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import streamlit as st
import PyPDF2
from docx import Document
import json
from google import genai
from dotenv import load_dotenv
import os
import re
import pandas as pd
# Load API Key from .env or environment variable (for Hugging Face Spaces)
load_dotenv()
api_key = os.getenv("GEMINI_API_KEY")
if not api_key:
st.error("❌ Gemini API key not found. Please set GEMINI_API_KEY.")
st.stop()
# Utility: Extract text from PDF
def extract_text_from_pdf(file):
reader = PyPDF2.PdfReader(file)
text = ""
for page in reader.pages:
content = page.extract_text()
if content:
text += content + "\n"
return text.strip()
# Utility: Extract text from DOCX
def extract_text_from_docx(file):
doc = Document(file)
return "\n".join([para.text for para in doc.paragraphs]).strip()
# Parse Gemini JSON response
def safe_parse_json(response_text):
try:
clean_text = re.sub(r"^```(?:json)?|```$", "", response_text.strip(), flags=re.MULTILINE)
return json.loads(clean_text)
except Exception as e:
st.error("⚠️ Could not parse Gemini response as JSON. Showing raw response.")
return {
"summary": response_text,
"highlights": None,
"glossary": None
}
# Call Gemini API
def call_gemini_api(document_text):
client = genai.Client(api_key=api_key)
prompt = (
f"Analyze the following legal document:\n\n{document_text}\n\n"
"Instructions:\n"
"- Summarize the key points of the document.\n"
"- Highlight obligations, rights, and critical clauses (as a list of objects with 'clause' and 'description').\n"
"- Provide simplified explanations of complex legal terms (as a dictionary).\n"
"Return the result as JSON with keys: 'summary', 'highlights', 'glossary'."
)
response = client.models.generate_content(
model="gemini-2.0-flash",
contents=prompt
)
return safe_parse_json(response.text)
# Render Highlights as Table
def render_highlights(highlights):
if isinstance(highlights, list) and all(isinstance(item, dict) for item in highlights):
df = pd.DataFrame(highlights)
st.table(df)
elif isinstance(highlights, str):
st.markdown(highlights)
else:
st.info("No highlights available.")
# Render Glossary as Table
def render_glossary(glossary):
if isinstance(glossary, dict):
glossary_list = [{"Term": term, "Explanation": explanation} for term, explanation in glossary.items()]
df = pd.DataFrame(glossary_list)
st.table(df)
elif isinstance(glossary, str):
st.markdown(glossary)
else:
st.info("No glossary available.")
# Main App
def main():
st.set_page_config(page_title="Legal Document Summarizer", layout="wide")
st.title("📄 Legal Document Summarizer")
st.caption("Upload a legal document (PDF or DOCX) to get a summary, key highlights, and glossary of legal terms.")
uploaded_file = st.file_uploader("Upload your document", type=["pdf", "docx"])
if uploaded_file:
if uploaded_file.type == "application/pdf":
document_text = extract_text_from_pdf(uploaded_file)
elif uploaded_file.type == "application/vnd.openxmlformats-officedocument.wordprocessingml.document":
document_text = extract_text_from_docx(uploaded_file)
else:
st.error("Unsupported file format.")
return
if not document_text.strip():
st.error("No text extracted from the document.")
return
st.subheader("📄 Document Preview")
st.text_area("Extracted Text", document_text, height=300)
if st.button("Summarize Document"):
with st.spinner("Calling Gemini..."):
result = call_gemini_api(document_text)
st.subheader("📝 Summary")
st.write(result.get("summary", "No summary found."))
st.subheader("📌 Highlights")
render_highlights(result.get("highlights"))
st.subheader("📘 Glossary")
render_glossary(result.get("glossary"))
if __name__ == "__main__":
main()
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