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#!/usr/bin/env python3
"""
Test script to verify the model prediction fixes
"""
import os
import numpy as np
import tensorflow as tf
from PIL import Image
# Configuration
MODEL_PATH = "models/1.h5"
IMAGE_SIZE = 256
# Updated class names that match the training order
CLASS_NAMES = ["Potato___Early_blight", "Potato___Late_blight", "Potato___healthy"]
CLASS_DISPLAY_NAMES = ["Early Blight", "Late Blight", "Healthy"]
def load_model():
"""Load the trained model"""
try:
model = tf.keras.models.load_model(MODEL_PATH)
print("✅ Model loaded successfully!")
return model, True
except Exception as e:
print(f"❌ Error loading model: {e}")
return None, False
def preprocess_image(image):
"""Preprocess image for prediction - fixed version"""
try:
# Convert to RGB if necessary
if image.mode != "RGB":
image = image.convert("RGB")
# Resize image
image = image.resize((IMAGE_SIZE, IMAGE_SIZE))
# Convert to numpy array (NO NORMALIZATION - model has built-in rescaling)
img_array = np.array(image)
# Add batch dimension
img_array = np.expand_dims(img_array, axis=0)
print(f"Image preprocessed - Shape: {img_array.shape}, Range: [{img_array.min()}, {img_array.max()}]")
return img_array
except Exception as e:
print(f"Error preprocessing image: {e}")
return None
def test_prediction(model, image_path):
"""Test prediction on a sample image"""
try:
# Load and preprocess image
image = Image.open(image_path)
processed_image = preprocess_image(image)
if processed_image is None:
return None
# Make prediction
predictions = model.predict(processed_image)
predicted_class_index = np.argmax(predictions[0])
confidence = float(np.max(predictions[0]))
# Get results
predicted_class = CLASS_NAMES[predicted_class_index]
predicted_display_name = CLASS_DISPLAY_NAMES[predicted_class_index]
print(f"\n🔍 Prediction Results for {image_path}:")
print(f"Raw predictions: {predictions[0]}")
print(f"Predicted class: {predicted_display_name} (index: {predicted_class_index})")
print(f"Confidence: {confidence:.4f} ({confidence*100:.2f}%)")
print(f"\n📊 All probabilities:")
for i, (class_name, display_name) in enumerate(zip(CLASS_NAMES, CLASS_DISPLAY_NAMES)):
prob = predictions[0][i] * 100
print(f" {display_name}: {prob:.2f}%")
return {
"predicted_class": predicted_display_name,
"confidence": confidence * 100,
"all_predictions": predictions[0]
}
except Exception as e:
print(f"Error in prediction: {e}")
return None
def main():
"""Main test function"""
print("🧪 Testing Model Prediction Fixes\n")
# Load model
model, model_loaded = load_model()
if not model_loaded:
print("❌ Cannot proceed without model")
return
# Test with sample images from uploads folder
upload_folder = "static/uploads"
if os.path.exists(upload_folder):
image_files = [f for f in os.listdir(upload_folder) if f.lower().endswith(('.jpg', '.jpeg', '.png'))]
if image_files:
print(f"📁 Found {len(image_files)} test images in uploads folder\n")
# Test first few images
for i, image_file in enumerate(image_files[:3]): # Test first 3 images
image_path = os.path.join(upload_folder, image_file)
print(f"Testing image {i+1}: {image_file}")
result = test_prediction(model, image_path)
print("-" * 50)
else:
print("📁 No test images found in uploads folder")
else:
print("📁 Uploads folder not found")
# Test with dummy data
print("\n🎲 Testing with random dummy data:")
dummy_image = np.random.randint(0, 255, (1, IMAGE_SIZE, IMAGE_SIZE, 3), dtype=np.uint8)
predictions = model.predict(dummy_image)
print(f"Dummy predictions: {predictions[0]}")
print(f"Sum of predictions: {np.sum(predictions[0]):.4f}")
print(f"Predicted class: {CLASS_DISPLAY_NAMES[np.argmax(predictions[0])]}")
if __name__ == "__main__":
main()
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