Upload 3 files

app (2).py

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+# -*- coding: utf-8 -*-
+"""app.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1KKOkxXNFwhNS2I3EHiTAAnmT0g6xZwIT
+"""
+
+import gradio as gr
+import torch
+import torch.nn as nn
+from torchvision import models, transforms
+from PIL import Image
+import numpy as np
+
+# =======================================
+# CONFIGURATION
+# =======================================
+
+# Force CPU for Hugging Face free tier
+DEVICE = torch.device("cpu")
+
+CLASS_NAMES = ["Non-Glaucoma", "Glaucoma"]
+MODEL_PATH = "model_fold_0.pth"
+
+# =======================================
+# LOAD MODEL
+# =======================================
+
+try:
+    model = models.resnet18(weights=None)
+    model.fc = nn.Linear(model.fc.in_features, 2)
+
+    state_dict = torch.load(MODEL_PATH, map_location=DEVICE)
+    model.load_state_dict(state_dict)
+
+    model.to(DEVICE)
+    model.eval()
+
+except Exception as e:
+    raise RuntimeError(f"Failed to load model from {MODEL_PATH}\nError: {str(e)}")
+
+# =======================================
+# IMAGE PREPROCESSING
+# =======================================
+
+transform = transforms.Compose([
+    transforms.Resize((256, 256)),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize(
+        mean=[0.485, 0.456, 0.406],
+        std=[0.229, 0.224, 0.225]
+    )
+])
+
+# =======================================
+# PREDICTION FUNCTION
+# =======================================
+
+def predict_fundus(image):
+    if image is None:
+        return "Please upload a retinal fundus image to begin.", None
+
+    try:
+        img_pil = Image.fromarray(image).convert("RGB")
+        img_tensor = transform(img_pil).unsqueeze(0).to(DEVICE)
+
+        with torch.no_grad():
+            output = model(img_tensor)
+            probs = torch.softmax(output, dim=1)[0].cpu().numpy()
+
+        pred_idx = int(np.argmax(probs))
+        confidence = float(probs[pred_idx])
+        label = CLASS_NAMES[pred_idx]
+
+        result_text = f"""
+### Prediction Result
+
+**Prediction:** {label}
+**Confidence:** {confidence:.1%}
+
+**Non-Glaucoma Probability:** {probs[0]:.1%}
+**Glaucoma Probability:** {probs[1]:.1%}
+
+---
+
+⚠ This tool is for research/educational purposes only.
+For clinical decisions, consult a qualified ophthalmologist.
+        """.strip()
+
+        img_display = np.array(img_pil.resize((400, 400)))
+
+        return result_text, img_display
+
+    except Exception as e:
+        return f"Error during analysis: {str(e)}", None
+
+
+# =======================================
+# CUSTOM CSS
+# =======================================
+
+custom_css = """
+body { font-family: 'Segoe UI', sans-serif; background: #f8fafc; }
+.gradio-container { max-width: 1100px !important; margin: auto; }
+h1 { color: #1e40af; text-align: center; margin-bottom: 0.4em; }
+.upload-zone { border: 2px dashed #94a3b8; border-radius: 12px; padding: 20px; background: white; }
+.result-panel {
+    background: white;
+    border-radius: 12px;
+    box-shadow: 0 4px 15px rgba(0,0,0,0.08);
+    padding: 24px;
+    min-height: 380px;
+}
+.note { font-size: 0.95em; color: #64748b; margin-top: 16px; }
+"""
+
+# =======================================
+# GRADIO INTERFACE
+# =======================================
+
+with gr.Blocks(theme=gr.themes.Soft(), css=custom_css) as demo:
+    gr.Markdown(
+        """
+        # Glaucoma Screening – Fundus Image Analysis
+
+        Upload a retinal fundus photograph to receive an AI-based probability assessment.
+        This tool is for research/educational purposes only — **not for clinical diagnosis**.
+        """
+    )
+
+    with gr.Row(equal_height=True):
+        with gr.Column(scale=5):
+            gr.Markdown("### Upload Fundus Image")
+            input_image = gr.Image(
+                type="numpy",
+                label="",
+                elem_classes=["upload-zone"],
+                height=480,
+                image_mode="RGB"
+            )
+            analyze_btn = gr.Button("Analyze Image", variant="primary")
+
+        with gr.Column(scale=5):
+            gr.Markdown("### Analysis Result")
+            output_text = gr.Markdown(
+                value="Upload an image and click Analyze to begin.",
+                elem_classes=["result-panel"]
+            )
+            output_image = gr.Image(
+                label="Uploaded Image (resized)",
+                type="numpy",
+                height=400,
+                interactive=False
+            )
+
+    gr.Markdown(
+        """
+        <div class="note">
+        <strong>Important:</strong> This is an experimental AI model trained on limited data.
+        Results should be interpreted cautiously and verified by a qualified ophthalmologist.
+        </div>
+        """,
+        elem_classes=["note"]
+    )
+
+    analyze_btn.click(
+        fn=predict_fundus,
+        inputs=input_image,
+        outputs=[output_text, output_image]
+    )
+
+# =======================================
+# LAUNCH (HF Compatible)
+# =======================================
+
+demo.launch()

model_fold_0.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:75c8995845cbefd4c06a0c3aefbad08007d9d131e3792a61b3c63f9dee0bd631
+size 44790027

requirements (2).txt

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+gradio
+torch
+torchvision
+pillow
+numpy