Update app.py

app.py

@@ -1,70 +1,35 @@
-from flask import Flask, render_template, request
 import joblib
 import numpy as np
 from sklearn import datasets
+import gradio as gr
 import os
 
-app = Flask(__name__)
+# Load the model and class names
+if os.path.exists('new_iris_model.pkl'):
+    model = joblib.load('new_iris_model.pkl')
+else:
+    model = joblib.load('iris_model.pkl')
 
-# Load the model and iris dataset for target names
-print("Starting Iris Flower Classification Application...")
-try:
-    # Try loading the new model first, then fall back to the original model
-    if os.path.exists('new_iris_model.pkl'):
-        model = joblib.load('new_iris_model.pkl')
-        print("Successfully loaded new_iris_model.pkl")
-    else:
-        model = joblib.load('iris_model.pkl')
-        print("Successfully loaded iris_model.pkl")
-    
-    # Load iris dataset to get target names
-    iris = datasets.load_iris()
-    class_names = iris.target_names
-    print(f"Class names: {class_names}")
-    
-except Exception as e:
-    print(f"Error loading model or dataset: {e}")
-    # Fallback to class names if model fails to load
-    class_names = ['setosa', 'versicolor', 'virginica']
-    model = None
+iris = datasets.load_iris()
+class_names = iris.target_names
 
-@app.route('/')
-def form():
-    return render_template('form.html')
+# Define prediction function
+def predict_species(sepal_length, sepal_width, petal_length, petal_width):
+    features = np.array([[sepal_length, sepal_width, petal_length, petal_width]])
+    prediction = model.predict(features)[0]
+    return f"Iris {class_names[prediction]}"
 
-@app.route('/predict', methods=['POST'])
-def predict():
-    try:
-        if model is None:
-            raise Exception("Model failed to load")
-            
-        # Get form values as float
-        features = [
-            float(request.form['sepal_length']),
-            float(request.form['sepal_width']),
-            float(request.form['petal_length']),
-            float(request.form['petal_width'])
-        ]
-        
-        # Make prediction
-        prediction = model.predict([features])[0]
-        
-        # Get the class name (flower species)
-        species = class_names[prediction]
-        
-        # Capitalize the species name for display
-        species_display = f"Iris {species}"
-        
-        # Print debug info
-        print(f"Input features: {features}")
-        print(f"Prediction: {prediction}, Species: {species_display}")
-        
-        return render_template('result.html', prediction=species_display)
-    
-    except Exception as e:
-        error_message = f"Error making prediction: {str(e)}"
-        print(error_message)
-        return render_template('result.html', prediction="Error: Could not make prediction", error=error_message)
+# Create Gradio interface
+demo = gr.Interface(
+    fn=predict_species,
+    inputs=[
+        gr.Number(label="Sepal Length"),
+        gr.Number(label="Sepal Width"),
+        gr.Number(label="Petal Length"),
+        gr.Number(label="Petal Width")
+    ],
+    outputs="text",
+    title="Iris Flower Classifier"
+)
 
-if __name__ == '__main__':
-    app.run(host="0.0.0.0", port=5000, debug=True)
+demo.launch()