Upload 4 files

Dockerfile

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+FROM python:3.10
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY app.py .
+COPY growlens_efficientnet_model.h5 .
+
+EXPOSE 7860
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI, UploadFile, File
+from fastapi.responses import JSONResponse
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+import io
+
+app = FastAPI()
+
+# Load model
+def load_model():
+    return tf.keras.models.load_model("growlens_efficientnet_model.h5")
+
+model = load_model()
+class_names = [
+    "ants", "bees", "beetle", "catterpillar", "earthworms", "earwig",
+    "grasshopper", "moth", "slug", "snail", "wasp", "weevil"
+]
+
+@app.post("/predict")
+async def predict(file: UploadFile = File(...)):
+    image_bytes = await file.read()
+    image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+    image = image.resize((224, 224))  # Adjust size as per your model
+    img_array = np.array(image) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+    preds = model.predict(img_array)
+    pred_class = class_names[np.argmax(preds)]
+    confidence = float(np.max(preds))
+    return JSONResponse({"class": pred_class, "confidence": confidence})

growlens_efficientnet_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ad68ad756d68ad00f95fdd40882781eb4a9c64c3fcd7ce7c7178e2d3d1057c3d
+size 20906560

requirements.txt

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+fastapi
+uvicorn
+tensorflow
+pillow
+numpy