Update app.py

app.py

@@ -1,24 +1,25 @@
-import gradio as gr
-import numpy as np
+from fastapi import FastAPI, UploadFile, File
+from fastapi.responses import JSONResponse
 from tensorflow.keras.models import load_model
 from PIL import Image
+import numpy as np
+import io
 
-# Load the model
+app = FastAPI()
+
+# Load model and class names
 model = load_model("hf_keras_model.keras")
 class_names = ['buildings', 'forest', 'glacier', 'mountain', 'sea', 'street']
 
-def predict_image(img):
-    img = img.resize((150, 150))
-    img_array = np.array(img) / 255.0
+@app.post("/predict/")
+async def predict(file: UploadFile = File(...)):
+    contents = await file.read()
+    image = Image.open(io.BytesIO(contents)).convert("RGB")
+    image = image.resize((150, 150))
+    img_array = np.array(image) / 255.0
     img_array = np.expand_dims(img_array, axis=0)
+
     preds = model.predict(img_array)[0]
-    confidences = {class_names[i]: float(preds[i]) for i in range(6)}
-    return confidences
+    confidences = {class_names[i]: float(preds[i]) for i in range(len(class_names))}
 
-gr.Interface(
-    fn=predict_image,
-    inputs=gr.Image(type="pil"),
-    outputs=gr.Label(num_top_classes=3),
-    title="Intel Image Classifier",
-    description="Upload a landscape image and get predictions (buildings, forest, glacier, etc.)"
-).launch()
+    return JSONResponse(content=confidences)