up

Dockerfile

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+FROM python:3.9-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+
+RUN pip install --no-cache-dir --upgrade pip -r requirements.txt
+
+COPY . .
+
+EXPOSE 8000
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

main.py

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+# main.py
+
+import io
+import numpy as np
+import tensorflow as tf
+from fastapi import FastAPI, File, UploadFile
+from fastapi.responses import JSONResponse
+from PIL import Image
+from pydantic import BaseModel
+
+class TranslationResponse(BaseModel):
+    prediction: str
+    confidence: float
+
+try:
+    model = tf.keras.models.load_model('best_model.keras')
+except Exception as e:
+    raise IOError(f"Error al cargar el modelo 'best_model.keras'. Asegúrate de que el archivo está en el directorio correcto. Error: {e}")
+
+app = FastAPI(
+    title="API de Traducción de Lenguaje de Señas Americano",
+    description="Sube una imagen del alfabeto de señas (ASL) para obtener una predicción del modelo.",
+    version="1.0.0"
+)
+
+CLASS_NAMES = [
+    'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M',
+    'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z',
+    'del', 'nothing', 'space'
+]
+
+def preprocess_image(image: Image.Image) -> np.ndarray:
+    image = image.resize((96, 96))
+    image_array = np.array(image)
+    
+    # Si la imagen es RGBA o en escala de grises, la convierte a RGB
+    if image_array.shape[2] == 4:
+        image_array = image_array[..., :3]
+    
+    # Normaliza y expande dimensiones para que coincida con la entrada del modelo (1, 96, 96, 3)
+    normalized_array = image_array.astype('float32') / 255.0
+    return np.expand_dims(normalized_array, axis=0)
+
+@app.post("/predict/", response_model=TranslationResponse)
+async def predict(file: UploadFile = File(...)):
+    contents = await file.read()
+    
+    try:
+        image = Image.open(io.BytesIO(contents)).convert('RGB')
+    except Exception as e:
+        return JSONResponse(status_code=400, content={"message": f"Error al leer la imagen: {e}"})
+
+    processed_image = preprocess_image(image)
+
+    predictions = model.predict(processed_image)
+    
+    predicted_index = np.argmax(predictions, axis=1)[0]
+    
+    confidence = float(predictions[0][predicted_index])
+    
+    prediction_label = CLASS_NAMES[predicted_index]
+
+    return TranslationResponse(prediction=prediction_label, confidence=confidence)
+
+@app.get("/")
+def read_root():
+    return {"message": "ok"}