Update app.py

app.py

@@ -1,4 +1,4 @@
-from flask import Flask, render_template, request, jsonify
+from flask import Flask, render_template, request, jsonify, send_file
 import tensorflow as tf
 import numpy as np
 from PIL import Image
@@ -14,9 +14,9 @@ logger = logging.getLogger(__name__)
 
 app = Flask(__name__)
 
-# Use relative paths for deployment
 MODEL_PATH = "skin_lesion_model.h5"
 HISTORY_PATH = "training_history.pkl"
+PLOT_PATH = "/tmp/static/training_plot.png"
 
 # Load model
 try:
@@ -42,9 +42,7 @@ else:
 # Plot accuracy history
 if "accuracy" in history_dict and "val_accuracy" in history_dict:
     try:
-        plot_dir = os.path.join(os.getcwd(), "static")
-        os.makedirs(plot_dir, exist_ok=True)  # ✅ ensure directory is writable
-
+        os.makedirs("/tmp/static", exist_ok=True)
         plt.plot(history_dict['accuracy'], label='Train Accuracy')
         plt.plot(history_dict['val_accuracy'], label='Val Accuracy')
         plt.xlabel('Epochs')
@@ -52,10 +50,9 @@ if "accuracy" in history_dict and "val_accuracy" in history_dict:
         plt.title('Training History')
         plt.legend()
         plt.grid(True)
-        plot_path = os.path.join(plot_dir, "training_plot.png")
-        plt.savefig(plot_path)  # ✅ safe full path
+        plt.savefig(PLOT_PATH)
         plt.close()
-        logger.info("Generated training history plot at %s", plot_path)
+        logger.info("Generated training history plot at %s", PLOT_PATH)
     except Exception as e:
         logger.error("Failed to generate training plot: %s", str(e))
 
@@ -87,7 +84,11 @@ def preprocess_image(image_bytes):
 @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")
+    return render_template("form.html", history_plot="/training_plot.png")
+
+@app.route("/training_plot.png")
+def training_plot():
+    return send_file(PLOT_PATH, mimetype='image/png')
 
 @app.route("/predict", methods=["POST"])
 def predict():
@@ -124,6 +125,6 @@ def predict():
         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
+# Required for Hugging Face Spaces
 if __name__ == "__main__":
     app.run(host="0.0.0.0", port=7860)