Update app.py

app.py

@@ -1,16 +1,26 @@
 import requests
 from PIL import Image
 import streamlit as st
-from transformers import pipeline
+# Load model directly
+from transformers import AutoTokenizer, AutoModelForImageTextToText
+
+tokenizer = AutoTokenizer.from_pretrained("microsoft/trocr-large-printed")
+model = AutoModelForImageTextToText.from_pretrained("microsoft/trocr-large-printed")
 
-pipe = pipeline("image-to-text", model="microsoft/trocr-large-printed")     
 st.title("Duh!")
 
 # load image from the IAM dataset
 url = "https://parivahan.gov.in/rcdlstatus/DispplayCaptcha?txtp_cd=1&bkgp_cd=2&noise_cd=2&gimp_cd=3&txtp_length=5&pfdrid_c=true?1429026471&pfdrid_c=true"
-image = Image.open(requests.get(url, stream=True).raw)
+image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
 
 col1, col2 = st.columns(2)
-predictions = pipeline(image)
+processor = TrOCRProcessor.from_pretrained('microsoft/trocr-large-printed')
+model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-large-printed')
+pixel_values = processor(images=image, return_tensors="pt").pixel_values
+
+generated_ids = model.generate(pixel_values)
+generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+
 col1.image(image, use_column_width=True)
-col2.subheader(f"Detected Text: {predictions}")
+col2.subheader(f"Detected Text: {generated_text}")