app.py

# app.py
"""Streamlit UI for SentiBot — delegates all analysis to analyzer.py."""
from __future__ import annotations

import csv
import io
from typing import Any

import pandas as pd
import streamlit as st

from analyzer import analyze_sentiment, load_csv, save_results


# ── helpers ───────────────────────────────────────────────────────────────────

def _label_color(label: str) -> str:
    """Map sentiment label to a Streamlit color name."""
    return {"positive": "green", "negative": "red"}.get(label, "gray")


def _result_card(result: dict[str, Any]) -> None:
    """Render a color-coded sentiment result card."""
    color = _label_color(result["label"])
    confidence = result["confidence"]
    st.markdown(
        f'<div style="border-left:4px solid {color};padding-left:12px;">',
        unsafe_allow_html=True,
    )
    col1, col2, col3 = st.columns(3)
    col1.metric("Sentiment", result["label"].capitalize())
    col2.metric("Confidence", f"{confidence:.0%}")
    col3.metric("Tokens Used", result["tokens_used"])
    st.progress(confidence)
    st.markdown("</div>", unsafe_allow_html=True)


# ── tabs ──────────────────────────────────────────────────────────────────────

def render_text_tab() -> None:
    """Render the single-text analysis tab."""
    text = st.text_area(
        "Text to analyze",
        placeholder="Type or paste text here…",
        label_visibility="collapsed",
    )
    if st.button("Analyze"):
        if not text or not text.strip():
            st.warning("Please enter some text.")
            return
        try:
            with st.spinner("Analyzing…"):
                result = analyze_sentiment(text)
            _result_card(result)
        except Exception as exc:  # noqa: BLE001
            st.error(f"Error: {exc}")


def render_batch_tab() -> None:
    """Render the CSV batch analysis tab."""
    uploaded_file = st.file_uploader("Upload a CSV file", type=["csv"])

    if st.button("Analyze Batch", disabled=uploaded_file is None):
        try:
            content = uploaded_file.getvalue().decode("utf-8")
            reader = csv.reader(io.StringIO(content))
            next(reader, None)  # skip header row
            texts = [row[0] for row in reader if row]

            if not texts:
                st.warning("No data rows found in the CSV file.")
                return

            results: list[dict[str, Any]] = []
            progress = st.progress(0.0)
            for i, text in enumerate(texts):
                results.append(analyze_sentiment(text))
                progress.progress((i + 1) / len(texts))

            df = pd.DataFrame(
                results,
                columns=["text", "label", "confidence", "tokens_used", "model"],
            )
            st.dataframe(df)

            buf = io.StringIO()
            writer = csv.DictWriter(
                buf,
                fieldnames=["text", "label", "confidence", "tokens_used", "model"],
            )
            writer.writeheader()
            writer.writerows(results)

            st.download_button(
                label="Download Results CSV",
                data=buf.getvalue().encode("utf-8"),
                file_name="sentiment_results.csv",
                mime="text/csv",
            )
        except Exception as exc:  # noqa: BLE001
            st.error(f"Error: {exc}")


# ── page ──────────────────────────────────────────────────────────────────────

st.set_page_config(page_title="SentiBot", layout="wide")
st.title("SentiBot")

tab_text, tab_batch = st.tabs(["Analyze Text", "Batch CSV"])
with tab_text:
    render_text_tab()
with tab_batch:
    render_batch_tab()