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.gitattributes

@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 backend/accident_model.keras filter=lfs diff=lfs merge=lfs -text
+accident_model.keras filter=lfs diff=lfs merge=lfs -text

accident_model.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a119e7725838b3813adb595c7d706c987a666b1f5c0ea36fb38bf1d367238d9f
+size 31873058

app.py

@@ -0,0 +1,50 @@
+import gradio as gr
+import numpy as np
+from PIL import Image
+import tensorflow as tf
+import json
+import os
+
+# Load metadata
+CLASSES = ['Minor', 'Serious', 'Fatal']
+IMG_SIZE = 224
+
+if os.path.exists("metadata.json"):
+    with open("metadata.json") as f:
+        meta = json.load(f)
+        CLASSES = meta.get("classes", CLASSES)
+        IMG_SIZE = meta.get("img_size", IMG_SIZE)
+
+# Load model
+model = tf.keras.models.load_model("model.keras")
+
+def preprocess(image):
+    img = image.resize((IMG_SIZE, IMG_SIZE))
+    img = np.array(img, dtype=np.float32) / 255.0
+    return np.expand_dims(img, 0)
+
+def predict(image):
+    img_array = preprocess(image)
+    
+    preds = model.predict(img_array)[0]
+    idx = int(np.argmax(preds))
+
+    probs = {
+        CLASSES[i]: float(preds[i] * 100)
+        for i in range(len(CLASSES))
+    }
+
+    return {
+        "severity": CLASSES[idx],
+        "confidence": f"{preds[idx]*100:.2f}%",
+        "probabilities": probs
+    }
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs="json",
+    title="Accident Severity Prediction"
+)
+
+iface.launch()

metadata.json

@@ -0,0 +1,19 @@
+{
+  "classes": [
+    "Minor",
+    "Serious",
+    "Fatal"
+  ],
+  "folder_classes": [
+    "1",
+    "2",
+    "3"
+  ],
+  "label_map": {
+    "1": "Minor",
+    "2": "Serious",
+    "3": "Fatal"
+  },
+  "img_size": 224,
+  "model_type": "MobileNetV2"
+}

requirements.txt

@@ -0,0 +1,4 @@
+gradio
+tensorflow
+numpy
+pillow