Update app.py

app.py

@@ -1,61 +1,67 @@
-import streamlit as st
-from fastai.vision.all import *
-from PIL import Image
-
-# Load the model
-learn = load_learner('model.pkl')
-
-st.set_page_config(page_title="Breast Cancer Detector", layout="centered")
-st.title("🩺 Breast Cancer Detection (MIAS ROI Dataset)")
-
-st.write("Upload a mammogram ROI image to see if it's **cancerous** or **normal**.")
-
-# Upload image only (removed camera input)
-uploaded_file = st.file_uploader("Upload an Image", type=["png", "jpg", "jpeg"])
-
-img = None
-if uploaded_file is not None:
-    img = Image.open(uploaded_file).convert('RGB')
-
-if img is not None:
-    st.image(img, caption='Input Image', use_column_width=True)
-    
-    # Prediction
-    pred_class, pred_idx, probs = learn.predict(img)
-    
-    # Show results
-    st.subheader(f"Prediction: {pred_class.upper()}")
-    
-    # Show both class probabilities
-    for cls, prob in zip(learn.dls.vocab, probs):
-        st.write(f"**{cls.capitalize()}**: {prob*100:.2f}% confidence")
-
-st.markdown("---")
-
-# Sample Images
-st.subheader("Try with Sample Images:")
-
-col1, col2 = st.columns(2)
-
-with col1:
-    if st.button('Sample Normal'):
-        sample_path = 'sample_images/normal_sample.png'  # You will upload this
-        img = Image.open(sample_path).convert('RGB')
-        st.image(img, caption="Normal Sample", use_column_width=True)
-        pred_class, pred_idx, probs = learn.predict(img)
-        st.subheader(f"Prediction: {pred_class.upper()}")
-        for cls, prob in zip(learn.dls.vocab, probs):
-            st.write(f"**{cls.capitalize()}**: {prob*100:.2f}% confidence")
-
-with col2:
-    if st.button('Sample Cancer'):
-        sample_path = 'sample_images/cancer_sample.png'  # You will upload this
-        img = Image.open(sample_path).convert('RGB')
-        st.image(img, caption="Cancer Sample", use_column_width=True)
-        pred_class, pred_idx, probs = learn.predict(img)
-        st.subheader(f"Prediction: {pred_class.upper()}")
-        for cls, prob in zip(learn.dls.vocab, probs):
-            st.write(f"**{cls.capitalize()}**: {prob*100:.2f}% confidence")
-
-st.markdown("---")
-st.write("Made with ❤️ using FastAI and Streamlit.")
+import streamlit as st
+from fastai.vision.all import *
+from PIL import Image
+
+# Load the model
+learn = load_learner('model.pkl')
+
+st.set_page_config(page_title="Breast Cancer Detector", layout="centered")
+st.title("🩺 Breast Cancer Detection (MIAS ROI Dataset)")
+
+st.write("Upload a mammogram ROI image to see if it's **cancerous** or **normal**.")
+
+# Option to upload an image
+uploaded_file = st.file_uploader("Upload an Image", type=["png", "jpg", "jpeg"])
+
+# Select input source
+img = None
+if uploaded_file is not None:
+    img = Image.open(uploaded_file).convert('RGB')
+
+if img is not None:
+    st.image(img, caption='Input Image', use_column_width=True)
+
+    # Convert to fastai's PILImage
+    img_fastai = PILImage.create(img)
+
+    # Prediction
+    pred_class, pred_idx, probs = learn.predict(img_fastai)
+
+    # Show results
+    st.subheader(f"Prediction: {pred_class.upper()}")
+    
+    # Show both class probabilities
+    for cls, prob in zip(learn.dls.vocab, probs):
+        st.write(f"**{cls.capitalize()}**: {prob*100:.2f}% confidence")
+
+st.markdown("---")
+
+# Sample Images
+st.subheader("Try with Sample Images:")
+
+col1, col2 = st.columns(2)
+
+with col1:
+    if st.button('Sample Normal'):
+        sample_path = 'sample_images/normal_sample.png'  # You must upload this in Hugging Face
+        img = Image.open(sample_path).convert('RGB')
+        st.image(img, caption="Normal Sample", use_column_width=True)
+        img_fastai = PILImage.create(img)
+        pred_class, pred_idx, probs = learn.predict(img_fastai)
+        st.subheader(f"Prediction: {pred_class.upper()}")
+        for cls, prob in zip(learn.dls.vocab, probs):
+            st.write(f"**{cls.capitalize()}**: {prob*100:.2f}% confidence")
+
+with col2:
+    if st.button('Sample Cancer'):
+        sample_path = 'sample_images/cancer_sample.png'  # You must upload this too
+        img = Image.open(sample_path).convert('RGB')
+        st.image(img, caption="Cancer Sample", use_column_width=True)
+        img_fastai = PILImage.create(img)
+        pred_class, pred_idx, probs = learn.predict(img_fastai)
+        st.subheader(f"Prediction: {pred_class.upper()}")
+        for cls, prob in zip(learn.dls.vocab, probs):
+            st.write(f"**{cls.capitalize()}**: {prob*100:.2f}% confidence")
+
+st.markdown("---")
+st.write("Made by Novoo.")