Update app.py

app.py

@@ -1,64 +1,64 @@
-import streamlit as st
-from dataclasses import dataclass
-import pytesseract
-from PIL import Image
-import io
-import re
-import cv2
-import numpy as np
-import OCR
-
-from  OCR import *
-
-# Initialize chat history
-if "messages" not in st.session_state:
-    st.session_state.messages = [{"role": "Invoice Reader", "content": "Submit an invoice and I will read it."}]
-
-# Display chat messages from history on app rerun
-for message in st.session_state.messages:
-    with st.chat_message(message["role"]):
-        st.markdown(message["content"])
-
-USER = "user"
-ASSISTANT = "Invoice Reader"
-
-# Accept file uploads
-uploaded_file = st.file_uploader("Upload an invoice", type=["pdf", "png", "jpg", "jpeg"])
-if uploaded_file is not None:
-    # Display uploaded file content
-    file_content = uploaded_file.getvalue()
-    st.session_state.messages.append({"role": USER, "content": f"Uploaded file: {uploaded_file.name}"})
-    with st.chat_message(USER):
-        st.markdown(f"Uploaded file: {uploaded_file.name}")
-
-    # Preprocess and extract text from image or PDF
-    try:
-        if uploaded_file.type == "application/pdf":
-            text = extract_text_from_pdf(file_content)
-        else:
-            text = extract_text_from_image(file_content)
-
-        # Extract specific details
-        details = extract_invoice_details(text)
-
-        # Create and display assistant's response to extracted text
-        assistant_response = (
-            f"Extracted text from the uploaded file:\n\n{text}\n\n"
-            f"**Extracted Details:**\n"
-            f"**Invoice Number:** {details['Invoice Number']}\n"
-            
-            f"**Amount:** {details['Amount']}\n"
-            f"**Invoice Date:** {details['Invoice Date']}\n"
-            f"**Due Date:** {details['Due Date']}"
-        )
-        st.session_state.messages.append({"role": ASSISTANT, "content": assistant_response})
-        with st.chat_message(ASSISTANT):
-            st.markdown(assistant_response)
-    except Exception as e:
-        error_message = f"An error occurred while processing the file: {e}"
-        st.session_state.messages.append({"role": ASSISTANT, "content": error_message})
-        with st.chat_message(ASSISTANT):
-            st.markdown(error_message)
-
-
+import streamlit as st
+from dataclasses import dataclass
+import pytesseract
+from PIL import Image
+import io
+import re
+import cv2
+import numpy as np
+import OCR
+import os
+from  OCR import *
+
+# Initialize chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = [{"role": "Invoice Reader", "content": "Submit an invoice and I will read it."}]
+
+# Display chat messages from history on app rerun
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+HF_TOKEN = os.environ.get("HF_TOKEN")
+USER = "user"
+ASSISTANT = "Invoice Reader"
+
+# Accept file uploads
+uploaded_file = st.file_uploader("Upload an invoice", type=["pdf", "png", "jpg", "jpeg"])
+if uploaded_file is not None:
+    # Display uploaded file content
+    file_content = uploaded_file.getvalue()
+    st.session_state.messages.append({"role": USER, "content": f"Uploaded file: {uploaded_file.name}"})
+    with st.chat_message(USER):
+        st.markdown(f"Uploaded file: {uploaded_file.name}")
+
+    # Preprocess and extract text from image or PDF
+    try:
+        if uploaded_file.type == "application/pdf":
+            text = extract_text_from_pdf(file_content)
+        else:
+            text = extract_text_from_image(file_content)
+
+        # Extract specific details
+        details = extract_invoice_details(text)
+
+        # Create and display assistant's response to extracted text
+        assistant_response = (
+            f"Extracted text from the uploaded file:\n\n{text}\n\n"
+            f"**Extracted Details:**\n"
+            f"**Invoice Number:** {details['Invoice Number']}\n"
+            
+            f"**Amount:** {details['Amount']}\n"
+            f"**Invoice Date:** {details['Invoice Date']}\n"
+            f"**Due Date:** {details['Due Date']}"
+        )
+        st.session_state.messages.append({"role": ASSISTANT, "content": assistant_response})
+        with st.chat_message(ASSISTANT):
+            st.markdown(assistant_response)
+    except Exception as e:
+        error_message = f"An error occurred while processing the file: {e}"
+        st.session_state.messages.append({"role": ASSISTANT, "content": error_message})
+        with st.chat_message(ASSISTANT):
+            st.markdown(error_message)
+
+
 #streamlit run C:/Users/leahw/PycharmProjects/Int-to-Artificial-Intelligence-Final-Project/app.py