Update app.py

app.py

@@ -1,36 +1,36 @@
-from transformers import AutoModel, AutoTokenizer
-import streamlit as st
-from PIL import Image
-import tempfile
-
-
-
-def perform_ocr(image):
-    tokenizer = AutoTokenizer.from_pretrained('srimanth-d/GOT_CPU', trust_remote_code=True)
-    model = AutoModel.from_pretrained('srimanth-d/GOT_CPU', trust_remote_code=True, low_cpu_mem_usage=True, use_safetensors=True, pad_token_id=tokenizer.eos_token_id)
-    model = model.eval()
-    res = model.chat(tokenizer, image, ocr_type='ocr')
-    return res
-
-
-# Title and instructions
-st.title(' OCR and Document Search Web Application Prototype')
-st.write('Upload an image and extract text in Hindi and English. You can also search for keywords within the extracted text.')
-
-# Upload the image
-uploaded_file = st.file_uploader("Choose an image file", type=["jpg", "png", "jpeg"])
-
-# If an image is uploaded
-if uploaded_file is not None:
-    image = Image.open(uploaded_file)
-    st.image(image, caption='Uploaded Image.', use_column_width=True)
-    
-    with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_file:
-        temp_file.write(uploaded_file.getbuffer())
-        temp_file_path = temp_file.name
-
-    # Perform OCR on the uploaded image
-    st.write("Extracting text...")
-    extracted_text = perform_ocr(temp_file_path)
-    st.write("Extracted Text:")
+from transformers import AutoModel, AutoTokenizer
+import streamlit as st
+from PIL import Image
+import tempfile
+
+
+
+def perform_ocr(image):
+    tokenizer = AutoTokenizer.from_pretrained('srimanth-d/GOT_CPU', trust_remote_code=True)
+    model = AutoModel.from_pretrained('srimanth-d/GOT_CPU', trust_remote_code=True, low_cpu_mem_usage=True, use_safetensors=True, pad_token_id=tokenizer.eos_token_id)
+    model = model.eval()
+    res = model.chat(tokenizer, image, ocr_type='ocr')
+    return res
+
+
+# Title and instructions
+st.title(' OCR and Document Search Web Application Prototype')
+st.write('Upload an image and extract text in Hindi and English. You can also search for keywords within the extracted text.')
+
+# Upload the image
+uploaded_file = st.file_uploader("Choose an image file", type=["jpg", "png", "jpeg"])
+
+# If an image is uploaded
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image, caption='Uploaded Image.', use_column_width=True)
+    
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_file:
+        temp_file.write(uploaded_file.getbuffer())
+        temp_file_path = temp_file.name
+
+    # Perform OCR on the uploaded image
+    st.write("Extracting text...")
+    extracted_text = perform_ocr(temp_file_path)
+    st.write("Extracted Text:")
     st.text_area("OCR Output", extracted_text, height=200)