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Apy.py

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+from fastapi import FastAPI, File, UploadFile, HTTPException
+from pydantic import BaseModel
+import tensorflow as tf
+import numpy as np
+import cv2
+
+app = FastAPI()
+
+# Cargar el modelo TFLite
+interpreter = tf.lite.Interpreter(model_path="model.tflite")
+interpreter.allocate_tensors()
+
+# Obtener detalles de las entradas y salidas del modelo
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+
+# Función para preprocesar la imagen
+def preprocess_image(image):
+    image = cv2.resize(image, (224, 224))
+    image = image / 255.0
+    image = np.expand_dims(image, axis=0).astype(np.float32)
+    return image
+
+# Ruta de predicción
+@app.post("/predict/")
+async def predict(file: UploadFile = File(...)):
+    try:
+        # Leer la imagen
+        image = await file.read()
+        image = cv2.imdecode(np.frombuffer(image, np.uint8), cv2.IMREAD_COLOR)
+        
+        # Preprocesar la imagen
+        processed_image = preprocess_image(image)
+        
+        # Realizar la predicción
+        interpreter.set_tensor(input_details[0]['index'], processed_image)
+        interpreter.invoke()
+        output_data = interpreter.get_tensor(output_details[0]['index'])
+
+        # Determinar la clase y la confianza
+        class_idx = np.argmax(output_data[0])
+        labels = ['Benign', 'Malignant']
+        result = labels[class_idx]
+        confidence = float(output_data[0][class_idx])
+
+        return {"class": result, "confidence": confidence}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+

model.tflite

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+version https://git-lfs.github.com/spec/v1
+oid sha256:77c9587ffe7289faecba206ef81375fe7204f096a9b0f52ff2da054b5d1a0ea3
+size 11561972

requirements.txt

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+fastapi
+uvicorn
+tensorflow
+opencv-python-headless
+numpy