Spaces:
Sleeping
Sleeping
using 3 new models of VQA
Browse files- app.py +11 -3
- inference.py +30 -9
app.py
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import gradio as gr
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import os
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from inference import
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from utils import *
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with gr.Blocks() as block:
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options = gr.Dropdown(choices=["Model 1", "Model 2", "Model 3"], label="Models", info="Select the model to use..")
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dogs = os.path.join(os.path.dirname(__file__), "617.jpg")
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image = gr.Image(type="pil", value=dogs)
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btn.click(inference, inputs=[image, txt], outputs=[txt_3])
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btn = gr.Button(value="Submit")
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if __name__ == "__main__":
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import gradio as gr
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import os
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from inference import Inference
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from utils import *
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inference = Inference()
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with gr.Blocks() as block:
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options = gr.Dropdown(choices=["Model 1", "Model 2", "Model 3"], label="Models", info="Select the model to use..")
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dogs = os.path.join(os.path.dirname(__file__), "617.jpg")
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image = gr.Image(type="pil", value=dogs)
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btn.click(inference.inference_vilt, inputs=[image, txt], outputs=[txt_3])
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btn = gr.Button(value="Submit")
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iface = gr.Interface(
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fn=lambda: "Selected Model: " + options.value,
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inputs=block,
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outputs="text",
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live=False
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)
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if __name__ == "__main__":
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iface.launch()
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inference.py
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from transformers import ViltProcessor, ViltForQuestionAnswering
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from transformers import ViltProcessor, ViltForQuestionAnswering, Pix2StructProcessor, Pix2StructForConditionalGeneration, Blip2Processor, Blip2ForConditionalGeneration
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import torch
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class Inference:
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def __init__(self):
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self.vilt_processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
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self.vilt_model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
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self.deplot_processor = Pix2StructProcessor.from_pretrained('google/deplot')
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self.deplot_model = Pix2StructForConditionalGeneration.from_pretrained('google/deplot')
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self.blip_processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
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self.blip_model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", load_in_8bit=True, device_map={"": 0}, torch_dtype=torch.float16)
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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def inference_vilt(self, image, text):
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encoding = self.vilt_processor(image, text, return_tensors="pt")
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outputs = self.vilt_model(**encoding)
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logits = outputs.logits
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idx = logits.argmax(-1).item()
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return f"{self.vilt_model.config.id2label[idx]}"
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def inference_deplot(self, image, text):
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inputs = self.deplot_processor(images=image, text=text, return_tensors="pt")
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predictions = self.deplot_model.generate(**inputs, max_new_tokens=512)
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return f"{self.deplot_processor.decode(predictions[0], skip_special_tokens=True)}"
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def inference_vilt(self, image, text):
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inputs = self.blip_processor(images=image, return_tensors="pt").to(self.device, torch.float16)
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generated_ids = self.blip_model.generate(**inputs)
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generated_text = self.blip_processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
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return f"{generated_text}"
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