Create app.py

app.py

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+import torch
+import requests
+from PIL import Image
+from transformers import BlipProcessor, BlipForConditionalGeneration
+import gradio as gr
+
+processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
+model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base", torch_dtype=torch.float16).to("cuda")
+
+# Function to process the image and generate captions
+def generate_caption(image, caption_type, text):
+    raw_image = Image.fromarray(image.astype('uint8'), 'RGB')
+    
+    if caption_type == "Conditional":
+        caption = conditional_image_captioning(raw_image, text)
+    else:
+        caption = unconditional_image_captioning(raw_image)
+    
+    return caption
+
+# Conditional image captioning
+def conditional_image_captioning(raw_image, text):
+    inputs = processor(raw_image, text, return_tensors="pt").to("cuda", torch.float16)
+    out = model.generate(**inputs)
+    caption = processor.decode(out[0], skip_special_tokens=True)
+    return caption
+
+# Unconditional image captioning
+def unconditional_image_captioning(raw_image):
+    inputs = processor(raw_image, return_tensors="pt").to("cuda", torch.float16)
+    out = model.generate(**inputs)
+    caption = processor.decode(out[0], skip_special_tokens=True)
+    return caption
+
+# Interface setup
+input_image = gr.inputs.Image()
+input_text = gr.inputs.Textbox(label="Enter Text (for Conditional Captioning)")
+
+choices = ["Conditional", "Unconditional"]
+radio_button = gr.inputs.Radio(choices, label="Captioning Type")
+
+output_text = gr.outputs.Textbox(label="Caption")
+
+# Create the interface
+gr.Interface(fn=generate_caption, inputs=[input_image, radio_button, input_text], outputs=output_text, title="Image Captioning",debug=True).launch()