface_hub import hf_hub_download
+import numpy as np
+
+# Define the repository ID and the model filename
+# Make sure this matches your deployed model on Hugging Face
+repo_id = "farooqhasanDA/logistic-regression-sklearn-model"
+model_filename = "models/logistic_regression_model.joblib"
+
+# Global variable to store the loaded model
+loaded_model = None
+
+def predict_proba_wrapper(feature1_num, feature2_text, feature3_dropdown, feature4_checkbox):
+    global loaded_model
+
+    # Download and load the model if not already loaded
+    if loaded_model is None:
+        print("Downloading and loading model for the first time...")
+        try:
+            model_path_local = hf_hub_download(repo_id=repo_id, filename=model_filename)
+            loaded_model = joblib.load(model_path_local)
+            print("Model loaded successfully.")
+        except Exception as e:
+            return f"Error loading model: {e}"
+
+    try:
+        # Convert text to a numerical value (e.g., length, or some simple encoding)
+        f2_val = len(feature2_text) if feature2_text else 0
+        # Convert dropdown choice to a numerical value
+        f3_val = {'Option A': 0, 'Option B': 1, 'Option C': 2}.get(feature3_dropdown, 0)
+        # Convert checkbox (boolean) to 0 or 1
+        f4_val = 1 if feature4_checkbox else 0
+
+        input_array = np.array([[float(feature1_num), float(f2_val), float(f3_val), float(f4_val)]])
+
+        # Make a prediction and get prediction probabilities
+        prediction = loaded_model.predict(input_array)
+        probabilities = loaded_model.predict_proba(input_array)
+
+        # Return formatted string
+        return (f"Prediction: Class {prediction[0]}, "
+                f"Probabilities: Class 0 = {probabilities[0][0]:.4f}, Class 1 = {probabilities[0][1]:.4f}")
+    except ValueError:
+        return "Error: Please ensure Feature 1 is a valid number."
+    except Exception as e:
+        return f"Prediction error: {e}"
+
+
+# Create gradio input components with different types
+inputs = [
+    gr.Number(label="Numerical Feature 1 (e.g., 0.5)", value=0.5),
+    gr.Textbox(label="Text Feature 2 (e.g., 'Hello world')", value="Sample text"),
+    gr.Dropdown(choices=["Option A", "Option B", "Option C"], label="Categorical Feature 3"),
+    gr.Checkbox(label="Boolean Feature 4 (checked/unchecked)", value=True)
+]
+
+# Create a gradio output component
+output = gr.Textbox(label="Prediction Result")
+
+# Instantiate gr.Interface
+iface = gr.Interface(fn=predict_proba_wrapper, inputs=inputs, outputs=output,
+                     title="Logistic Regression Model Predictor (Hosted on HF Space)",
+                     description="Enter different types of features to get a binary classification prediction and probabilities. 
+(Note: Text/Dropdown/Checkbox inputs are converted to numbers for this demo model.)")
+
+# Launch the Gradio interface
+iface.launch(debug=True)