Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+from transformers import BlipProcessor, BlipForConditionalGeneration
+from transformers import CLIPProcessor, CLIPModel
+import torch
+from PIL import Image
+import requests
+import os
+
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model_id = "openai/clip-vit-base-patch16"  # You can choose a different CLIP model from Hugging Face
+clipprocessor = CLIPProcessor.from_pretrained(model_id)
+clipmodel = CLIPModel.from_pretrained(model_id).to(device)
+
+
+model_id = "Salesforce/blip-image-captioning-base" ## load modelID for BLIP
+blipmodel = BlipForConditionalGeneration.from_pretrained(model_id)
+blipprocessor = BlipProcessor.from_pretrained(model_id)
+
+
+def evaluate_caption(image, caption):
+    # # Pre-process image
+    # image = processor(images=image, return_tensors="pt").to(device)
+
+    # # Tokenize and encode the caption
+    # text = processor(text=caption, return_tensors="pt").to(device)
+
+
+
+    blip_input = blipprocessor(image, return_tensors="pt")
+    out = blipmodel.generate(**blip_input,max_new_tokens=50)
+    blip_caption = blipprocessor.decode(out[0], skip_special_tokens=True)
+
+    inputs = clipprocessor(text=[caption,blip_caption], images=image, return_tensors="pt", padding=True)
+
+    similarity_score = clipmodel(**inputs).logits_per_image
+
+
+
+    # Convert score to a float
+    score = similarity_score.softmax(dim=1).detach().numpy()
+    print(score)
+    if score[0][0]>score[0][1]:
+      winner = "The first caption is the human"
+    else:
+      winner = "The second caption is the human"
+
+    
+    return blip_caption,winner
+    # ,gr.Image(type="pil", value="mukherjee_kushin_WIDPICS1.jpg")
+
+
+callback = gr.HuggingFaceDatasetSaver('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', "gradioTest")
+with gr.Blocks() as demo:
+  im_path_str = 'n01677366_12918.JPEG'
+  im_path = gr.Textbox(label="Image fname",value=im_path_str,interactive=False, visible=False)
+  # fn=evaluate_caption,
+  # inputs=["image", "text"]
+  
+  with gr.Column():
+    im = gr.Image(label="Target Image", interactive = False, type="pil",value =f'images/{im_path_str}',height=500)
+    caps = gr.Textbox(label="Player 1 Caption")
+    submit_btn = gr.Button("Submit!!")
+  # outputs=["text","text"],
+  with gr.Column():
+    out1 = gr.Textbox(label="Player 2 (Machine) Caption",interactive=False)
+    out2 = gr.Textbox(label="Winner",interactive=False)
+
+
+  # live=False,
+  # interpretation="default"
+  callback.setup([caps, out1, out2, im_path], "flagged_data_points")
+  # callback.flag([image, caption, blip_caption, winner])
+  submit_btn.click(fn = evaluate_caption,inputs = [im,caps], outputs = [out1, out2],api_name="test").success(lambda *args: callback.flag(args), [caps, out1, out2, im_path], None, preprocess=False)
+  # with gr.Row():
+  #     btn = gr.Button("Flag")
+  # btn.click(lambda *args: callback.flag(args), [im, caps, out1, out2], None, preprocess=False)
+
+demo.launch(debug=False)