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.history/.streamlit/config_20251011123628.toml

@@ -0,0 +1,4 @@
+[server]
+headless = true
+enableCORS = false
+port = 8501

.history/main/app_20251011123657.py

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+import os
+os.makedirs(os.path.expanduser("~/.streamlit"), exist_ok=True)
+
+import streamlit as st
+from transformers import ViTFeatureExtractor, ViTForImageClassification
+from PIL import Image
+import torch
+
+st.set_page_config(page_title="Cataract Detection with ViT", layout="wide")
+st.title("👁️ Cataract Detection using Vision Transformer (ViT)")
+
+uploaded_file = st.file_uploader("Upload an eye image (JPG/PNG)", type=["jpg", "jpeg", "png"])
+if uploaded_file:
+    image = Image.open(uploaded_file).convert("RGB")
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    model_name = "Decoder24/Cataract-ViT"
+    model = ViTForImageClassification.from_pretrained(model_name)
+    extractor = ViTFeatureExtractor.from_pretrained(model_name)
+
+    inputs = extractor(images=image, return_tensors="pt")
+    outputs = model(**inputs)
+    preds = outputs.logits.softmax(dim=-1)
+    label = preds.argmax(dim=-1).item()
+
+    st.success(f"Predicted class: {model.config.id2label[label]}")

.history/requirements_20251011123708.txt

@@ -0,0 +1,11 @@
+torch
+torchvision
+torchaudio
+timm
+scikit-learn
+opencv-python
+matplotlib
+seaborn
+albumentations
+wandb
+streamlit

.history/requirements_20251011123712.txt

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+torch
+torchvision
+torchaudio
+timm
+scikit-learn
+opencv-python
+matplotlib
+seaborn
+albumentations
+wandb
+streamlit

.history/requirements_20251011123720.txt

@@ -0,0 +1,12 @@
+torch
+torchvision
+torchaudio
+timm
+scikit-learn
+opencv-python
+matplotlib
+seaborn
+albumentations
+wandb
+streamlit
+transformers

.history/requirements_20251011123723.txt

@@ -0,0 +1,13 @@
+torch
+torchvision
+torchaudio
+timm
+scikit-learn
+opencv-python
+matplotlib
+seaborn
+albumentations
+wandb
+streamlit
+transformers
+pillow

.streamlit/config.toml

@@ -0,0 +1,4 @@
+[server]
+headless = true
+enableCORS = false
+port = 8501

main/app.py

@@ -0,0 +1,26 @@
+import os
+os.makedirs(os.path.expanduser("~/.streamlit"), exist_ok=True)
+
+import streamlit as st
+from transformers import ViTFeatureExtractor, ViTForImageClassification
+from PIL import Image
+import torch
+
+st.set_page_config(page_title="Cataract Detection with ViT", layout="wide")
+st.title("👁️ Cataract Detection using Vision Transformer (ViT)")
+
+uploaded_file = st.file_uploader("Upload an eye image (JPG/PNG)", type=["jpg", "jpeg", "png"])
+if uploaded_file:
+    image = Image.open(uploaded_file).convert("RGB")
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    model_name = "Decoder24/Cataract-ViT"
+    model = ViTForImageClassification.from_pretrained(model_name)
+    extractor = ViTFeatureExtractor.from_pretrained(model_name)
+
+    inputs = extractor(images=image, return_tensors="pt")
+    outputs = model(**inputs)
+    preds = outputs.logits.softmax(dim=-1)
+    label = preds.argmax(dim=-1).item()
+
+    st.success(f"Predicted class: {model.config.id2label[label]}")

requirements.txt

@@ -8,3 +8,6 @@ matplotlib
 seaborn
 albumentations
 wandb
+streamlit
+transformers
+pillow