Revolutionizing PDF Workflows with AI: Next-Gen Automation for Developers (2025)

import fitz  

def extract_text_with_layout(pdf_path):  
    doc = fitz.open(pdf_path)  
    full_text = []  
    for page in doc:  
        blocks = page.get_text("blocks")  
        for block in blocks:  
            x0, y0, x1, y1, text, block_no, block_type = block  
            if block_type == 0:  # Text block  
                full_text.append(f"{text}\n")  
    return "\n".join(full_text)  

contract_text = extract_text_with_layout("contract.pdf")

import openai  

response = openai.ChatCompletion.create(  
    model="gpt-4-1106-preview",  
    messages=[{  
        "role": "user",  
        "content": f"Summarize this contract in 3 bullet points:\n{contract_text}"  
    }]  
)  
print(response.choices[0].message.content)

- Term: 2 years, auto-renewal unless 60-day notice.  
- Liability capped at 1.5x annual fees.  
- Governing law: California.

from transformers import pipeline  
summarizer = pipeline("summarization", model="meta-llama/Llama-2-7b-chat-hf")  
print(summarizer(contract_text, max_length=150))

import boto3  

textract = boto3.client("textract")  
response = textract.analyze_document(  
    Document={"S3Object": {"Bucket": "your-bucket", "Name": "doc.pdf"}},  
    FeatureTypes=["FORMS", "TABLES"]  
)  

# Save JSON annotations for ML training  
with open("training_data.json", "w") as f:  
    json.dump(response, f)

import spacy  
from spacy.training.example import Example  

nlp = spacy.blank("en")  
ner = nlp.add_pipe("ner")  

# Add labels from Textract data  
for entity in ["CONTRACT_TERM", "LIABILITY_CLAUSE"]:  
    ner.add_label(entity)  

# Train model  
optimizer = nlp.begin_training()  
for epoch in range(10):  
    losses = {}  
    for example in training_data:  
        doc = nlp.make_doc(example["text"])  
        example = Example.from_dict(doc, {"entities": example["annotations"]})  
        nlp.update([example], losses=losses)  
nlp.to_disk("pdf_ner_model")

from presidio_analyzer import AnalyzerEngine  
from presidio_anonymizer import AnonymizerEngine  

analyzer = AnalyzerEngine()  
anonymizer = AnonymizerEngine()  

def redact_pii(text):  
    results = analyzer.analyze(text=text, language="en")  
    return anonymizer.anonymize(text, results).text  

contract_text = extract_text_with_layout("contract.pdf")  
safe_text = redact_pii(contract_text)  # Removes SSNs, addresses, etc.  

from langchain.chains import LLMChain  
from langchain.prompts import PromptTemplate  

template = """Is this text GDPR compliant?  
{text}  
Answer: [Yes/No] and explain in one line."""  

prompt = PromptTemplate(template=template, input_variables=["text"])  
chain = LLMChain(llm=llm, prompt=prompt)  
print(chain.run(contract_text))

No - Missing data retention period (GDPR Article 5(1)(e)).

from tensorflow.keras.layers import Input, LSTM, Conv2D  
from tensorflow.keras.models import Model  

inputs = Input(shape=(128, 128, 1))  
x = Conv2D(64, (3,3), activation='relu')(inputs)  
x = LSTM(128)(x)  
outputs = Dense(vocab_size, activation='softmax')(x)  

model = Model(inputs, outputs)  
model.compile(optimizer='adam', loss='categorical_crossentropy')  
model.fit(train_images, train_labels, epochs=10)

def ocr_with_self_healing(image):  
    text = pytesseract.image_to_string(image)  
    corrected = model.predict(preprocess(image))  
    return corrected  

scanned_pages = convert_from_path("scanned.pdf")  
for page in scanned_pages:  
    corrected_text = ocr_with_self_healing(page)