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app.py

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+import gradio as gr
+import pdfplumber
+import docx
+import os
+import datetime
+from transformers import pipeline
+
+# Load open-source LLMs
+summary_llm = pipeline("summarization", model="google/pegasus-xsum", tokenizer="google/pegasus-xsum")
+text_llm = pipeline("text2text-generation", model="MBZUAI/LaMini-T5-738M", tokenizer="MBZUAI/LaMini-T5-738M")
+
+# Extract text from files
+def extract_text(file):
+    if file.name.endswith(".pdf"):
+        with pdfplumber.open(file.name) as pdf:
+            return "\n".join([p.extract_text() for p in pdf.pages if p.extract_text()])
+    elif file.name.endswith(".docx"):
+        doc = docx.Document(file)
+        return "\n".join([para.text for para in doc.paragraphs])
+    elif file.name.endswith(".txt"):
+        return file.read().decode("utf-8")
+    else:
+        return "Unsupported file format."
+
+# Format glossary visually
+def format_glossary_html(glossary_text):
+    lines = glossary_text.split('\n')
+    html = ""
+    for line in lines:
+        if ":" in line:
+            term, desc = line.split(":", 1)
+            html += f"<b style='color:#1e3a8a'>{term.strip()}</b>: {desc.strip()}<br>"
+        else:
+            html += f"{line}<br>"
+    return html
+
+# Generate summary
+def generate_summary(text):
+    return summary_llm(text[:1024], max_length=250, min_length=80, do_sample=False)[0]["summary_text"]
+
+# Generate text (glossary/verdict/custom)
+def generate_text_response(prompt, max_len=512):
+    return text_llm(prompt, max_length=max_len, do_sample=True)[0]["generated_text"]
+
+# Main document analyzer
+def analyze_document(file):
+    filename = os.path.basename(file.name)
+    text = extract_text(file)
+    if not text.strip():
+        return "No content found in file.", "", "", "", "", None, ""
+
+    short_text = text[:3000]
+
+    # Enhanced prompts
+    summary_prompt = f"""
+You are a legal assistant. Read the following legal document and generate a comprehensive summary.
+
+Include: parties involved, key facts, legal issues, arguments, court observations, and likely outcome.
+
+Document:
+{short_text}
+"""
+    glossary_prompt = f"""
+Extract and explain all legal terms, laws, or references. Format:
+
+Term: ...
+Explanation: ...
+
+Document:
+{short_text}
+"""
+    verdict_prompt = f"""
+Based on the document, predict the likely verdict in 2–3 sentences using standard legal reasoning.
+
+Document:
+{short_text}
+"""
+
+    # Run LLMs
+    summary = generate_summary(short_text)
+    glossary = generate_text_response(glossary_prompt)
+    verdict = generate_text_response(verdict_prompt)
+    glossary_html = format_glossary_html(glossary)
+
+    # Save report
+    timestamp = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+    output_filename = f"LegalSummary_{timestamp}.txt"
+    with open(output_filename, "w", encoding="utf-8") as f:
+        f.write(f"📄 File: {filename}\n🕒 Time: {timestamp}\n\n")
+        f.write("=== 📑 Summary ===\n" + summary + "\n\n")
+        f.write("=== 📘 Glossary ===\n" + glossary + "\n\n")
+        f.write("=== ⚖️ Verdict ===\n" + verdict + "\n")
+
+    return text, summary, glossary, glossary_html, verdict, output_filename, short_text
+
+# Custom prompt answer
+def custom_prompt_response(doc_text, user_prompt):
+    if not doc_text.strip() or not user_prompt.strip():
+        return "⚠️ Please provide both a document and a prompt."
+    prompt = f"""
+You are a legal expert. Answer the question below using only the document provided.
+
+Question:
+{user_prompt.strip()}
+
+Document:
+{doc_text.strip()}
+"""
+    return generate_text_response(prompt)
+
+# Gradio UI
+with gr.Blocks(css="body { background-color: #f9f9f9; font-family: 'Segoe UI'; }") as demo:
+    with gr.Row():
+        with gr.Column(scale=3):
+            gr.Markdown("""
+<div style='text-align: center; font-size: 28px; font-weight: bold; color: #1e3a8a; margin-bottom: 10px;'>
+🧾 Legal Document Summarizer Using LLMs
+</div>
+<div style='text-align: center; font-size: 16px; color: #444444; margin-bottom: 25px;'>
+Upload legal documents in PDF, DOCX, or TXT format to receive structured summaries, legal term glossaries, and AI-inferred verdicts using open-source language models.
+</div>
+""")
+            file_input = gr.File(label="📁 Upload Legal Document")
+            submit_btn = gr.Button("🔍 Analyze Document")
+            download_btn = gr.File(label="⬇️ Download Report")
+
+        with gr.Column(scale=1):
+            gr.Markdown("### 💡 Features")
+            gr.Markdown("""
+- 📝 AI-generated legal summaries  
+- 📘 Glossary of legal terms  
+- ⚖️ Inferred legal verdict  
+- ❓ Custom Q&A based on the document
+""")
+
+    extracted = gr.Textbox(label="📄 Extracted Text", lines=10, interactive=False)
+    summary = gr.Textbox(label="📝 Summary", lines=6, interactive=False)
+    glossary_raw = gr.Textbox(visible=False)
+    glossary_html = gr.HTML(label="📘 Glossary of Legal Terms")
+    final_verdict = gr.Textbox(label="⚖️ Verdict (AI Inferred)", lines=3, interactive=False)
+
+    with gr.Row():
+        gr.Markdown("### ❓ Ask a Question About the Document")
+    user_prompt = gr.Textbox(label="Your Question", placeholder="e.g., What is the legal issue?")
+    custom_response = gr.Textbox(label="🤖 AI Answer", lines=4)
+    custom_btn = gr.Button("🧠 Get Answer")
+    hidden_doc_text = gr.Textbox(visible=False)
+
+    submit_btn.click(fn=analyze_document, inputs=[file_input], outputs=[
+        extracted, summary, glossary_raw, glossary_html, final_verdict, download_btn, hidden_doc_text
+    ])
+    custom_btn.click(fn=custom_prompt_response, inputs=[hidden_doc_text, user_prompt], outputs=custom_response)
+
+demo.launch()

requirements.txt

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+transformers==4.40.1
+torch
+gradio==4.14.0
+pdfplumber==0.10.3
+python-docx==1.1.0