Create app.py

app.py

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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()