created app.py

app.py

@@ -0,0 +1,27 @@
+import gradio as gr
+from transformers import BlipForConditionalGeneration, AutoProcessor
+from PIL import Image
+import torch
+
+# Load model and processor
+processor = AutoProcessor.from_pretrained("blip-fine-tuned/")
+processor.tokenizer.padding_size = 'left'
+model = BlipForConditionalGeneration.from_pretrained("blip-fine-tuned/")
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+def predict(image):
+    # Preprocess the image
+    inputs = processor(images=image, return_tensors="pt").to(device)
+    pixel_values = inputs.pixel_values
+
+    # get predictions
+    with torch.no_grad():
+        generated_ids = model.generate(pixel_values=pixel_values, max_length=100)
+    
+    generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    return generated_caption
+
+
+interface = gr.Interface(fn=predict, inputs=gr.Image(type="pil"), outputs="text")
+interface.launch()