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from flask import Flask, render_template, request, jsonify
import joblib
import numpy as np
from sklearn import datasets
import os
import json
app = Flask(__name__)
# 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
# Additional flower information for enhanced display
flower_info = {
'setosa': {
'emoji': '🌸',
'color': '#FFB6C1',
'color_name': 'Pink and White',
'description': 'Small, delicate petals with beautiful pink and white colors',
'origin': 'North America and eastern Asia',
'size': 'Small (petals < 2cm)',
'habitat': 'Cooler climates, Arctic regions',
'image': 'iris setosa.jpg'
},
'versicolor': {
'emoji': '🌺',
'color': '#8A2BE2',
'color_name': 'Blue-Purple',
'description': 'Medium-sized flowers with stunning blue-purple hues',
'origin': 'Eastern North America',
'size': 'Medium (petals 2-4cm)',
'habitat': 'Wetlands and marshy areas',
'image': 'iris versicolor.jpg'
},
'virginica': {
'emoji': '🌷',
'color': '#4B0082',
'color_name': 'Deep Violet-Purple',
'description': 'Large, magnificent blooms with deep violet-purple colors',
'origin': 'Eastern North America (Virginia)',
'size': 'Large (petals > 4cm)',
'habitat': 'Various soil types, adaptable',
'image': 'iris verginica.jpg'
}
}
@app.route('/')
def form():
return render_template('form.html')
@app.route('/api/flower-info')
def get_flower_info():
"""API endpoint to get flower information"""
return jsonify(flower_info)
@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'])
]
# Validate input ranges (basic sanity check)
for i, feature in enumerate(features):
if feature < 0 or feature > 15: # Reasonable limits for iris measurements
raise ValueError(f"Feature {i+1} value {feature} is outside reasonable range (0-15 cm)")
# Make prediction
prediction = model.predict([features])[0]
# Get prediction probabilities for confidence
prediction_proba = model.predict_proba([features])[0]
confidence = max(prediction_proba) * 100
# Get the class name (flower species)
species = class_names[prediction]
# Capitalize the species name for display
species_display = f"Iris {species.capitalize()}"
# Get additional flower information
additional_info = flower_info.get(species, {})
# Print debug info
print(f"Input features: {features}")
print(f"Prediction: {prediction}, Species: {species_display}")
print(f"Confidence: {confidence:.1f}%")
print(f"Probabilities: {prediction_proba}")
return render_template('result.html',
prediction=species_display,
confidence=confidence,
features=features,
flower_info=additional_info)
except ValueError as ve:
error_message = f"Invalid input: {str(ve)}"
print(error_message)
return render_template('result.html',
prediction="Error: Invalid input values",
error=error_message)
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)
if __name__ == '__main__':
app.run(host="0.0.0.0", port=5000, debug=True)
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