app.py

# app.py
"""
Streamlit UI for the News Sentiment Analyzer.
- Calls the in-process FastAPI orchestrator (NewsAnalyzer) directly for zero-latency on Spaces.
- Lightweight, CPU-safe widgets with progress, charts, tables, and exports (CSV/JSON/PDF + Audio).
"""

from __future__ import annotations

import io
import json
import logging
from datetime import datetime
from typing import Any, Dict, List

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

# Local modules
from api import analyzer  # global NewsAnalyzer instance
from utils import (
    setup_logging,
    load_config,
    calculate_sentiment_distribution,
    format_number,
)
from report import generate_pdf_report  # your existing PDF generator

# ------------------------------------------------------------------------------
# App setup
# ------------------------------------------------------------------------------

setup_logging()
logger = logging.getLogger("app")

st.set_page_config(
    page_title="News Sentiment Analyzer",
    page_icon="📰",
    layout="wide",
)

# Minimal CSS polish
st.markdown(
    """
    <style>
    .small { font-size: 0.85rem; color: #666; }
    .ok { color: #1b8a5a; }
    .bad { color: #b00020; }
    .neutral { color: #666; }
    .stProgress > div > div > div { background-color: #4b8bf4; }
    .block-container { padding-top: 2rem; }
    </style>
    """,
    unsafe_allow_html=True,
)

# ------------------------------------------------------------------------------
# Sidebar controls
# ------------------------------------------------------------------------------

cfg = load_config()

st.sidebar.header("Settings")
default_query = st.sidebar.text_input("Company / Keyword", value="Tesla")
num_articles = st.sidebar.slider("Number of articles", 5, 50, 20, step=1)
languages = st.sidebar.multiselect(
    "Summaries in languages",
    options=["English", "Hindi", "Tamil"],
    default=["English"],
)
include_audio = st.sidebar.checkbox("Generate audio summary", value=True)
sentiment_models = st.sidebar.multiselect(
    "Sentiment models",
    options=["VADER", "Loughran-McDonald", "FinBERT"],
    default=["VADER", "Loughran-McDonald", "FinBERT"],
)
st.sidebar.caption("Tip: disable FinBERT if your Space has < 2GB RAM.")

run_btn = st.sidebar.button("Analyze", use_container_width=True, type="primary")

# ------------------------------------------------------------------------------
# Header
# ------------------------------------------------------------------------------

st.title("📰 News Sentiment Analyzer")
st.caption("Scrape → Summarize → Sentiment → Keywords → Multilingual → Audio — deployed on Hugging Face Spaces")

# ------------------------------------------------------------------------------
# Helper functions
# ------------------------------------------------------------------------------

def _articles_to_df(articles: List[Dict[str, Any]]) -> pd.DataFrame:
    rows = []
    for a in articles:
        rows.append(
            {
                "title": a.get("title", ""),
                "source": a.get("source", ""),
                "date": a.get("date"),
                "url": a.get("url", ""),
                "summary": a.get("summary", ""),
                "sentiment_compound": a.get("sentiment", {}).get("compound", 0.0),
            }
        )
    df = pd.DataFrame(rows)
    if "date" in df.columns:
        try:
            df["date"] = pd.to_datetime(df["date"])
        except Exception:
            pass
    return df


def _render_distribution(dist: Dict[str, Any]):
    cols = st.columns(4)
    cols[0].metric("Total", dist.get("total", 0))
    cols[1].metric("Positive", dist.get("positive", 0))
    cols[2].metric("Negative", dist.get("negative", 0))
    cols[3].metric("Neutral", dist.get("neutral", 0))

    chart_df = pd.DataFrame(
        {
            "Sentiment": ["Positive", "Negative", "Neutral"],
            "Count": [
                dist.get("positive", 0),
                dist.get("negative", 0),
                dist.get("neutral", 0),
            ],
        }
    )
    fig = px.bar(chart_df, x="Sentiment", y="Count", title="Sentiment distribution")
    st.plotly_chart(fig, use_container_width=True)


def _download_buttons(results: Dict[str, Any], df: pd.DataFrame):
    c1, c2, c3 = st.columns(3)

    # JSON
    with c1:
        json_bytes = json.dumps(results, default=str, indent=2).encode("utf-8")
        st.download_button(
            "Download JSON",
            data=json_bytes,
            file_name=f"news_analysis_{results['query']}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json",
            mime="application/json",
            use_container_width=True,
        )

    # CSV
    with c2:
        csv_bytes = df.to_csv(index=False).encode("utf-8")
        st.download_button(
            "Download CSV",
            data=csv_bytes,
            file_name=f"news_analysis_{results['query']}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv",
            mime="text/csv",
            use_container_width=True,
        )

    # PDF
    with c3:
        try:
            pdf_obj = generate_pdf_report(results)

            # Accept bytes, BytesIO/file-like, or path
            pdf_bytes = None
            if isinstance(pdf_obj, (bytes, bytearray)):
                pdf_bytes = pdf_obj
            elif hasattr(pdf_obj, "read"):  # file-like object
                try:
                    pdf_bytes = pdf_obj.read()
                    if not pdf_bytes and hasattr(pdf_obj, "getvalue"):
                        pdf_bytes = pdf_obj.getvalue()
                finally:
                    try:
                        pdf_obj.close()
                    except Exception:
                        pass
            else:
                # Assume file path
                with open(pdf_obj, "rb") as f:
                    pdf_bytes = f.read()

            if not pdf_bytes:
                raise ValueError("Empty PDF bytes")

            st.download_button(
                "Download PDF",
                data=pdf_bytes,
                file_name=f"news_analysis_{results['query']}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.pdf",
                mime="application/pdf",
                use_container_width=True,
            )
        except Exception as e:
            st.info("PDF generator not available or failed. You can still export JSON/CSV.")
            logger.exception(f"PDF generation failed: {e}")



def _render_audio(audio_files: Dict[str, Any]):
    if not audio_files:
        return
    st.subheader("Audio summaries")
    for lang, path in audio_files.items():
        if path:
            st.markdown(f"**{lang}**")
            try:
                with open(path, "rb") as f:
                    st.audio(f.read(), format="audio/mp3")
            except Exception:
                # Some Spaces require passing the path directly
                st.audio(path)


# ------------------------------------------------------------------------------
# Main flow
# ------------------------------------------------------------------------------

if run_btn:
    st.info("Starting analysis… this may take ~30–60 seconds on a CPU Space (FinBERT/summarizer/translation are heavy).")

    progress = st.progress(0, text="Initializing…")

    def _cb(p: int, status: str):
        try:
            progress.progress(p, text=status)
        except Exception:
            pass

    config = {
        "query": default_query,
        "num_articles": num_articles,
        "languages": languages or ["English"],
        "include_audio": include_audio,
        "sentiment_models": sentiment_models or ["VADER", "Loughran-McDonald", "FinBERT"],
    }

    try:
        results: Dict[str, Any] = analyzer.analyze_news(config, progress_callback=_cb)
    except Exception as e:
        progress.empty()
        st.error(f"Analysis failed: {e}")
        st.stop()

    progress.empty()

    # Handle empty gracefully
    if not results.get("articles"):
        st.warning("No articles found or scraping failed. Try a different query or reduce filters.")
        st.stop()

    # Header summary
    st.subheader(f"Results — {results['query']}")
    dist = results["summary"]["distribution"]
    _render_distribution(dist)

    # Keywords
    if results.get("keywords"):
        top_kw = ", ".join(kw["keyword"] for kw in results["keywords"][:12])
        st.markdown(f"**Top keywords:** {top_kw}")

    # Articles table
    df = _articles_to_df(results["articles"])
    st.dataframe(df, use_container_width=True, hide_index=True)

    # Audio (optional)
    if results.get("audio_files"):
        _render_audio(results["audio_files"])

    # Exports
    st.divider()
    _download_buttons(results, df)

else:
    st.info("Enter a company/keyword on the left and click Analyze. Example: Tesla, Nvidia, Reliance, HDFC, Adani, BYD.")

# Footer
st.markdown(
    "<p class='small'>Built with Streamlit + FastAPI · CPU-only · "
    "FinBERT/VADER/LM sentiment · BART/T5 summarization · YAKE keywords · gTTS audio.</p>",
    unsafe_allow_html=True,
)