Create app.py

app.py

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+from langchain.chat_models import ChatOpenAI
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+from dotenv import load_dotenv
+from pytesseract import image_to_string
+from PIL import Image
+from io import BytesIO
+import pypdfium2 as pdfium
+import streamlit as st
+import multiprocessing
+from tempfile import NamedTemporaryFile
+import pandas as pd
+import json
+import requests
+
+OPENAI_API_KEY = "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"
+
+# 1. Convert PDF file into images via pypdfium2
+
+
+def convert_pdf_to_images(file_path, scale=300/72):
+    print("convert_pdf_to_images:")
+
+    pdf_file = pdfium.PdfDocument(file_path)
+
+    page_indices = [i for i in range(len(pdf_file))]
+
+    renderer = pdf_file.render(
+        pdfium.PdfBitmap.to_pil,
+        page_indices=page_indices,
+        scale=scale,
+    )
+
+    final_images = []
+
+    for i, image in zip(page_indices, renderer):
+
+        image_byte_array = BytesIO()
+        image.save(image_byte_array, format='jpeg', optimize=True)
+        image_byte_array = image_byte_array.getvalue()
+        final_images.append(dict({i: image_byte_array}))
+    print("convert_pdf_to_images Completed!")
+    
+    return final_images
+
+# 2. Extract text from images via pytesseract
+
+
+def extract_text_from_img(list_dict_final_images):
+    print("extract_text_from_img:")
+
+    image_list = [list(data.values())[0] for data in list_dict_final_images]
+    image_content = []
+
+    for index, image_bytes in enumerate(image_list):
+
+        image = Image.open(BytesIO(image_bytes))
+        raw_text = str(image_to_string(image))
+        image_content.append(raw_text)
+    print("extract_text_from_img completed!")
+    return "\n".join(image_content)
+
+
+def extract_content_from_url(url: str):
+    print("extract_content_from_url:" + url)
+    images_list = convert_pdf_to_images(url)
+    text_with_pytesseract = extract_text_from_img(images_list)
+    print("Content Extracted from URL!")
+    return text_with_pytesseract
+
+# 3. Extract structured info from text via LLM
+def extract_structured_data(content: str, data_points):
+    llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613", openai_api_key=OPENAI_API_KEY)
+    template = """
+    You are an expert admin people who will extract core information from documents
+
+    {content}
+
+    Above is the content; please try to extract all data points from the content above 
+    and export in a JSON array format:
+    {data_points}
+
+    Now please extract details from the content  and export in a JSON array format, 
+    return ONLY the JSON array:
+    """
+
+    prompt = PromptTemplate(
+        input_variables=["content", "data_points"],
+        template=template,
+    )
+
+    chain = LLMChain(llm=llm, prompt=prompt)
+
+    results = chain.run(content=content, data_points=data_points)
+
+    return results
+
+# 5. Streamlit app
+def main():
+    default_data_points = """{
+        "order_id": "what is the order id",
+        "Invoice_Number":"what is the invice number",
+        "order_date":"what is the date of the order",
+        "bill_to":"what is the bill to details i.e. name and the address",
+        "ship_to":"what is the ship to details i.e. name and the address",
+        "Product_name":"what is the name of the product",
+        "Title":"what is the title of the product",
+        "qty": "what is the qty of the product",
+        "cst_%":"what is the cst %",
+        "cst_amount":"What is the cst amount"
+        "taxable value":"what is the taxable value",
+        "total":"what is the total of the product",
+        "Grand_total":"What is the grand totalof the product",
+    }"""
+
+    st.set_page_config(page_title="Doc extraction", page_icon=":bird:")
+
+    st.header("Doc extraction :bird:")
+
+    data_points = st.text_area(
+        "Data points", value=default_data_points, height=170)
+
+    uploaded_files = st.file_uploader(
+        "upload PDFs", accept_multiple_files=True)
+
+    if uploaded_files is not None and data_points is not None:
+        results = []
+        for file in uploaded_files:
+            with NamedTemporaryFile(dir='.', suffix='.csv') as f:
+                f.write(file.getbuffer())
+                content = extract_content_from_url(f.name)
+                print(content)
+                data = extract_structured_data(content, data_points)
+                json_data = json.loads(data)
+                if isinstance(json_data, list):
+                    results.extend(json_data)  # Use extend() for lists
+                else:
+                    results.append(json_data)  # Wrap the dict in a list
+
+        if len(results) > 0:
+            try:
+                df = pd.DataFrame(results)
+                st.subheader("Results")
+                st.data_editor(df)
+                if st.button("Sync to Make"):
+                    send_to_make(results)
+                    st.write("Synced to Make!")
+            except Exception as e:
+                st.error(
+                    f"An error occurred while creating the DataFrame: {e}")
+                st.write(results)  # Print the data to see its content
+
+
+if __name__ == '__main__':
+    multiprocessing.freeze_support()
+    main()