Upload 4 files

Dockerfile

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+# Use an official Python runtime as a parent image
+FROM python:3.9-slim
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy the current directory contents into the container at /app
+COPY . /app
+
+# Install any needed packages specified in requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Make port 8000 available to the world outside this container
+EXPOSE 8000
+
+# Run app.py when the container launches
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]

app.py

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+from fastapi import FastAPI, File, UploadFile
+from fastapi.responses import JSONResponse
+# from tensorflow.keras.models import load_model
+# from tensorflow.keras.preprocessing import image
+import numpy as np
+from fastapi.middleware.cors import CORSMiddleware
+import io
+import os
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["http://localhost:3000"],  # Add your frontend URL
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+# Load your trained model
+# model = load_model('flower_species_model.h5')
+
+# def preprocess_image(img_file):
+#     img = image.load_img(img_file, target_size=(64, 64))
+#     img_array = image.img_to_array(img)
+#     img_array = np.expand_dims(img_array, axis=0)
+#     img_array /= 255.0
+#     return img_array
+
+@app.post("/predict")
+async def predict(files: list[UploadFile] = File(...)):
+    if not files:
+        return JSONResponse(content={"error": "No files uploaded"}, status_code=400)
+
+    predictions = []
+    for file in files:
+        contents = await file.read()
+        img = io.BytesIO(contents)
+        # preprocessed_img = preprocess_image(img)
+        # prediction = model.predict(preprocessed_img)
+        # predictions.append(prediction[0][0])
+        print("File uploaded")
+
+    threshold = 0.5
+    # predicted_classes = [1 if p > threshold else 0 for p in predictions]
+    # percentage_class_1 = (predicted_classes.count(1) / len(predicted_classes)) * 100
+
+    # return {"percentage_class_1": round(percentage_class_1, 2)}
+    return {"message": "Files uploaded", "percentage": 100}
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)

flower_species_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4b52398edd4b1826b7cfd3a56e88a53936f016e17cf21a87ca483845353c3201
+size 8251576

requirements.txt

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+fastapi
+uvicorn
+python-multipart
+numpy
+tensorflow