Update app.py

app.py

@@ -1,36 +1,24 @@
+import os
 from flask import Flask, request, jsonify
 from transformers import AutoModelForImageClassification, AutoProcessor
 import torch
 from PIL import Image
 import io
 
+# ✅ Set a writable cache directory inside the /app folder
+os.environ["HF_HOME"] = "/app/huggingface"
+
 app = Flask(__name__)
 
-# ✅ Load the model with trust_remote_code=True
+# ✅ Load the model with a custom cache directory
 model = AutoModelForImageClassification.from_pretrained(
     "shahad-alh/arabichar-finetuned-v2",
-    trust_remote_code=True
+    trust_remote_code=True,
+    cache_dir=os.environ["HF_HOME"]  # ✅ Fix: Store model inside /app
 )
-processor = AutoProcessor.from_pretrained("shahad-alh/arabichar-finetuned-v2")
-
-@app.route('/classify', methods=['POST'])
-def classify():
-    if 'file' not in request.files:
-        return jsonify({"error": "No file uploaded"}), 400
-
-    file = request.files['file']
-    image = Image.open(io.BytesIO(file.read()))
-
-    # ✅ Process the image
-    inputs = processor(images=image, return_tensors="pt")
 
-    # ✅ Run inference
-    with torch.no_grad():
-        outputs = model(**inputs)
-
-    # ✅ Get predicted class
-    predicted_class = torch.argmax(outputs.logits, dim=-1).item()
-    return jsonify({"prediction": predicted_class})
+processor = AutoProcessor.from_pretrained(
+    "shahad-alh/arabichar-finetuned-v2",
+    cache_dir=os.environ["HF_HOME"]  # ✅ Fix: Store processor inside /app
+)
 
-if __name__ == '__main__':
-    app.run(host="0.0.0.0", port=7860)