import streamlit as st
import pandas as pd
import plotly.express as px

from pdf_parser import extract_text
from esg_metrics import extract_scope, extract_revenue, carbon_intensity
from vector_store import index_doc
from hyperrag import build_graph
from analysis import answer
from discourse_graph import add_claim, detect_greenwashing

st.set_page_config(layout="wide")

st.title("🌱 ESG HyperRAG Analyzer")

st.sidebar.header("Upload ESG Report")

file = st.sidebar.file_uploader("Upload ESG PDF", type=["pdf"])

company = st.sidebar.text_input("Company")

sector = st.sidebar.text_input("Sector")

if st.sidebar.button("Analyze"):

    if file and company and sector:

        text = extract_text(file)

        s1 = extract_scope(text,1)
        s2 = extract_scope(text,2)
        s3 = extract_scope(text,3)

        revenue = extract_revenue(text)

        intensity = carbon_intensity(s1,revenue)

        index_doc(text,{
            "company":company,
            "sector":sector,
            "scope1":s1,
            "scope2":s2,
            "scope3":s3,
            "intensity":intensity
        })

        build_graph([text])

        add_claim(company,"low emissions",f"Scope1 {s1}")

        st.success("Report processed")

        df = pd.DataFrame({
            "Scope":["Scope1","Scope2","Scope3"],
            "Value":[s1,s2,s3]
        })

        fig = px.bar(df,x="Scope",y="Value",color="Scope")

        st.plotly_chart(fig,use_container_width=True)

        st.metric("Carbon Intensity", intensity)


st.header("🔎 HyperRAG Query")

q = st.text_input("Ask ESG question")

if st.button("Search"):

    st.text(answer(q))


st.header("🚨 Greenwashing Detection")

if st.button("Check"):

    issues = detect_greenwashing()

    if issues:

        st.error(f"Potential contradictions: {issues}")

    else:

        st.success("No greenwashing detected")