from flask import Flask, request, jsonify import numpy as np import tensorflow as tf from PIL import Image import io import base64 import re import joblib import os app = Flask(__name__) # Ensure the "images" directory exists IMAGE_DIR = "images" if not os.path.exists(IMAGE_DIR): os.makedirs(IMAGE_DIR) # Load all models - use absolute paths for Hugging Face MODEL_DIR = os.path.join(os.getcwd(), "models") models = { "cnn": tf.keras.models.load_model(os.path.join(MODEL_DIR, "mnist_cnn_model.h5")), "svm": joblib.load(os.path.join(MODEL_DIR, "mnist_svm.pkl")), "logistic": joblib.load(os.path.join(MODEL_DIR, "mnist_logistic_regression.pkl")), "random_forest": joblib.load(os.path.join(MODEL_DIR, "mnist_random_forest.pkl")) } # [Keep your existing classification_reports, preprocess_image, # and create_simulated_scores functions exactly as they are] # Classification reports for each model classification_reports = { "cnn": """ precision recall f1-score support 0 0.99 1.00 0.99 980 1 1.00 1.00 1.00 1135 2 0.99 0.99 0.99 1032 3 0.99 1.00 0.99 1010 4 1.00 0.99 0.99 982 5 0.98 0.99 0.99 892 6 1.00 0.98 0.99 958 7 0.99 0.99 0.99 1028 8 1.00 0.99 0.99 974 9 0.99 0.99 0.99 1009 accuracy 0.99 10000 macro avg 0.99 0.99 0.99 10000 weighted avg 0.99 0.99 0.99 10000 """, "svm": """ precision recall f1-score support 0 0.9874 0.9896 0.9885 1343 1 0.9882 0.9925 0.9903 1600 2 0.9706 0.9819 0.9762 1380 3 0.9783 0.9749 0.9766 1433 4 0.9777 0.9822 0.9800 1295 5 0.9827 0.9796 0.9811 1273 6 0.9858 0.9921 0.9889 1396 7 0.9768 0.9807 0.9788 1503 8 0.9813 0.9683 0.9748 1357 9 0.9807 0.9669 0.9738 1420 accuracy 0.9810 14000 macro avg 0.9809 0.9809 0.9809 14000 weighted avg 0.9810 0.9810 0.9810 14000 """, "random_forest": """ precision recall f1-score support 0 0.9844 0.9866 0.9855 1343 1 0.9831 0.9831 0.9831 1600 2 0.9522 0.9674 0.9597 1380 3 0.9579 0.9532 0.9556 1433 4 0.9617 0.9699 0.9658 1295 5 0.9707 0.9631 0.9669 1273 6 0.9800 0.9828 0.9814 1396 7 0.9668 0.9681 0.9674 1503 8 0.9599 0.9528 0.9564 1357 9 0.9566 0.9465 0.9515 1420 accuracy 0.9675 14000 macro avg 0.9673 0.9674 0.9673 14000 weighted avg 0.9675 0.9675 0.9675 14000 """, "logistic": """ precision recall f1-score support 0 0.9636 0.9650 0.9643 1343 1 0.9433 0.9675 0.9553 1600 2 0.9113 0.8935 0.9023 1380 3 0.9021 0.8939 0.8980 1433 4 0.9225 0.9290 0.9257 1295 5 0.8846 0.8790 0.8818 1273 6 0.9420 0.9534 0.9477 1396 7 0.9273 0.9421 0.9347 1503 8 0.8973 0.8696 0.8832 1357 9 0.9019 0.9000 0.9010 1420 accuracy 0.9204 14000 macro avg 0.9196 0.9193 0.9194 14000 weighted avg 0.9201 0.9204 0.9202 14000 """ } # Preprocess image before prediction def preprocess_image(image, model_type): image = image.resize((28, 28)).convert('L') # Convert to grayscale img_array = np.array(image) / 255.0 # Normalize if model_type == "cnn": # CNN expects 4D tensor with channel dimension return np.expand_dims(np.expand_dims(img_array, axis=0), axis=-1) else: # Other models expect flattened 1D array return img_array.flatten().reshape(1, -1) @app.route('/') def home(): return jsonify({ "message": "MNIST Classifier API", "available_models": list(models.keys()), "endpoints": { "/predict": "POST - Send image and model_type", "/get_classification_report": "POST - Get model metrics" } }) # [Keep your existing /get_classification_report and /predict routes exactly as they are] @app.route('/get_classification_report', methods=['POST']) def get_classification_report(): model_type = request.json['model_type'] if model_type in classification_reports: return jsonify({ 'report': classification_reports[model_type] }) return jsonify({'error': 'Model not found'}) @app.route('/predict', methods=['POST']) def predict(): if request.method == 'POST': data = request.json['image'] model_type = request.json['model_type'] img_data = re.sub('^data:image/png;base64,', '', data) img = Image.open(io.BytesIO(base64.b64decode(img_data))) # Save the image to "images" folder image_path = os.path.join(IMAGE_DIR, "digit.png") img.save(image_path) # Preprocess image and predict processed_image = preprocess_image(img, model_type) if model_type in models: model = models[model_type] # Model-specific prediction logic if model_type == "cnn": # For CNN, use softmax probabilities prediction = model.predict(processed_image) predicted_digit = np.argmax(prediction) confidence_scores = prediction[0].tolist() score_type = "probability" elif model_type == "svm": # For SVM, use decision function distances predicted_digit = model.predict(processed_image)[0] # Try to get decision function scores if hasattr(model, "decision_function") and callable(getattr(model, "decision_function")): try: # Get raw decision scores decision_scores = model.decision_function(processed_image) # One-vs-One SVMs have a different shape for decision_function output if len(decision_scores.shape) == 2: # This is a standard one-vs-rest SVM, shape should be (1, n_classes) confidence_scores = decision_scores[0].tolist() else: # One-vs-One SVM returns pairwise comparisons # Convert to a simplified score per class (this is an approximation) confidence_scores = [0] * 10 for i in range(10): # Count how many times class i wins in pairwise comparisons confidence_scores[i] = sum(1 for score in decision_scores[0] if score > 0) # Normalize scores to positive values for visualization min_score = min(confidence_scores) if min_score < 0: confidence_scores = [score - min_score for score in confidence_scores] score_type = "decision_distance" except (AttributeError, NotImplementedError) as e: print(f"Error getting decision function: {e}") confidence_scores = create_simulated_scores(int(predicted_digit)) score_type = "simulated" else: # Fallback if decision_function is not available confidence_scores = create_simulated_scores(int(predicted_digit)) score_type = "simulated" else: # For other models (Random Forest, Logistic Regression) predicted_digit = model.predict(processed_image)[0] # Try to get probability estimates if hasattr(model, "predict_proba") and callable(getattr(model, "predict_proba")): try: confidence_scores = model.predict_proba(processed_image)[0].tolist() score_type = "probability" except (AttributeError, NotImplementedError): confidence_scores = create_simulated_scores(int(predicted_digit)) score_type = "simulated" else: confidence_scores = create_simulated_scores(int(predicted_digit)) score_type = "simulated" return jsonify({ 'digit': int(predicted_digit), 'confidence_scores': confidence_scores, 'score_type': score_type }) return jsonify({'error': 'Model not found'}) def create_simulated_scores(predicted_digit): """Create simulated confidence scores that sum to 1.0 with highest probability for the predicted digit.""" # Assign base probabilities scores = [0.01] * 10 # Give each digit a small base probability # Calculate remaining probability (should be around 0.9) remaining = 1.0 - sum(scores) # Assign the remaining probability to the predicted digit scores[predicted_digit] += remaining return scores if __name__ == '__main__': app.run(host='0.0.0.0', port=7860) # Hugging Face uses port 7860