app.py

import os
import gradio as gr
import fitz  # PyMuPDF
from typing import List

from groq import Groq
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.docstore.document import Document
from langchain.chains import RetrievalQA
from langchain.llms.base import LLM

# === Groq Client Setup ===
client = Groq(api_key=os.environ.get("GROQ_API_KEY"))

# === Custom LLM Wrapper ===
class GroqLLM(LLM):
    model: str = "llama3-70b-8192"

    def __init__(self, model: str = None):
        super().__init__()
        if model:
            self.model = model

    def _call(self, prompt: str, stop: List[str] = None) -> str:
        try:
            response = client.chat.completions.create(
                model=self.model,
                messages=[{"role": "user", "content": prompt}]
            )
            return response.choices[0].message.content.strip()
        except Exception as e:
            return f"[Groq API Error] {str(e)}"

    @property
    def _llm_type(self) -> str:
        return "groq_llm"

# === Input Extraction ===
def extract_text(file=None, clipboard=None):
    try:
        if file:
            doc = fitz.open(file.name)
            return " ".join(page.get_text() for page in doc)
        elif clipboard:
            return clipboard
    except Exception as e:
        return f"[Extract Error] {str(e)}"
    return ""

# === Preprocessing & Vector Store Setup ===
def process_text(input_text):
    splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
    texts = splitter.split_text(input_text)
    docs = [Document(page_content=t) for t in texts]

    embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
    db = FAISS.from_documents(docs, embeddings)

    retriever = db.as_retriever()
    qa_chain = RetrievalQA.from_chain_type(
        llm=GroqLLM(), retriever=retriever, return_source_documents=True
    )
    return qa_chain

# === Core RAG Handler ===
def handle_input(file, clipboard, query):
    try:
        raw_text = extract_text(file, clipboard)
        if not raw_text:
            return "Please provide either a PDF or clipboard text."

        # Your prompt for explanation + risks
        default_query = (
            "Explain this policy in simple terms and highlight the risks for the user. "
            "Provide bullet points for risks."
        )
        user_query = query if query else default_query

        qa = process_text(raw_text)
        response = qa.invoke({"query": user_query})

        result = response["result"]
        sources = response["source_documents"]
        source_preview = "\n\n📄 Sources:\n" + "\n---\n".join(
            [doc.page_content[:300] + "..." for doc in sources[:3]]
        )

        return result + source_preview

    except Exception as e:
        return f"❌ Error: {str(e)}"

# === Gradio UI ===
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("# 🤖 Lexicon: Your Policy Explainer Bot")

    with gr.Row():
        file_input = gr.File(label="Upload PDF", file_types=[".pdf"])
        clipboard_input = gr.Textbox(label="Or Paste Text", placeholder="Paste policy text here", lines=10)

    query_input = gr.Textbox(label="Ask a Question (optional)", placeholder="e.g., What risks am I agreeing to?")
    submit_btn = gr.Button("🔍 Analyze")
    output = gr.Textbox(label="Output", lines=20)

    submit_btn.click(fn=handle_input, inputs=[file_input, clipboard_input, query_input], outputs=output)

demo.launch()