first commit

app.py

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+import streamlit as st
+from http import client
+import os,json
+import pandas as pd
+import requests
+from PIL import Image
+from transformers import TrOCRProcessor, VisionEncoderDecoderModel
+st.header("Xelpmoc - Optical Character Recognition - Document AI")
+
+processor = TrOCRProcessor.from_pretrained('microsoft/trocr-base-printed')
+model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-base-printed')
+
+def TrOCR_predict(pixel_values, processor, model):
+    generated_ids = model.generate(pixel_values,output_scores=True,return_dict_in_generate=True, max_length = 64)
+    predicted_text = processor.batch_decode(generated_ids[0], skip_special_tokens=True)
+    return predicted_text
+
+
+df = pd.DataFrame()
+uploaded_file = st.file_uploader("Choose a file")
+if uploaded_file is not None:
+    content = uploaded_file.read()
+    st.image(uploaded_file)
+    image = Image.open(uploaded_file)
+    pixel_values = processor(images=image, return_tensors="pt").pixel_values
+    predicted_text = TrOCR_predict(pixel_values, processor, model)[0]
+    texts = predicted_text
+
+    st.write(texts)

requirements.txt

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+pandas
+streamlit
+numpy
+transformers
+pillow
+torch