create app.py

app.py

@@ -0,0 +1,89 @@
+import gradio as gr
+from transformers import AutoProcessor, AutoTokenizer, AutoImageProcessor, AutoModelForCausalLM, BlipForConditionalGeneration, Blip2ForConditionalGeneration, VisionEncoderDecoderModel
+import torch
+import open_clip
+
+from huggingface_hub import hf_hub_download
+
+
+git_processor_large_coco = AutoProcessor.from_pretrained("microsoft/git-large-coco")
+git_model_large_coco = AutoModelForCausalLM.from_pretrained("microsoft/git-large-coco")
+
+git_processor_large_textcaps = AutoProcessor.from_pretrained("microsoft/git-large-r-textcaps")
+git_model_large_textcaps = AutoModelForCausalLM.from_pretrained("microsoft/git-large-r-textcaps")
+
+
+blip_processor_large = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
+blip_model_large = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large")
+
+blip2_processor_8_bit = AutoProcessor.from_pretrained("Salesforce/blip2-opt-6.7b")
+blip2_model_8_bit = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-6.7b", device_map="auto", load_in_8bit=True)
+
+coca_model, _, coca_transform = open_clip.create_model_and_transforms(
+  model_name="coca_ViT-L-14",
+  pretrained="mscoco_finetuned_laion2B-s13B-b90k"
+)
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+git_model_large_coco.to(device)
+git_model_large_textcaps.to(device)
+blip_model_large.to(device)
+coca_model.to(device)
+
+def generate_caption(processor, model, image, tokenizer=None, use_float_16=False):
+    inputs = processor(images=image, return_tensors="pt").to(device)
+
+    if use_float_16:
+        inputs = inputs.to(torch.float16)
+    
+    generated_ids = model.generate(pixel_values=inputs.pixel_values, max_length=50)
+
+    if tokenizer is not None:
+        generated_caption = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    else:
+        generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+   
+    return generated_caption
+
+
+def generate_caption_coca(model, transform, image):
+    im = transform(image).unsqueeze(0).to(device)
+    with torch.no_grad(), torch.cuda.amp.autocast():
+        generated = model.generate(im, seq_len=20)
+    return open_clip.decode(generated[0].detach()).split("<end_of_text>")[0].replace("<start_of_text>", "")
+
+
+def generate_captions(image):
+
+    caption_git_large_coco = generate_caption(git_processor_large_coco, git_model_large_coco, image)
+
+    caption_git_large_textcaps = generate_caption(git_processor_large_textcaps, git_model_large_textcaps, image)
+
+    caption_blip_large = generate_caption(blip_processor_large, blip_model_large, image)
+
+    caption_coca = generate_caption_coca(coca_model, coca_transform, image)
+
+    caption_blip2_8_bit = generate_caption(blip2_processor_8_bit, blip2_model_8_bit, image, use_float_16=True).strip()
+
+    return caption_git_large_coco, caption_git_large_textcaps, caption_blip_large, caption_coca, caption_blip2_8_bit
+
+   
+
+examples = [["Image1.jpg"], ["Image2.jpg"], ["Image3.jpg"]]
+outputs = [gr.outputs.Textbox(label="Caption generated - 1"), gr.outputs.Textbox(label="Caption generated - 2"), gr.outputs.Textbox(label="Caption generated -3"), gr.outputs.Textbox(label="Caption generated - 4"), gr.outputs.Textbox(label="Caption generated - 5")] 
+
+
+title = "Interactive demo: comparing image captioning models"
+description = "Image Caption Generator by Sravanth Kurmala"
+article = "Assignment for Listed Inc"
+
+interface = gr.Interface(fn=generate_captions, 
+                         inputs=gr.inputs.Image(type="pil"),
+                         outputs=outputs,
+                         examples=examples, 
+                         title=title,
+                         description=description,
+                         article=article, 
+                         enable_queue=True)
+interface.launch(debug=True)