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Browse files- app.py +59 -0
- requirements.txt +5 -0
app.py
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import gradio as gr
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from transformers import pipeline
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import PyPDF2
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from docx import Document
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# Load pipelines
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classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
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ner = pipeline("ner", model="Jean-Baptiste/roberta-large-ner-english", grouped_entities=True)
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# File reading
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def read_file(file_obj):
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name = file_obj.name
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if name.endswith(".txt"):
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return file_obj.read().decode("utf-8")
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elif name.endswith(".pdf"):
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reader = PyPDF2.PdfReader(file_obj)
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return " ".join([page.extract_text() for page in reader.pages if page.extract_text()])
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elif name.endswith(".docx"):
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doc = Document(file_obj)
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return "\n".join([para.text for para in doc.paragraphs])
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else:
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return "Unsupported file format"
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# Contract classification
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def is_contract(text):
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result = classifier(text[:1000], ["contract", "not a contract"])
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return result['labels'][0] == 'contract', result
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# Party extraction
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def extract_parties(text):
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entities = ner(text[:1000])
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return list(set(ent['word'] for ent in entities if ent['entity_group'] in ['ORG', 'PER']))
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# Main logic
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def process_file(file):
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text = read_file(file)
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if not text.strip():
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return "Empty or unreadable file.", None
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is_contract_flag, classification = is_contract(text)
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if is_contract_flag:
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parties = extract_parties(text)
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return "✅ This is a contract.", parties
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else:
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return "❌ This is NOT a contract.", []
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# Gradio interface
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iface = gr.Interface(
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fn=process_file,
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inputs=gr.File(file_types=[".txt", ".pdf", ".docx"], label="Upload a document"),
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outputs=[
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gr.Textbox(label="Classification Result"),
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gr.Label(label="Detected Parties")
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],
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title="Contract Classifier with RoBERTa",
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description="Upload a document (.pdf, .txt, .docx) to detect if it's a contract and extract involved parties using RoBERTa."
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)
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iface.launch()
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requirements.txt
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gradio
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transformers
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torch
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python-docx
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PyPDF2
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