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The invoice data extraction code

app.py

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+import streamlit as st
+from dotenv import load_dotenv
+
+from pipeline import create_docs
+
+
+
+def main():
+    load_dotenv()
+
+    st.set_page_config(page_title="Invoice Extraction Bot")
+    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 it...'):
+            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, 
+                "benchmark-tools.csv",
+                "text/csv",
+                key="download-tools-csv",
+            )
+        st.success("Hope I was able to save your time❤️")
+
+
+#Invoking main function
+if __name__ == '__main__':
+    main()

pipeline.py

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+# from langchain.llms import OpenAI
+from langchain_openai import OpenAI
+from pypdf import PdfReader
+from langchain.llms.openai import OpenAI
+import pandas as pd
+import re
+import replicate
+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 = """Extract all the following values : invoice no., Description, Quantity, date, 
+        Unit price , Amount, Total, email, phone number and address from this data: {pages}
+
+        Expected output: remove any dollar symbols {{'Invoice no.': '1001329','Description': 'Office Chair','Quantity': '2','Date': '5/4/2023','Unit price': '1100.00','Amount': '2200.00','Total': '2200.00','Email': 'Santoshvarma0988@gmail.com','Phone number': '9999999999','Address': 'Mumbai, India'}}
+        """
+    prompt_template = PromptTemplate(input_variables=["pages"], template=template)
+
+    llm = OpenAI(temperature=.7)
+    full_response=llm(prompt_template.format(pages=pages_data))
+    
+
+    #The below code will be used when we want to use LLAMA 2 model,  we will use Replicate for hosting our model....
+    
+    #output = replicate.run('replicate/llama-2-70b-chat:2c1608e18606fad2812020dc541930f2d0495ce32eee50074220b87300bc16e1', 
+                           #input={"prompt":prompt_template.format(pages=pages_data) ,
+                                  #"temperature":0.1, "top_p":0.9, "max_length":512, "repetition_penalty":1})
+    
+    #full_response = ''
+    #for item in output:
+        #full_response += item
+    
+
+    #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 no.': pd.Series(dtype='str'),
+                   'Description': pd.Series(dtype='str'),
+                   'Quantity': pd.Series(dtype='str'),
+                   'Date': 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'),
+                   'Address': pd.Series(dtype='str')
+                    })
+
+    for filename in user_pdf_list:
+        
+        print(filename)
+        raw_data=get_pdf_text(filename)
+        print(raw_data)
+        print("extracted raw data")
+
+        llm_extracted_data=extracted_data(raw_data)
+        print("llm extracted data")
+        #Adding items to our list - Adding data & its metadata
+
+        pattern = r'{(.+)}'
+        match = re.search(pattern, llm_extracted_data, re.DOTALL)
+
+        data_dict = {}
+
+        if match:
+            extracted_text = match.group(1)
+            # Converting the extracted text to a dictionary
+            data_dict = eval('{' + extracted_text + '}')
+            print(data_dict)
+        else:
+            print("No match found.")
+        
+        df=df._append([data_dict], ignore_index=True)
+        print("********************DONE***************")
+        #df=df.append(save_to_dataframe(llm_extracted_data), ignore_index=True)
+
+    df.head()
+    return df

requirements.txt

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+langchain==0.0.351
+streamlit==1.29.0
+openai==1.5.0
+python-dotenv==1.0.0
+pypdf==3.17.3
+replicate==0.9.0