Create app.py

app.py

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+import requests
+from PIL import Image
+from io import BytesIO
+import numpy as np
+import gradio as gr
+from transformers import pipeline
+
+image_to_text = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")
+
+def process_image(image):
+
+    if isinstance(image, str):  # Si l'entrée est une URL
+        response = requests.get(image)
+        image = Image.open(BytesIO(response.content))
+    elif isinstance(image, np.ndarray):  # Si l'entrée est un tableau numpy
+        image = Image.fromarray(image)
+    else:  # Si l'entrée est un fichier local
+        image = Image.open(image.name)
+
+    result = image_to_text(image)
+
+    text_description = result[0]["generated_text"]
+    return text_description
+
+imager = gr.Interface(process_image, inputs="image", outputs="text")
+imager.launch(debug=True)
+