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main.py

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+import os
+import streamlit as st
+from paddleocr import PaddleOCR
+import cv2
+from langchain.chains import LLMChain
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_google_genai import ChatGoogleGenerativeAI
+from dotenv import load_dotenv
+from sqlalchemy import create_engine, Column, Integer, String, JSON
+from sqlalchemy.ext.declarative import declarative_base
+from sqlalchemy.orm import sessionmaker
+import google.generativeai as genai
+
+# Load environment variables
+load_dotenv()
+
+# Set up environment variables
+api_key = os.getenv('API_KEY')
+DATABASE_URL = "sqlite:///mydatabase.db"
+
+# Setup database
+Base = declarative_base()
+
+class MyDataModel(Base):
+    __tablename__ = 'my_data_table'
+    
+    id = Column(Integer, primary_key=True)
+    name = Column(String)
+    data = Column(JSON)
+
+engine = create_engine(DATABASE_URL)
+Session = sessionmaker(bind=engine)
+session = Session()
+
+Base.metadata.create_all(engine)
+
+# Initialize Google Generative AI API
+genai.configure(api_key=api_key)
+
+# Define OCR function using PaddleOCR
+def ocr_with_paddle(img_path):
+    finaltext = ''
+    ocr = PaddleOCR(lang='en', use_angle_cls=True)
+
+    
+    img = cv2.imread(img_path)
+
+   
+    result = ocr.ocr(img)
+
+ 
+    for line in result[0]:  
+        for word_info in line:
+            
+            if isinstance(word_info[1], list):
+                text = word_info[1][0] 
+                text=str(text) 
+                finaltext += text + ' '  
+            else:
+               
+               finaltext += str(word_info)+' '
+
+    return finaltext.strip() 
+
+# Define the prompt template for extracting invoice details
+prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", "You are a helpful assistant that extracts invoice details such as invoice number, customer name, date, amount, and other relevant information from a provided invoice text."),
+        ("human", "{input}"),
+    ]
+)
+
+llm = ChatGoogleGenerativeAI(
+    model="gemini-1.5-pro",
+    temperature=0.5,
+    max_tokens=None,
+    timeout=None,
+    max_retries=2,
+    api_key=api_key
+)
+
+invoice_chain = LLMChain(prompt=prompt, llm=llm)
+
+def extract_invoice_details(input_text):
+    response = invoice_chain({"input": input_text})
+    extracted_details = response["text"].strip()
+    return extracted_details
+
+# Streamlit UI
+st.title("Invoice OCR and Details Extraction")
+
+st.write(
+    "Upload an image file to extract the text and invoice details such as invoice number, customer name, date, and amount."
+)
+
+# Image Upload
+uploaded_image = st.file_uploader("Choose an Image", type=["jpg", "jpeg", "png"])
+
+if uploaded_image is not None:
+    # Save uploaded image to a temporary file
+    img_path = "temp_image.png"
+    with open(img_path, "wb") as f:
+        f.write(uploaded_image.getbuffer())
+    
+    # Perform OCR on the uploaded image
+    text = ocr_with_paddle(img_path)
+    st.write("Extracted Text:")
+    st.text_area("OCR Output", text, height=300)
+    
+    # Extract invoice details from the text
+    invoice_details = extract_invoice_details(text)
+    
+    st.write("Extracted Invoice Details:")
+    st.text_area("Invoice Details", invoice_details, height=300)
+    
+    # Save details to the database
+    new_entry = MyDataModel(name="invoice_details", data=invoice_details)
+    session.add(new_entry)
+    session.commit()
+    session.close()
+    
+    st.success("Invoice details saved to the database!")