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app.py

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+# Description: This file contains  code for streamlit  application which is used to extract invoice data from pdf files.
+import streamlit as st
+from dotenv import load_dotenv
+from utils import *
+
+
+def main():
+    load_dotenv()
+
+    st.set_page_config(page_title="Invoice Parser Agent")
+    st.title("Invoice Extraction Bot 🤖")
+    st.subheader("I can help you in extracting invoice data")
+    
+
+    # Upload the Invoices (pdf files)
+    pdf = st.file_uploader("Upload invoices here, only PDF files allowed", type=["pdf"],accept_multiple_files=True)
+    
+
+    submit=st.button("Extract Data")
+
+    if submit:
+        with st.spinner('Wait for extraction to complete...🔄'):
+            df=create_docs(pdf)
+            st.write(df.head())
+
+            data_as_csv= df.to_csv(index=False).encode("utf-8")
+            st.download_button(
+                "Download data as CSV", 
+                data_as_csv, 
+                "ConsolidatedInvoices.csv",
+                "text/csv",
+                key="ConsolidatedInvoices-csv",
+            )
+        st.success("Extraction complete")
+
+
+#Invoking main function
+if __name__ == '__main__':
+    main()

requirements.txt

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+langchain
+streamlit
+openai
+tiktoken
+python-dotenv
+faiss-cpu
+pypdf
+
+

utils.py

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+from os import write
+from langchain.llms import OpenAI
+from pypdf import PdfReader
+from langchain.llms.openai import OpenAI
+import pandas as pd
+import re
+import streamlit as st
+from langchain.prompts import PromptTemplate
+
+#Extract Information from PDF file
+def get_pdf_text(pdf_doc):
+    text = ""
+    pdf_reader = PdfReader(pdf_doc)
+    for page in pdf_reader.pages:
+        text += page.extract_text()
+    return text
+
+
+
+#Function to extract data from text
+def extracted_data(pages_data):
+
+    template = """ 
+        Remove any $ symbols.
+        Extract  the following values  in a JSON format : Invoice,Date, Description, Quantity, 
+        Unit Price , Amount, Total, Email and Phone Number  from this data: {pages}.
+        
+        """
+    prompt_template = PromptTemplate(input_variables=["pages"], template=template)
+    llm = OpenAI(temperature=.7)
+    full_response=llm(prompt_template.format(pages=pages_data))
+    
+    #print("Full Response")
+    #print(full_response)
+    
+    return full_response
+
+
+# iterate over files in
+# that user uploaded PDF files, one by one
+def create_docs(user_pdf_list):
+    
+    
+    df = pd.DataFrame({'Invoice': pd.Series(dtype='str'),
+                       'Description': pd.Series(dtype='str'),
+		               'Date': pd.Series(dtype='str'),
+                       'Quantity': pd.Series(dtype='str'),
+                       'Unit Price': pd.Series(dtype='str'),
+                       'Amount': pd.Series(dtype='int'),
+                       'Total': pd.Series(dtype='str'),
+                       'Email': pd.Series(dtype='str'),
+                       'Phone Number': pd.Series(dtype='str')
+                        
+                    })
+    
+    for filename in user_pdf_list:
+        
+        print("filename")
+        print(filename)
+        raw_data=get_pdf_text(filename)
+        #st.write("RAWDATA:..."+raw_data)
+        #print("extracted raw data")
+        #print(raw_data)
+        
+        llm_extracted_data=extracted_data(raw_data)
+        #st.write("LLMDATA..."+llm_extracted_data)
+        # Write code to append the extracted data to the dataframe
+       # df = df.append(llm_extracted_data, ignore_index=True)
+        #print("DF start")
+       # print(df)
+        
+        print("llm extracted data")
+        #Adding items to our list - Adding data & its metadata
+        print(llm_extracted_data)
+        pattern = r'{(.+)}'
+        match = re.search(pattern, llm_extracted_data, re.DOTALL)
+
+        if match:
+            extracted_text = match.group(1)
+             # Escape quotes in the extracted text
+            #extracted_text = extracted_text.replace('"', '\\"').replace("'", "\\'")
+            # Converting the extracted text to a dictionary
+            # Remove line continuation characters
+            #extracted_text = extracted_text.replace('\\', '')
+            #print("********************Extracted Text***************")
+            #print(extracted_text)
+            
+            #print("********************Data Dict***************")
+            data_dict = eval('{' + extracted_text + '}')
+            #print(data_dict)
+            #Convert data_dict to a dataframe
+           # df = pd.DataFrame.from_dict(data_dict, orient='index').T
+            #print("********************Data Dict End***************")
+           
+        else:
+            print("No match found.")
+            data_dict = {}
+            
+        #print("********************DF Final start***************")       
+        df = pd.concat([df, pd.DataFrame([data_dict])], ignore_index=True)
+        #print(df)
+        #print("********************DF Final End***************")
+    #df = pd.concat([df, pd.DataFrame.from_dict(data_dict)])
+    #df= pd.concat([df, pd.DataFrame(data_dict, index=[0])], ignore_index=True)
+    #df = pd.concat([df, pd.DataFrame.from_dict(data_dict, orient='columns', index=[0])])
+    #df = pd.concat([df, pd.DataFrame({k: [v] for k, v in data_dict.items()})])
+       
+    
+        # Create a DataFrame from the dictionary
+        # If the dictionary values are not lists, convert them to lists
+        
+        #df=df.append(save_to_dataframe(llm_extracted_data), ignore_index=True)
+
+    df.head()
+    return df