Update app.py

app.py

@@ -13,6 +13,7 @@ import numpy as np
 from tensorflow.keras.models import load_model
 from tensorflow.keras.preprocessing import image
 import matplotlib.pyplot as plt
+from huggingface_hub import hf_hub_download
 
 
 # ------------------------- PAGE CONFIG -------------------------
@@ -44,35 +45,37 @@ if app_mode == "🏠 Home":
 elif app_mode == "🩻 X-ray Classifier":
     st.header("🩺 Chest X-ray COVID Prediction")
 
-    uploaded_image = st.file_uploader("📤 Upload Chest X-ray", type=["jpg", "jpeg", "png"])
+   uploaded_image = st.file_uploader("📤 Upload Chest X-ray", type=["jpg", "jpeg", "png"])
+
+if uploaded_image:
+    with st.spinner("🔍 Predicting..."):
+        try:
+            # ✅ Download model from Hugging Face Hub (only once, then cached)
+            model_path = hf_hub_download(
+                repo_id="nehda10/covid-xray-model",  # 👈 Your HF model repo
+                filename="covid_xray_model.keras"
+            )
+            model = load_model(model_path)
+            st.success("✅ Model loaded successfully!")
+
+            # ✅ Process image and predict
+            img = image.load_img(uploaded_image, target_size=(224, 224))
+            img_array = image.img_to_array(img) / 255.0
+            img_array = np.expand_dims(img_array, axis=0)
+
+            pred = model.predict(img_array)[0]
+            labels = ['COVID', 'NORMAL', 'Viral Pneumonia']
+            result = labels[np.argmax(pred)]
+
+            col1, col2 = st.columns([1, 2])
+            with col1:
+                st.image(uploaded_image, caption="Uploaded X-ray", use_container_width=True)
+            with col2:
+                st.success(f"🧠 **Predicted Condition:** `{result}`")
+
+        except Exception as e:
+            st.error(f"🚫 Prediction failed. Error: `{e}`")
 
-    if uploaded_image:
-        with st.spinner("🔍 Predicting..."):
-            # ✅ Show files in directory to debug model path
-            st.write("📁 Files in directory:", os.listdir())
-
-            try:
-                # ✅ Attempt to load model
-                model = load_model("covid_xray_model.keras")
-                st.success("✅ Model loaded successfully!")
-
-                # ✅ Process image and predict
-                img = image.load_img(uploaded_image, target_size=(224, 224))
-                img_array = image.img_to_array(img) / 255.0
-                img_array = np.expand_dims(img_array, axis=0)
-
-                pred = model.predict(img_array)[0]
-                labels = ['COVID', 'NORMAL', 'Viral Pneumonia']
-                result = labels[np.argmax(pred)]
-
-                col1, col2 = st.columns([1, 2])
-                with col1:
-                    st.image(uploaded_image, caption="Uploaded X-ray", use_container_width=True)
-                with col2:
-                    st.success(f"🧠 **Predicted Condition:** `{result}`")
-
-            except Exception as e:
-                st.error(f"🚫 Prediction failed. Error: `{e}`")