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vverma commited on Jun 10, 2025

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7d5d5aa

1 Parent(s): 2661513

HTML form

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__pycache__/app.cpython-39.pyc +0 -0
app.py +22 -9
templates/form.html +18 -0

__pycache__/app.cpython-39.pyc CHANGED Viewed

Binary files a/__pycache__/app.cpython-39.pyc and b/__pycache__/app.cpython-39.pyc differ

app.py CHANGED Viewed

@@ -1,31 +1,44 @@
-from fastapi import FastAPI, File, UploadFile
-from fastapi.responses import JSONResponse
 from transformers import TrOCRProcessor, VisionEncoderDecoderModel
 from PIL import Image
 import io
 app = FastAPI()
-# Load model and processor only once at startup
 processor = TrOCRProcessor.from_pretrained('tjoab/latex_finetuned')
 model = VisionEncoderDecoderModel.from_pretrained('tjoab/latex_finetuned')
-@app.post("/predict")
-async def predict_latex(file: UploadFile = File(...)):
     if file.content_type not in ["image/png", "image/jpeg"]:
-        return JSONResponse(status_code=400, content={"error": "Only PNG and JPEG files are supported."})
-    # Read image contents
     contents = await file.read()
     image = Image.open(io.BytesIO(contents))
     image = prepare_image(image)
-    # Preprocess and run inference
     inputs = processor(images=image, return_tensors="pt").pixel_values
     pred_ids = model.generate(inputs, max_length=128)
     latex_preds = processor.batch_decode(pred_ids, skip_special_tokens=True)
-    return {"latex_preds": latex_preds}
 def prepare_image(image: Image.Image) -> Image.Image:
     """Converts image to RGB if needed and flattens transparency if present."""

+from fastapi import FastAPI, File, UploadFile, Form, Request
+from fastapi.responses import HTMLResponse
+from fastapi.templating import Jinja2Templates
+from fastapi.staticfiles import StaticFiles
 from transformers import TrOCRProcessor, VisionEncoderDecoderModel
 from PIL import Image
 import io
 app = FastAPI()
+# Setup template engine
+templates = Jinja2Templates(directory="templates")
+# Load model and processor once
 processor = TrOCRProcessor.from_pretrained('tjoab/latex_finetuned')
 model = VisionEncoderDecoderModel.from_pretrained('tjoab/latex_finetuned')
+@app.get("/", response_class=HTMLResponse)
+async def form_page(request: Request):
+    return templates.TemplateResponse("form.html", {"request": request, "result": None})
+@app.post("/", response_class=HTMLResponse)
+async def handle_upload(request: Request, file: UploadFile = File(...)):
     if file.content_type not in ["image/png", "image/jpeg"]:
+        return templates.TemplateResponse("form.html", {"request": request, "result": "Invalid file type"})
     contents = await file.read()
     image = Image.open(io.BytesIO(contents))
     image = prepare_image(image)
     inputs = processor(images=image, return_tensors="pt").pixel_values
     pred_ids = model.generate(inputs, max_length=128)
     latex_preds = processor.batch_decode(pred_ids, skip_special_tokens=True)
+    return templates.TemplateResponse("form.html", {"request": request, "result": latex_preds[0]})
+def prepare_image(image: Image.Image) -> Image.Image:
+    if image.mode in ('RGBA', 'LA') or (image.mode == 'P' and 'transparency' in image.info):
+        background = Image.new('RGB', image.size, 'white')
+        return Image.alpha_composite(background, image.convert('RGBA')).convert('RGB')
+    return image.convert('RGB')
 def prepare_image(image: Image.Image) -> Image.Image:
     """Converts image to RGB if needed and flattens transparency if present."""

templates/form.html ADDED Viewed

	@@ -0,0 +1,18 @@

+<!DOCTYPE html>
+<html>
+<head>
+    <title>LaTeX Image Parser</title>
+</head>
+<body>
+    <h1>Upload an Image</h1>
+    <form action="/" method="post" enctype="multipart/form-data">
+        <input type="file" name="file" accept="image/png,image/jpeg" required>
+        <button type="submit">Upload</button>
+    </form>
+    {% if result %}
+        <h2>Prediction:</h2>
+        <pre>{{ result }}</pre>
+    {% endif %}
+</body>
+</html>