# ------------------------🔧 ENVIRONMENT SETUP ------------------------
import os
os.environ["TRANSFORMERS_CACHE"] = "./hf_cache"

import streamlit as st
from transformers import pipeline
from streamlit_lottie import st_lottie
import requests
import datetime
import pandas as pd

# ------------------------🎞️ LOAD LOTTIE ANIMATION ------------------------
def load_lottieurl(url):
    r = requests.get(url)
    if r.status_code != 200:
        return None
    return r.json()

lottie_animation = load_lottieurl("https://assets2.lottiefiles.com/packages/lf20_w51pcehl.json")

# ------------------------📌 APP TITLE & HEADER ------------------------
st.markdown("<h1 style='text-align: center;'>📝 Text Summarization App</h1>", unsafe_allow_html=True)
st_lottie(lottie_animation, height=250, key="header_anim")

# ------------------------🚀 LOAD SUMMARIZATION MODELS ------------------------
@st.cache_resource(show_spinner="🔄 Loading summarization model...")
def load_summarizer(model_name):
    return pipeline("summarization", model=model_name)

model_map = {
    "BART": "facebook/bart-large-cnn",
    "T5": "t5-small",
    "PEGASUS": "google/pegasus-cnn_dailymail"
}

model_choice = st.selectbox("🔍 Choose Summarization Model", list(model_map.keys()))
summarizer = load_summarizer(model_map[model_choice])

# ------------------------🛠️ USER INPUT & CONTROLS ------------------------
mode = st.radio("📤 Choose Output Mode:", ["Paragraph", "Bullet Points", "Custom"], horizontal=True)

col1, col2 = st.columns(2)

with col1:
    st.markdown("### ✍️ Enter Text or Upload File")

    uploaded_file = st.file_uploader("📂 Upload .txt file", type=["txt"])

    if uploaded_file is not None:
        user_input = uploaded_file.read().decode("utf-8")
        st.text_area("📃 Uploaded Text Preview", value=user_input, height=200)
    else:
        user_input = st.text_area("", height=300, placeholder="Paste your job description, article, or any long-form text here...")

    word_count = len(user_input.split())
    st.markdown(f"**🧮 {word_count} words**")

    # 🔧 Summary length control
    if mode != "Custom":
        length_label = st.radio("📏 Summary Length", ["Short", "Medium"], horizontal=True)
        min_len = 40
        max_len = 150 if length_label == "Short" else 300
    else:
        st.markdown("### 🎚️ Customize Summary Length")
        min_len = st.slider("Minimum Length", 20, 200, 50)
        max_len = st.slider("Maximum Length", 100, 500, 200)

    # ✨ Generate Summary
    if st.button("✨ Summarize", use_container_width=True):
        if not user_input.strip():
            st.warning("⚠️ Please enter text to summarize.")
        else:
            with st.spinner("🔄 Generating your summary... hang tight! ⏳"):
                try:
                    result = summarizer(user_input, max_length=max_len, min_length=min_len, do_sample=False)
                    summary = result[0]['summary_text']

                    if mode == "Bullet Points":
                        summary = "• " + summary.replace(". ", ".\n• ")

                    st.session_state["summary"] = summary

                except Exception as e:
                    st.error(f"⚠️ Error during summarization: {e}")

# ------------------------📄 SUMMARY OUTPUT & HISTORY ------------------------
with col2:
    st.markdown("### 📄 Summary Output")
    
    if "summary" in st.session_state:
        st.success(st.session_state["summary"])
        summary_words = len(st.session_state["summary"].split())
        st.markdown(f"📝 {summary_words} words")

        # 📥 Download Summary as TXT
        st.download_button(
            label="📥 Download This Summary (TXT)",
            data=st.session_state["summary"],
            file_name="summary.txt",
            mime="text/plain"
        )

        # 💾 Save to Summary History
        with st.expander("💾 Save & View Summary History"):
            if st.button("✅ Save this summary to history"):
                try:
                    with open("summary_history.txt", "a", encoding="utf-8") as f:
                        f.write("\n" + "="*50 + "\n")
                        f.write(f"🕒 Timestamp: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n")
                        f.write(f"🔹 Model Used: {model_choice}\n")
                        f.write(f"🔸 Mode: {mode}\n")
                        f.write(f"📝 Original Text:\n{user_input.strip()}\n\n")
                        f.write(f"✅ Summary:\n{st.session_state['summary'].strip()}\n")
                        f.write("="*50 + "\n\n")
                    st.success("📌 Summary saved to history!")
                except Exception as e:
                    st.error(f"❌ Failed to save summary: {e}")

            # 📚 View Summary History
            if st.checkbox("📚 Show Summary History"):
                try:
                    with open("summary_history.txt", "r", encoding="utf-8") as f:
                        history = f.read()
                    st.text_area("🗂️ Summary History", value=history, height=300)
                except FileNotFoundError:
                    st.info("ℹ️ No history found yet.")

            # 📊 Export as CSV
            if st.button("⬇️ Export History as CSV"):
                try:
                    summaries = []
                    with open("summary_history.txt", "r", encoding="utf-8") as f:
                        lines = f.read().split("="*50)
                        for entry in lines:
                            if "🕒 Timestamp" in entry:
                                lines_dict = {
                                    "Timestamp": entry.split("🕒 Timestamp: ")[1].split("\n")[0].strip(),
                                    "Model": entry.split("🔹 Model Used: ")[1].split("\n")[0].strip(),
                                    "Mode": entry.split("🔸 Mode: ")[1].split("\n")[0].strip(),
                                    "Original_Text": entry.split("📝 Original Text:\n")[1].split("\n\n")[0].strip(),
                                    "Summary": entry.split("✅ Summary:\n")[1].strip()
                                }
                                summaries.append(lines_dict)
                    df = pd.DataFrame(summaries)
                    csv = df.to_csv(index=False).encode('utf-8')
                    st.download_button("📄 Download CSV File", csv, "summary_history.csv", "text/csv")
                except Exception as e:
                    st.error(f"❌ Failed to export as CSV: {e}")
    else:
        st.info("ℹ️ Your summary will appear here once generated.")

# ------------------------🔚 FOOTER ------------------------
st.markdown("<hr>", unsafe_allow_html=True)
st.markdown(
    "<small>🚀 Built by <b>MULA VAMSHI🤍</b> using Hugging Face Transformers, Streamlit & Lottie</small>",
    unsafe_allow_html=True
)