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Update Summarization/appImage.py
Browse files- Summarization/appImage.py +0 -44
Summarization/appImage.py
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"""from fastapi import FastAPI, UploadFile, File
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from fastapi.responses import RedirectResponse, JSONResponse
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from transformers import AutoProcessor, AutoModelForCausalLM
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from PIL import Image
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import tempfile
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import torch
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app = FastAPI()
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# Load model
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try:
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processor = AutoProcessor.from_pretrained("microsoft/git-large-coco")
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model = AutoModelForCausalLM.from_pretrained("microsoft/git-large-coco")
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USE_GIT = True
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except Exception:
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from transformers import pipeline
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captioner = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")
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USE_GIT = False
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def generate_caption(image_path):
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try:
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if USE_GIT:
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image = Image.open(image_path)
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inputs = processor(images=image, return_tensors="pt")
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outputs = model.generate(**inputs, max_length=50)
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return processor.batch_decode(outputs, skip_special_tokens=True)[0]
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else:
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result = captioner(image_path)
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return result[0]['generated_text']
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except Exception as e:
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return f"Error generating caption: {str(e)}"
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@app.post("/imagecaption/")
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async def caption_from_frontend(file: UploadFile = File(...)):
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contents = await file.read()
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with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as tmp:
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tmp.write(contents)
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image_path = tmp.name
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caption = generate_caption(image_path)
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return JSONResponse({"caption": caption})
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@app.get("/")
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def home():
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return RedirectResponse(url="/")"""
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from fastapi import UploadFile
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from transformers import AutoProcessor, AutoModelForCausalLM, pipeline
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from PIL import Image
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from fastapi import UploadFile
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from transformers import AutoProcessor, AutoModelForCausalLM, pipeline
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from PIL import Image
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