Upload app.py

app.py

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+import streamlit as st
+import torch
+import torch.nn as nn
+from torchvision import transforms
+from PIL import Image
+import numpy as np
+from tensorflow.keras.models import load_model
+import os
+import requests
+
+st.title("Stroke Patient Pain Intensity Detector")
+st.markdown("Upload a full-face image of a stroke patient. The app will detect the affected side and predict pain intensity using the unaffected side.")
+st.write("🔧 App started. Preparing to download models...")
+
+@st.cache_resource
+def download_models():
+    model_urls = {
+        "cnn_stroke_model.keras": "https://huggingface.co/AdhamQQ/cnn_stroke_model/resolve/main/cnn_stroke_model.keras",
+        "right_side_pain_model.pth": "https://huggingface.co/AdhamQQ/cnn_stroke_model/resolve/main/right_side_pain_model.pth"
+    }
+
+    for filename, url in model_urls.items():
+        if not os.path.exists(filename):
+            st.write(f"📥 Downloading {filename}...")
+            r = requests.get(url)
+            with open(filename, "wb") as f:
+                f.write(r.content)
+            st.write(f"✅ {filename} downloaded.")
+        else:
+            st.write(f"✔️ {filename} already exists.")
+
+    st.write("📦 Loading models...")
+    stroke_model = load_model("cnn_stroke_model.keras")
+
+    class PainRegressor(nn.Module):
+        def __init__(self):
+            super(PainRegressor, self).__init__()
+            from torchvision.models import resnet18, ResNet18_Weights
+            self.base = resnet18(weights=ResNet18_Weights.DEFAULT)
+            num_features = self.base.fc.in_features
+            self.base.fc = nn.Linear(num_features, 1)
+        def forward(self, x):
+            return self.base(x)
+
+    pain_model = PainRegressor()
+    pain_model.load_state_dict(torch.load("right_side_pain_model.pth", map_location=torch.device('cpu')))
+    pain_model.eval()
+    st.write("✅ Models loaded.")
+
+    return stroke_model, pain_model
+
+stroke_model, pain_model = download_models()
+
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                         std=[0.229, 0.224, 0.225])
+])
+
+uploaded_file = st.file_uploader("Choose an image", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    st.write("📷 Image uploaded. Processing...")
+    full_image = Image.open(uploaded_file).convert("RGB")
+    st.image(full_image, caption="Uploaded Full-Face Image", use_column_width=True)
+
+    w, h = full_image.size
+    mid = w // 2
+    left_face = full_image.crop((0, 0, mid, h))
+    right_face = full_image.crop((mid, 0, w, h))
+
+    stroke_input = full_image.resize((128, 128))
+    stroke_array = np.array(stroke_input).astype("float32") / 255.0
+    stroke_array = np.expand_dims(stroke_array, axis=0)
+    st.write("🧠 Running stroke model prediction...")
+    stroke_pred = stroke_model.predict(stroke_array)
+    affected = int(np.round(stroke_pred[0][0]))
+
+    unaffected_face = right_face if affected == 0 else left_face
+    unaffected_tensor = transform(unaffected_face).unsqueeze(0)
+
+    st.write("📈 Predicting pain score...")
+    with torch.no_grad():
+        output = pain_model(unaffected_tensor)
+        pspi_score = output.item()
+
+    st.subheader("Prediction Results")
+    st.image(unaffected_face, caption="Unaffected Side Used for Pain Detection", width=300)
+    st.write(f"**Affected side:** {'left' if affected == 0 else 'right'}")
+    st.write(f"**Unaffected side:** {'right' if affected == 0 else 'left'}")
+    st.write(f"**Predicted PSPI Pain Score:** {round(pspi_score, 3)}")