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Model.py

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+from transformers import pipeline
+
+classifier = pipeline("image-classification", "JJNeila/fish_classification")
+
+class FishModel:
+    def __init__(self):
+        self.classifier = classifier
+    
+    def get_fish_species(self, image):
+        return self.classifier(image)

app.py

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+import gradio as gr
+import requests
+from PIL import Image
+import io
+
+from Model import FishModel
+
+Classifier =  FishModel()
+
+def inference(image_input, url_input) -> dict[str, float]:
+    image = load_image(image_input, url_input)
+
+    result = Classifier.get_fish_species(image)
+
+    data = dict()
+
+    for element in result:
+        data[element['label']] = round(element['score'], 2)
+
+    return data # {"dog" : 30, "cat" : 70}
+
+def load_image(image_input, url_input):
+    if image_input is not None:
+        return image_input
+        
+    elif url_input:
+        try:
+            response = requests.get(url_input)
+            response.raise_for_status()
+            return Image.open(io.BytesIO(response.content))
+        
+        except Exception as e:
+            raise gr.Error(f"No se pudo cargar la imagen desde la URL. Error: {e}")
+
+
+demo = gr.Interface(
+    title="🐳📸 Fish Classification",
+    description=open("description.md", "r", encoding="utf8").read(),
+    fn=inference,
+    inputs=[
+        gr.Image(label="Sube una imagen", type="pil"),
+        gr.Textbox(label="O pega una URL de imagen aquí")
+    ],
+    outputs=gr.Label(label="Resultado"),
+    # examples=[
+    #     [None, "https://gradio-builds.s3.amazonaws.com/demo-files/goldfish.jpg"],
+    #     ["images/salmon.jpg", None]
+    # ]
+)
+
+demo.launch()

description.md

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+Este proyecto es para clasificar peces en las siguientes categorías:
+* Grandes
+* Pequeños

requirements.txt

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+mpmath
+transformers
+gradio
+requests
+PIL
+io