Create app.py

app.py

@@ -0,0 +1,27 @@
+import gradio as gr
+from transformers import AutoProcessor,  AutoModelForCausalLM, pipeline
+import torch
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+checkpoint1 = "ayoubkirouane/git-base-One-Piece"
+processor = AutoProcessor.from_pretrained(checkpoint1)
+model1 = AutoModelForCausalLM.from_pretrained(checkpoint1)
+def img2cap(image):
+    input1 = processor(images=image, return_tensors="pt").to(device)
+    pixel_values1 = input1.pixel_values
+    generated_id1 = model1.generate(pixel_values=pixel_values1, max_length=50)
+    generated_caption1 = processor.batch_decode(generated_id1, skip_special_tokens=True)[0]
+    return generated_caption1
+
+
+examples = ["1.png" ,"2.png" , "3.png" ]
+
+gr.Interface(
+    img2cap,
+    inputs = gr.inputs.Image(type="pil", label="Original Image"),
+    outputs= gr.outputs.Textbox(label="Caption from pre-trained model"),
+    title = "Image Captioning using git-base-One-Piece Model",
+    description = "git-base-One-Piece is used to generate Image Caption for the uploaded image.",
+    examples=examples,
+    theme="huggingface",
+).launch(debug=True)