Create APP

APP

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+from fastapi import FastAPI, File, UploadFile, Request
+from fastapi.responses import HTMLResponse, JSONResponse
+from fastapi.staticfiles import StaticFiles
+from fastapi.templating import Jinja2Templates
+from PIL import Image
+import torch
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+import io
+
+app = FastAPI()
+
+# Load model and processor once
+processor = AutoImageProcessor.from_pretrained("aashituli/promblemo")
+model = AutoModelForImageClassification.from_pretrained("aashituli/promblemo")
+
+# Mount templates and static files
+app.mount("/static", StaticFiles(directory="static"), name="static")
+templates = Jinja2Templates(directory="templates")
+
+@app.get("/", response_class=HTMLResponse)
+async def home(request: Request):
+    return templates.TemplateResponse("index.html", {"request": request})
+
+@app.post("/predict/")
+async def predict(file: UploadFile = File(...)):
+    try:
+        contents = await file.read()
+        image = Image.open(io.BytesIO(contents)).convert("RGB")
+        inputs = processor(images=image, return_tensors="pt")
+
+        with torch.no_grad():
+            outputs = model(**inputs)
+
+        predicted_class_idx = outputs.logits.argmax(-1).item()
+        predicted_class = model.config.id2label[predicted_class_idx]
+
+        return JSONResponse(content={"prediction": predicted_class})
+
+    except Exception as e:
+        return JSONResponse(content={"error": str(e)}, status_code=500)