Create app.py

app.py

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+
+
+import streamlit as st
+import pytesseract
+from tempfile import NamedTemporaryFile
+from langchain.document_loaders import PyPDFLoader
+from langchain.llms import CTransformers
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+
+def main():
+    st.title("Invoice Entity Extractor 📚")
+
+    uploaded_file = st.sidebar.file_uploader("Upload a PDF file", type="pdf")
+    uploaded_image = st.sidebar.file_uploader("Upload an image", type=["png", "jpg", "jpeg"])
+
+    if uploaded_file is not None:
+        process_pdf(uploaded_file)
+    elif uploaded_image is not None:
+        process_image(uploaded_image)
+
+def process_pdf(uploaded_file):
+    # Save the uploaded file to a temporary location
+    with NamedTemporaryFile(delete=False) as temp_file:
+        temp_file.write(uploaded_file.read())
+        temp_file_path = temp_file.name
+
+    loader = PyPDFLoader(temp_file_path)
+    pages = loader.load()
+
+    st.write(f"Number of pages: {len(pages)}")
+
+    for page in pages:
+        st.write(page.page_content)
+
+    llm = CTransformers(model="llama-2-7b-chat.ggmlv3.q4_0.bin",model_type="llama",
+                config={'max_new_tokens':128,'temperature':0.01})
+    
+    template = """Extract invoice number, name of organization, address, date, 
+        Qty, Rate ,Tax ,Amount {pages}
+    Output : entity : type
+    """
+    prompt_template = PromptTemplate(input_variables=["pages"], template=template)
+    chain = LLMChain(llm=llm, prompt=prompt_template)
+
+    result = chain.run(pages=pages[0].page_content)
+    
+    st.write("Extracted entities:")
+    entities = result.strip().split("\n")
+    table_data = [line.split(":") for line in entities]
+    st.table(table_data)
+
+def process_image(uploaded_image):
+    # Process the uploaded image
+    st.write("Image processing is not implemented yet.")
+
+if __name__ == "__main__":
+    main()