Initial commit - Adding prescription recognition model

DockerFile

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+FROM python:3.9
+
+WORKDIR /app
+
+COPY requirements.txt requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

Group Project.code-workspace

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+{
+	"folders": [
+		{
+			"path": "../.."
+		},
+		{
+			"name": "ISTM6218_Group_Project",
+			"path": "."
+		}
+	],
+	"settings": {
+		"liveServer.settings.multiRootWorkspaceName": "Group Project"
+	}
+}

app.py

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+from fastapi import FastAPI, UploadFile, File
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import io
+import easyocr
+
+app = FastAPI()
+
+# Load the model
+model = tf.keras.models.load_model("prescription_model.h5")
+ocr_reader = easyocr.Reader(['en'])  # English OCR model
+
+@app.post("/predict/")
+async def predict(file: UploadFile = File(...)):
+    image = Image.open(io.BytesIO(await file.read())).convert('L').resize((128, 128))  # Convert to grayscale
+    img_array = np.array(image) / 255.0
+    img_array = np.expand_dims(img_array, axis=(0, -1))
+    
+    # Get predictions
+    prediction = model.predict(img_array)
+
+    # Use OCR for text recognition
+    text_prediction = ocr_reader.readtext(np.array(image))
+    text_result = " ".join([text[1] for text in text_prediction])
+
+    return {"prediction": prediction.tolist(), "ocr_text": text_result}

prescription_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:afd2afd1b1907c2397ae740a099d509e9332c98be069df3f3cc53f05eba972da
+size 29964376

requirements.txt

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+fastapi
+uvicorn
+tensorflow
+pillow
+numpy
+easyocr
+torch