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app.py

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+import streamlit as st
+from pdf2image import convert_from_path
+import google.generativeai as genai
+import os
+import imghdr
+import PIL.Image
+import json
+import tempfile
+from dotenv import load_dotenv
+load_dotenv()
+
+# Configure Gemini API
+GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
+genai.configure(api_key=GOOGLE_API_KEY)
+model = genai.GenerativeModel('gemini-1.5-flash')
+
+def system_prompt() -> str:
+    return """You are a Invoice/Receipt Analysing tool. Analyse the image of the invoice provided and extract information from the following receipt image and return a JSON object with these exact keys:Customer_Details,Products,Total Amount.
+
+    Rules:
+    1. For total_cost, use the highest monetary value in the text.
+    2. Customer_Details will consist of another json object consisting the keys Customer_Name,Customer_Address,Email Address,Phone Number,Customer ID,Billing Address,Shipping Address,Account Number,Tax ID/VAT Number,Company Name,Payment Method.
+    3.Products will consist of another json object consisting of keys Product_name_1,quantity,unit_price.
+    6. If any value is not found, return null.
+    7. If all values are null, return null.
+    Ensure the strictly that output is a valid JSON object containing strictly the above keys, without any explanations.
+    Generate a JSON response in the following format without using the ```json block. Ensure the output is properly formatted as plain text JSON.
+
+    """
+
+def get_invoice_details(image):
+    response = model.generate_content([system_prompt(), image], stream=True)
+    response.resolve()
+    return response.text
+
+def process_file(file):
+    images = []
+    if file.type == "application/pdf":
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp_file:
+            tmp_file.write(file.getvalue())
+            tmp_file_path = tmp_file.name
+        
+        pdf_images = convert_from_path(tmp_file_path)
+        for img in pdf_images:
+            images.append(img)
+        
+        os.unlink(tmp_file_path)
+    elif file.type.startswith('image'):
+        image = PIL.Image.open(file)
+        images.append(image)
+    else:
+        st.error(f"Unsupported file type: {file.type}")
+        return None
+
+    return images
+
+def main():
+    st.title("Invoice Analyzer")
+
+    uploaded_file = st.file_uploader("Choose an image or PDF file", type=["jpg", "jpeg", "png", "pdf"])
+
+    if uploaded_file is not None:
+        images = process_file(uploaded_file)
+
+        if images:
+            for i, img in enumerate(images):
+                st.image(img, caption=f"Page {i+1}", use_column_width=True)
+                
+                with st.spinner(f"Analyzing page {i+1}..."):
+                    json_output = get_invoice_details(img)
+                
+                try:
+                    parsed_json = json.loads(json_output)
+                    st.json(parsed_json)
+                except json.JSONDecodeError:
+                    st.error(f"Failed to parse JSON for page {i+1}. Raw output:")
+                    st.text(json_output)
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+streamlit
+pdf2image
+google-generativeai
+Pillow