Update app.py

app.py

@@ -1,134 +1,126 @@
-from flask import Flask, render_template, request, jsonify
-import tensorflow as tf
-import numpy as np
-from PIL import Image
-import pickle
-import io
-import os
-import matplotlib.pyplot as plt
-import logging
-import socket
-
-# Configure logging
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-logger = logging.getLogger(__name__)
-
-app = Flask(__name__)
-
-MODEL_PATH = "D:\\CODEVERSE\\DNACreator\\skin_lesion_model.h5"
-HISTORY_PATH = "D:\\CODEVERSE\\DNACreator\\training_history.pkl"
-
-# Load model
-try:
-    logger.info("Loading model from %s", MODEL_PATH)
-    model = tf.keras.models.load_model(MODEL_PATH)
-except Exception as e:
-    logger.error("Failed to load model: %s", str(e))
-    raise
-
-# Load training history for plotting
-if os.path.exists(HISTORY_PATH):
-    try:
-        with open(HISTORY_PATH, "rb") as f:
-            history_dict = pickle.load(f)
-        logger.info("Loaded training history from %s", HISTORY_PATH)
-    except Exception as e:
-        logger.error("Failed to load training history: %s", str(e))
-        history_dict = {}
-else:
-    logger.warning("Training history file %s not found", HISTORY_PATH)
-    history_dict = {}
-
-# Plot accuracy history
-if "accuracy" in history_dict and "val_accuracy" in history_dict:
-    try:
-        plt.plot(history_dict['accuracy'], label='Train Accuracy')
-        plt.plot(history_dict['val_accuracy'], label='Val Accuracy')
-        plt.xlabel('Epochs')
-        plt.ylabel('Accuracy')
-        plt.title('Training History')
-        plt.legend()
-        plt.grid(True)
-        plt.savefig("static/training_plot.png")
-        plt.close()
-        logger.info("Generated training history plot at static/training_plot.png")
-    except Exception as e:
-        logger.error("Failed to generate training plot: %s", str(e))
-
-IMG_SIZE = (224, 224)
-CONFIDENCE_THRESHOLD = 0.30
-
-label_map = {
-    0: "Melanoma",
-    1: "Melanocytic nevus",
-    2: "Basal cell carcinoma",
-    3: "Actinic keratosis",
-    4: "Benign keratosis",
-    5: "Dermatofibroma",
-    6: "Vascular lesion",
-    7: "Squamous cell carcinoma"
-}
-
-def preprocess_image(image_bytes):
-    try:
-        image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
-        image = image.resize(IMG_SIZE)
-        image_array = tf.keras.utils.img_to_array(image)
-        image_array = np.expand_dims(image_array, axis=0)
-        return image_array / 255.0
-    except Exception as e:
-        logger.error("Failed to preprocess image: %s", str(e))
-        raise
-
-@app.route("/form", methods=["GET"])
-def form():
-    logger.info("Serving form page at /form")
-    return render_template("form.html", history_plot="/static/training_plot.png")
-
-@app.route("/predict", methods=["POST"])
-def predict():
-    logger.info("Received prediction request")
-    if "image" not in request.files:
-        logger.warning("No image uploaded in request")
-        return jsonify({"error": "No image uploaded"}), 400
-
-    try:
-        image = request.files["image"].read()
-        img_array = preprocess_image(image)
-
-        logger.info("Running model prediction")
-        prediction = model.predict(img_array)[0]
-        predicted_index = int(np.argmax(prediction))
-        confidence = float(prediction[predicted_index])
-
-        if confidence < CONFIDENCE_THRESHOLD:
-            result = {
-                "prediction": "Low confidence",
-                "confidence": f"{confidence * 100:.2f}%",
-                "message": "⚠ This image is not confidently recognized. Please upload a clearer image."
-            }
-            logger.info("Prediction: Low confidence (%.2f%%)", confidence * 100)
-        else:
-            result = {
-                "prediction": label_map.get(predicted_index, "Unknown"),
-                "confidence": f"{confidence * 100:.2f}%"
-            }
-            logger.info("Prediction: %s (Confidence: %.2f%%)", result["prediction"], confidence * 100)
-
-        return jsonify(result), 200
-    except Exception as e:
-        logger.error("Error processing image: %s", str(e))
-        return jsonify({"error": f"Error processing image: {str(e)}"}), 500
-
-if __name__ == "__main__":
-    ports = [3000, 3001, 3002]  # Try these ports
-    for port in ports:
-        try:
-            logger.info("Attempting to start Flask application on port %d", port)
-            app.run(host='0.0.0.0', debug=True, port=port)
-            break
-        except socket.error as e:
-            logger.error("Port %d failed: %s", port, str(e))
-            if port == ports[-1]:
-                logger.error("All ports failed. Please free a port or check permissions.")
-                raise
+from flask import Flask, render_template, request, jsonify
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import pickle
+import io
+import os
+import matplotlib.pyplot as plt
+import logging
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+app = Flask(__name__)
+
+# Use relative paths for deployment
+MODEL_PATH = "skin_lesion_model.h5"
+HISTORY_PATH = "training_history.pkl"
+
+# Load model
+try:
+    logger.info("Loading model from %s", MODEL_PATH)
+    model = tf.keras.models.load_model(MODEL_PATH)
+except Exception as e:
+    logger.error("Failed to load model: %s", str(e))
+    raise
+
+# Load training history for plotting
+if os.path.exists(HISTORY_PATH):
+    try:
+        with open(HISTORY_PATH, "rb") as f:
+            history_dict = pickle.load(f)
+        logger.info("Loaded training history from %s", HISTORY_PATH)
+    except Exception as e:
+        logger.error("Failed to load training history: %s", str(e))
+        history_dict = {}
+else:
+    logger.warning("Training history file %s not found", HISTORY_PATH)
+    history_dict = {}
+
+# Plot accuracy history
+if "accuracy" in history_dict and "val_accuracy" in history_dict:
+    try:
+        plt.plot(history_dict['accuracy'], label='Train Accuracy')
+        plt.plot(history_dict['val_accuracy'], label='Val Accuracy')
+        plt.xlabel('Epochs')
+        plt.ylabel('Accuracy')
+        plt.title('Training History')
+        plt.legend()
+        plt.grid(True)
+        os.makedirs("static", exist_ok=True)
+        plt.savefig("static/training_plot.png")
+        plt.close()
+        logger.info("Generated training history plot at static/training_plot.png")
+    except Exception as e:
+        logger.error("Failed to generate training plot: %s", str(e))
+
+IMG_SIZE = (224, 224)
+CONFIDENCE_THRESHOLD = 0.30
+
+label_map = {
+    0: "Melanoma",
+    1: "Melanocytic nevus",
+    2: "Basal cell carcinoma",
+    3: "Actinic keratosis",
+    4: "Benign keratosis",
+    5: "Dermatofibroma",
+    6: "Vascular lesion",
+    7: "Squamous cell carcinoma"
+}
+
+def preprocess_image(image_bytes):
+    try:
+        image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+        image = image.resize(IMG_SIZE)
+        image_array = tf.keras.utils.img_to_array(image)
+        image_array = np.expand_dims(image_array, axis=0)
+        return image_array / 255.0
+    except Exception as e:
+        logger.error("Failed to preprocess image: %s", str(e))
+        raise
+
+@app.route("/form", methods=["GET"])
+def form():
+    logger.info("Serving form page at /form")
+    return render_template("form.html", history_plot="/static/training_plot.png")
+
+@app.route("/predict", methods=["POST"])
+def predict():
+    logger.info("Received prediction request")
+    if "image" not in request.files:
+        logger.warning("No image uploaded in request")
+        return jsonify({"error": "No image uploaded"}), 400
+
+    try:
+        image = request.files["image"].read()
+        img_array = preprocess_image(image)
+
+        logger.info("Running model prediction")
+        prediction = model.predict(img_array)[0]
+        predicted_index = int(np.argmax(prediction))
+        confidence = float(prediction[predicted_index])
+
+        if confidence < CONFIDENCE_THRESHOLD:
+            result = {
+                "prediction": "Low confidence",
+                "confidence": f"{confidence * 100:.2f}%",
+                "message": "⚠ This image is not confidently recognized. Please upload a clearer image."
+            }
+            logger.info("Prediction: Low confidence (%.2f%%)", confidence * 100)
+        else:
+            result = {
+                "prediction": label_map.get(predicted_index, "Unknown"),
+                "confidence": f"{confidence * 100:.2f}%"
+            }
+            logger.info("Prediction: %s (Confidence: %.2f%%)", result["prediction"], confidence * 100)
+
+        return jsonify(result), 200
+    except Exception as e:
+        logger.error("Error processing image: %s", str(e))
+        return jsonify({"error": f"Error processing image: {str(e)}"}), 500
+
+# Required port setup for Hugging Face Spaces
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=7860)