Spaces:
Running
Running
File size: 2,923 Bytes
85abd3d 32dd4d2 7cab805 d5d3aa6 32dd4d2 d5d3aa6 32dd4d2 d5d3aa6 32dd4d2 d5d3aa6 32dd4d2 d5d3aa6 7cab805 d5d3aa6 7cab805 d5d3aa6 32dd4d2 7cab805 d5d3aa6 7cab805 d5d3aa6 32dd4d2 d5d3aa6 32dd4d2 d5d3aa6 32dd4d2 d5d3aa6 7cab805 32dd4d2 85abd3d 8d67b19 3ba8f3d 85abd3d 3ba8f3d 6852c86 85abd3d 3ba8f3d 6852c86 85abd3d 3ba8f3d 8d67b19 3ba8f3d 8d67b19 3ba8f3d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 |
"""from fastapi import FastAPI, UploadFile, File
from fastapi.responses import RedirectResponse, JSONResponse
from transformers import AutoProcessor, AutoModelForCausalLM
from PIL import Image
import tempfile
import torch
app = FastAPI()
# Load model
try:
processor = AutoProcessor.from_pretrained("microsoft/git-large-coco")
model = AutoModelForCausalLM.from_pretrained("microsoft/git-large-coco")
USE_GIT = True
except Exception:
from transformers import pipeline
captioner = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")
USE_GIT = False
def generate_caption(image_path):
try:
if USE_GIT:
image = Image.open(image_path)
inputs = processor(images=image, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
return processor.batch_decode(outputs, skip_special_tokens=True)[0]
else:
result = captioner(image_path)
return result[0]['generated_text']
except Exception as e:
return f"Error generating caption: {str(e)}"
@app.post("/imagecaption/")
async def caption_from_frontend(file: UploadFile = File(...)):
contents = await file.read()
with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as tmp:
tmp.write(contents)
image_path = tmp.name
caption = generate_caption(image_path)
return JSONResponse({"caption": caption})
@app.get("/")
def home():
return RedirectResponse(url="/")"""
# appImage.py
from fastapi import UploadFile, File
from transformers import AutoProcessor, AutoModelForCausalLM
from transformers import pipeline
from PIL import Image
import tempfile
import torch
# Load model
try:
processor = AutoProcessor.from_pretrained("microsoft/git-large-coco")
model = AutoModelForCausalLM.from_pretrained("microsoft/git-large-coco")
USE_GIT = True
except Exception:
captioner = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")
USE_GIT = False
def generate_caption(image_path):
try:
if USE_GIT:
image = Image.open(image_path)
inputs = processor(images=image, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
return processor.batch_decode(outputs, skip_special_tokens=True)[0]
else:
result = captioner(image_path)
return result[0]['generated_text']
except Exception as e:
return f"Error generating caption: {str(e)}"
async def caption_image(file: UploadFile = File(...)):
try:
contents = await file.read()
with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as tmp:
tmp.write(contents)
tmp_path = tmp.name
caption = generate_caption(tmp_path)
return caption
except Exception as e:
return {"error": f"Failed to generate caption: {str(e)}"}
|