Update app.py

app.py

@@ -5,7 +5,6 @@ import torch
 import easyocr
 
 # Load the question-answering model and tokenizer
-#model_name = "t5-base"
 model_name = "google/flan-t5-base"
 qa_model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
 qa_tokenizer = AutoTokenizer.from_pretrained(model_name)
@@ -33,24 +32,31 @@ def get_response_from_llm(extracted_text, question):
     return response
 
 # Streamlit App
-st.set_page_config(page_title="Invoice Extractor")
-st.header("Invoice Extractor")
+st.set_page_config(page_title="Invoice Extractor", layout="wide")
+st.title("Invoice Extractor")
 
-uploaded_file = st.file_uploader("Upload an invoice image...", type=["jpg"])
-question = st.text_input("Enter your question about the invoice:")
-image = None
+# Sidebar for uploading image and entering question
+st.sidebar.title("Upload Image and Enter Question")
+uploaded_file = st.sidebar.file_uploader("Upload an invoice image...", type=["jpg", "jpeg", "png"])
+question = st.sidebar.text_input("Enter your question about the invoice:")
+
+# Main content area
+col1, col2 = st.columns([1, 3])
 
 if uploaded_file is not None:
-    image = Image.open(uploaded_file)
-    st.image(image, caption="Uploaded Image.", use_column_width=True)
-
-submit = st.button("Extract Information")
-
-if submit:
-    if image is None:
-        st.warning("Please upload an image.")
-    else:
-        extracted_text = extract_text_from_image(image)
-        response = get_response_from_llm(extracted_text, question)
-        st.subheader("Extracted Information:")
-        st.write(response)
+    with col1:
+        st.subheader("Uploaded Image")
+        image = Image.open(uploaded_file)
+        st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    with col2:
+        st.subheader("Extracted Information")
+        submit = st.button("Extract Information")
+
+        if submit:
+            if image is None:
+                st.warning("Please upload an image.")
+            else:
+                extracted_text = extract_text_from_image(image)
+                response = get_response_from_llm(extracted_text, question)
+                st.write(response)