Update Summarization/appImage.py

Summarization/appImage.py

@@ -1,48 +1,4 @@
-"""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="/")"""
 from fastapi import UploadFile
 from transformers import AutoProcessor, AutoModelForCausalLM, pipeline
 from PIL import Image