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"""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 transformers import pipeline
import tempfile, os
from PIL import Image
from gtts import gTTS
captioner = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")
async def caption_image(file):
contents = await file.read()
with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as tmp:
tmp.write(contents)
image_path = tmp.name
captions = captioner(image_path)
caption = captions[0]['generated_text'] if captions else "No caption generated."
audio_path = text_to_speech(caption)
result = {"caption": caption}
if audio_path:
result["audioUrl"] = f"/files/{os.path.basename(audio_path)}"
return result
def text_to_speech(text: str):
try:
tts = gTTS(text)
temp_audio = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3")
tts.save(temp_audio.name)
return temp_audio.name
except:
return ""
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