Create app.py

app.py

@@ -0,0 +1,54 @@
+from fastapi import FastAPI, UploadFile, File
+from huggingface_hub import hf_hub_download
+from keras.models import load_model
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.applications.inception_v3 import preprocess_input
+import numpy as np
+
+app = FastAPI()
+
+# Lista de clases
+class_names = ['acanthoica', 'akashiwo', 'alexandrium', 'amoeba', 'amphidinium', 'amylax', 'apedinella', 
+               'asterionellopsis', 'bacillaria', 'bacteriastrum', 'biddulphia', 'calciopappus', 'cerataulina', 
+               'ceratium', 'chaetoceros', 'chrysochromulina', 'cochlodinium', 'corethron', 'corymbellus', 
+               'coscinodiscus', 'cryptophyta', 'cylindrotheca', 'dactyliosolen', 'delphineis', 'dictyocha', 
+               'dinobryon', 'dinophysis', 'ditylum', 'emiliania', 'ephemera', 'eucampia', 'euglena', 
+               'gonyaulax', 'guinardia', 'gyrodinium', 'hemiaulus', 'heterocapsa', 'karenia', 'katodinium', 
+               'kryptoperidinium', 'laboea', 'lauderia', 'leptocylindrus', 'licmophora', 'nanoneis', 
+               'odontella', 'ophiaster', 'ostreopsis', 'oxytoxum', 'paralia', 'parvicorbicula', 'phaeocystis', 
+               'pleuronema', 'pleurosigma', 'polykrikos', 'prorocentrum', 'proterythropsis', 'protoperidinium', 
+               'pseudo-nitzschia', 'pseudochattonella', 'pyramimonas', 'rhabdolithes', 'rhizosolenia', 
+               'scrippsiella', 'skeletonema', 'stephanopyxis', 'syracosphaera', 'thalassionema', 'thalassiosira', 
+               'trichodesmium', 'vicicitus', 'warnowia']
+
+# Descargar y cargar el modelo desde Hugging Face Hub
+model_path = hf_hub_download(repo_id="Daniel00611/InceptionV3_72", filename="InceptionV3_72.keras")
+model = load_model(model_path)
+
+def preprocess_image(image_file, target_size=(299, 299)):
+    img = image.load_img(image_file, target_size=target_size)
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array = preprocess_input(img_array)
+    return img_array
+
+@app.post("/predict/")
+async def predict(file: UploadFile = File(...)):
+    # Procesar la imagen
+    img_array = preprocess_image(file.file)
+    
+    # Realizar la predicción
+    predictions = model.predict(img_array)[0]
+    
+    # Obtener el top 10 de predicciones
+    top_10_indices = predictions.argsort()[-10:][::-1]
+    top_10_classes = [class_names[i] for i in top_10_indices]
+    top_10_probabilities = predictions[top_10_indices]
+
+    # Formar la respuesta en formato JSON
+    result = [{"class": top_10_classes[i], "probability": float(top_10_probabilities[i])} for i in range(10)]
+    return {"predictions": result}
+
+@app.get("/")
+def greet_json():
+    return {"Hello": "World!"}