Delete app.py

app.py

@@ -1,93 +0,0 @@
-import PyPDF2
-from pprint import pprint
-from haystack import Pipeline
-from haystack.schema import Document
-from haystack.nodes import BM25Retriever
-from haystack.document_stores import InMemoryDocumentStore
-from haystack.nodes import PreProcessor, PromptTemplate, PromptNode
-from pdf2image import convert_from_path
-import pytesseract
-from PIL import Image
-import gradio as gr
-import os
-
-# Function to extract text from a PDF file using OCR
-def extract_text_from_pdf(pdf_path):
- text = ""
- # Convert PDF pages to images
- images = convert_from_path(pdf_path)
- for image in images:
- # Perform OCR on the image
- text += pytesseract.image_to_string(image)
- return text
-
-# Process and retrieve answers
-def process_invoice(pdf, hf_token, questions):
- # Extract text from the PDF
- extracted_text = extract_text_from_pdf(pdf.name)
- document = Document(content=extracted_text)
- docs = [document]
-
- # Initializing the processor
- processor = PreProcessor(
- clean_empty_lines=True,
- clean_whitespace=True,
- clean_header_footer=True,
- split_by="word",
- split_length=500,
- split_respect_sentence_boundary=True,
- split_overlap=0,
- )
-
- preprocessed_docs = processor.process(docs)
- document_store = InMemoryDocumentStore(use_bm25=True)
- document_store.write_documents(preprocessed_docs)
- retriever = BM25Retriever(document_store, top_k=2)
-
- qa_template = PromptTemplate(prompt=
- """ Using exclusively the information contained in the context, answer only the question asked without adding
- suggestions for possible questions, and respond exclusively in English. If the answer cannot be deduced from the
- context, Don't add anything from the references if it is not asked explicitly. Do not repeat the same information twice
- respond: "Not sure because not relevant to the context.
- Context: {join(documents)};
- Question: {query}
- """)
-
- prompt_node = PromptNode(
- model_name_or_path='mistralai/Mixtral-8x7B-Instruct-v0.1',
- api_key=hf_token,
- default_prompt_template=qa_template,
- max_length=500,
- model_kwargs={"model_max_length": 5000}
- )
-
- rag_pipeline = Pipeline()
- rag_pipeline.add_node(component=retriever, name="retriever", inputs=["Query"])
- rag_pipeline.add_node(component=prompt_node, name="prompt_node", inputs=["retriever"])
-
- answers = {}
- for question in questions.split(','):
- result = rag_pipeline.run(query=question.strip())
- answers[question] = result["results"][0].strip()
-
- return answers
-
-# Gradio interface
-def gradio_interface(pdf, hf_token, questions):
- answers = process_invoice(pdf, hf_token, questions)
- return answers
-
-interface = gr.Interface(
- fn=gradio_interface,
- inputs=[
- gr.inputs.File(file_count="single", type="file", label="Upload Invoice (PDF)"),
- gr.inputs.Textbox(type="password", label="Enter your Hugging Face Token"),
- gr.inputs.Textbox(lines=5, placeholder="Enter your questions separated by commas")
- ],
- outputs="json",
- title="Invoice Data Extraction",
- description="Upload an invoice PDF, provide your Hugging Face token, and get the extracted data based on your questions."
-)
-
-if __name__ == "__main__":
- interface.launch()