Update app.py

app.py

@@ -10,6 +10,7 @@ import folium
 from streamlit_folium import st_folium
 import cv2
 import numpy as np
+from huggingface_hub import snapshot_download
 
 
 st.set_page_config(page_title="Skin Cancer Dashboard", layout="wide")
@@ -37,14 +38,34 @@ geolocator = Nominatim(user_agent="skin-dashboard", timeout = 10)
 
 # --- Load Model & Feature Extractor ---
 @st.cache_resource
+@st.cache_resource
 def load_image_model(token: str):
+    # 1) grab *everything* from the remote repo into a local folder
+    local_dir = snapshot_download(
+        repo_id=MODEL_NAME,
+        use_auth_token=token,
+        cache_dir="/app/model_cache"   # you can pick any path under /app
+    )
+
+    # 2) write a minimal, valid config.json *into* that folder
+    cfg = {
+      "architectures": ["ConvNextForImageClassification"],
+      "model_type": "convnext",
+      "num_labels": 2,
+      "id2label": { "0": "benign", "1": "malignant" },
+      "label2id": { "benign": 0, "malignant": 1 }
+    }
+    with open(os.path.join(local_dir, "config.json"), "w") as f:
+        json.dump(cfg, f)
+
+    # 3) load extractor & model *from that local snapshot*
+    extractor = AutoFeatureExtractor.from_pretrained(local_dir)
+    model     = AutoModelForImageClassification.from_pretrained(local_dir)
     return pipeline(
-    "image-classification",
-    MODEL_NAME,
-    use_auth_token=token,
-    trust_remote_code=True,  # ← allow custom code in the model repo
-    device=0,# or -1 for CPU
-    model_type = "biogpt"
+        "image-classification",
+        model=model,
+        feature_extractor=extractor,
+        device=0  # or -1 for CPU
     )
 
 @st.cache_resource