# import os
# import re
# import fitz  # PyMuPDF
# import tempfile
# import base64
# from datetime import datetime
# from gtts import gTTS
# import streamlit as st
# from transformers import pipeline
# from groq import Groq

# # ✅ Hugging Face and GROQ secrets loaded via Hugging Face Spaces Secrets interface

# # ⚪ Access secrets securely from environment variables
# GROQ_API_KEY = os.getenv("GROQ_API_KEY")
# HF_TOKEN = os.getenv("HF_TOKEN")
# KAGGLE_USERNAME = os.getenv("KAGGLE_USERNAME")
# KAGGLE_KEY = os.getenv("KAGGLE_KEY")

# # ✅ Validate secrets
# if not all([GROQ_API_KEY, HF_TOKEN, KAGGLE_USERNAME, KAGGLE_KEY]):
#     st.error("❌ One or more required API keys are missing from the environment.")
#     st.stop()

# # ✅ Initialize Groq client
# client = Groq(api_key=GROQ_API_KEY)

# # ✅ Load phishing detection pipeline from Hugging Face
# phishing_pipe = pipeline(
#     "text-classification",
#     model="ealvaradob/bert-finetuned-phishing",
#     token=HF_TOKEN
# )

# # ✅ Language and role options
# language_choices = ["English", "Urdu", "French"]
# role_choices = ["Admin", "Procurement", "Logistics"]

# # ✅ Glossary terms
# GLOSSARY = {
#     "phishing": "Phishing is a scam where attackers trick you into revealing personal information.",
#     "malware": "Malicious software designed to harm or exploit systems.",
#     "spam": "Unwanted or unsolicited messages.",
#     "tone": "The emotional character of the message."
# }

# # ✅ Translations (demo dictionary-based)
# TRANSLATIONS = {
#     "Phishing": {"Urdu": "فشنگ", "French": "Hameçonnage"},
#     "Spam": {"Urdu": "سپیم", "French": "Courrier indésirable"},
#     "Malware": {"Urdu": "میلویئر", "French": "Logiciel malveillant"},
#     "Safe": {"Urdu": "محفوظ", "French": "Sûr"}
# }

# # ✅ In-memory history and audio
# if "history" not in st.session_state:
#     st.session_state.history = []
# if "audio_summary" not in st.session_state:
#     st.session_state.audio_summary = ""
# if "report_sent" not in st.session_state:
#     st.session_state.report_sent = False

# # =======================
# # Streamlit UI
# # =======================
# st.set_page_config(page_title="ZeroPhish Gate", layout="wide")

# st.title("🛡️ ZeroPhish Gate")
# st.markdown("AI-powered phishing message detection and explanation.")

# # Input fields
# col1, col2 = st.columns([3, 1])
# with col1:
#     text_input = st.text_area("✉️ Paste Suspicious Message", height=200)
#     uploaded_file = st.file_uploader("📄 Upload PDF/TXT (optional)", type=["pdf", "txt"])

# with col2:
#     language = st.selectbox("🌐 Preferred Language", language_choices)
#     role = st.selectbox("🧑‍💼 Your Role", role_choices)

# analyze_btn = st.button("🔍 Analyze with AI")
# clear_btn = st.button("🗑️ Clear History")

# # =======================
# # Function Definitions
# # =======================
# def extract_text_from_file(file):
#     if file is None:
#         return ""
#     ext = file.name.split(".")[-1].lower()
#     if ext == "pdf":
#         doc = fitz.open(stream=file.read(), filetype="pdf")
#         return "\n".join(page.get_text() for page in doc)
#     elif ext == "txt":
#         return file.read().decode("utf-8")
#     return ""

# def analyze_with_huggingface(text):
#     try:
#         result = phishing_pipe(text)
#         label = result[0]['label']
#         confidence = round(result[0]['score'] * 100, 2)
#         threat_type = {
#             "PHISHING": "Phishing",
#             "SPAM": "Spam",
#             "MALWARE": "Malware",
#             "LEGITIMATE": "Safe"
#         }.get(label.upper(), "Unknown")
#         return label, confidence, threat_type
#     except Exception as e:
#         return "Error", 0, f"Error: {e}"

# def semantic_analysis(text):
#     response = client.chat.completions.create(
#         model="llama3-8b-8192",
#         messages=[
#             {"role": "system", "content": "You are a cybersecurity assistant."},
#             {"role": "user", "content": f"Explain this suspicious message for a {role} in {language} without ending in questions:\n{text}"}
#         ]
#     )
#     raw = response.choices[0].message.content
#     clean = re.sub(r"Is there anything else you'd like.*", "", raw, flags=re.I).strip()
#     return clean

# def translate_label(threat_type):
#     return TRANSLATIONS.get(threat_type, {}).get(language, threat_type)

# def text_to_speech(text):
#     try:
#         tts = gTTS(text=text, lang='en')
#         with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as fp:
#             tts.save(fp.name)
#             return fp.name
#     except Exception as e:
#         st.error(f"❌ Audio generation error: {e}")
#         return None

# def create_report(label, score, threat_type, explanation, text):
#     ts = datetime.now().strftime("%Y%m%d_%H%M%S")
#     filename = f"Zerophish_Report_{ts}.txt"
#     report = f"""
# 🔍 AI Threat Detection Report

# Input Message:
# {text}

# Prediction: {label}
# Threat Type: {threat_type}
# Confidence: {score}%

# ---

# 🧠 Explanation:
# {explanation}
# """
#     with open(filename, "w") as f:
#         f.write(report)
#     return filename

# def render_history():
#     with st.expander("🕓 View Analysis History", expanded=True):
#         for i, record in enumerate(reversed(st.session_state.history)):
#             with st.container():
#                 st.markdown(f"**🔢 Entry #{len(st.session_state.history) - i}**")
#                 st.markdown(f"**📝 Input:** {record['input'][:100]}...")
#                 st.markdown(f"**🔐 Type:** {record['threat']} | **📊 Confidence:** {record['score']}%")
#                 st.markdown(f"**📖 Summary:** {record['summary'][:200]}...")
#                 st.markdown("---")

# # =======================
# # Run Analysis
# # =======================
# if clear_btn:
#     st.session_state.history.clear()
#     st.session_state.audio_summary = ""
#     st.session_state.report_sent = False
#     st.success("✅ History cleared!")

# if analyze_btn:
#     st.session_state.report_sent = False
#     combined_text = text_input
#     if uploaded_file:
#         extracted = extract_text_from_file(uploaded_file)
#         combined_text += "\n" + extracted

#     if not combined_text.strip():
#         st.warning("⚠️ Please enter some text or upload a file to analyze.")
#     else:
#         label, score, threat_type = analyze_with_huggingface(combined_text)
#         translated_threat = translate_label(threat_type)

#         st.subheader("🔍 AI Threat Detection Result")
#         st.markdown(f"**Prediction:** {label}")
#         st.markdown(f"**Threat Type:** {threat_type} ({translated_threat})")
#         st.markdown(f"**Confidence:** {score}%")

#         summary = ""
#         if threat_type.lower() != "safe":
#             with st.expander("🧠 Semantic Reanalysis by LLaMA"):
#                 summary = semantic_analysis(combined_text)
#                 st.write(summary)

#                 st.session_state.audio_summary = summary  # Save for audio playback

#                 audio_path = text_to_speech(summary)
#                 if audio_path:
#                     with open(audio_path, "rb") as f:
#                         st.markdown("### 🔊 Audio Explanation")
#                         st.audio(f.read(), format="audio/mp3")
#                     os.remove(audio_path)

#         # Save history
#         st.session_state.history.append({
#             "input": combined_text,
#             "threat": threat_type,
#             "score": score,
#             "summary": summary
#         })

#         # Generate and offer download link
#         if summary:
#             report_path = create_report(label, score, threat_type, summary, combined_text)
#             with open(report_path, "rb") as f:
#                 b64 = base64.b64encode(f.read()).decode()
#                 href = f'<a href="data:file/txt;base64,{b64}" download="{report_path}">📄 Download Full Report</a>'
#                 st.markdown(href, unsafe_allow_html=True)

#         with st.expander("📜 Glossary Help"):
#             for term, definition in GLOSSARY.items():
#                 st.markdown(f"**{term.capitalize()}**: {definition}")

# # ✅ Report to IT section - outside the expander and stable
# st.markdown("---")
# report_it = st.button("📤 Report to IT", key="report_it_btn")
# if report_it:
#     st.session_state.report_sent = True

# if st.session_state.report_sent:
#     st.success("📨 Report sent to IT successfully.")

# render_history()



# app.py

import os
import re
import fitz  # PyMuPDF
import tempfile
import base64
from datetime import datetime
from gtts import gTTS
import streamlit as st
from transformers import pipeline
from groq import Groq

# ✅ Hugging Face and GROQ secrets loaded via Hugging Face Spaces Secrets interface

# ⚪ Access secrets securely from environment variables
GROQ_API_KEY = os.getenv("GROQ_API_KEY")
HF_TOKEN = os.getenv("HF_TOKEN")
KAGGLE_USERNAME = os.getenv("KAGGLE_USERNAME")
KAGGLE_KEY = os.getenv("KAGGLE_KEY")

# ✅ Validate secrets
if not all([GROQ_API_KEY, HF_TOKEN, KAGGLE_USERNAME, KAGGLE_KEY]):
    st.error("❌ One or more required API keys are missing from the environment.")
    st.stop()

# ✅ Initialize Groq client
client = Groq(api_key=GROQ_API_KEY)

# ✅ Load phishing detection pipeline from Hugging Face
phishing_pipe = pipeline(
    "text-classification",
    model="ealvaradob/bert-finetuned-phishing",
    token=HF_TOKEN
)

# ✅ Language and role options
language_choices = ["English", "Urdu", "French"]
role_choices = ["Admin", "Procurement", "Logistics"]

# ✅ Glossary terms
GLOSSARY = {
    "phishing": "Phishing is a scam where attackers trick you into revealing personal information.",
    "malware": "Malicious software designed to harm or exploit systems.",
    "spam": "Unwanted or unsolicited messages.",
    "tone": "The emotional character of the message."
}

# ✅ Translations (demo dictionary-based)
TRANSLATIONS = {
    "Phishing": {"Urdu": "فشنگ", "French": "Hameçonnage"},
    "Spam": {"Urdu": "سپیم", "French": "Courrier indésirable"},
    "Malware": {"Urdu": "میلویئر", "French": "Logiciel malveillant"},
    "Safe": {"Urdu": "محفوظ", "French": "Sûr"}
}

# ✅ In-memory history and audio
if "history" not in st.session_state:
    st.session_state.history = []
if "audio_summary" not in st.session_state:
    st.session_state.audio_summary = ""
if "report_sent" not in st.session_state:
    st.session_state.report_sent = False
if "chat_active" not in st.session_state:
    st.session_state.chat_active = False
if "text_input" not in st.session_state:
    st.session_state.text_input = ""
if "uploaded_file" not in st.session_state:
    st.session_state.uploaded_file = None

# =======================
# Streamlit UI
# =======================
st.set_page_config(page_title="ZeroPhish Gate", layout="wide")

st.title("🛡️ ZeroPhish Gate")
st.markdown("AI-powered phishing message detection and explanation.")

# ✅ New Chat button
if st.button("🆕 New Chat"):
    st.session_state.chat_active = False
    st.session_state.audio_summary = ""
    st.session_state.report_sent = False
    st.session_state.text_input = ""
    st.session_state.uploaded_file = None
    

# Input fields
col1, col2 = st.columns([3, 1])
with col1:
    st.session_state.text_input = st.text_area("✉️ Paste Suspicious Message", value=st.session_state.text_input, height=200)
    st.session_state.uploaded_file = st.file_uploader("📄 Upload PDF/TXT (optional)", type=["pdf", "txt"])

with col2:
    language = st.selectbox("🌐 Preferred Language", language_choices)
    role = st.selectbox("🧑‍💼 Your Role", role_choices)

analyze_btn = st.button("🔍 Analyze with AI")
clear_btn = st.button("🗑️ Clear History")

# =======================
# Function Definitions
# =======================
def extract_text_from_file(file):
    if file is None:
        return ""
    ext = file.name.split(".")[-1].lower()
    if ext == "pdf":
        doc = fitz.open(stream=file.read(), filetype="pdf")
        return "\n".join(page.get_text() for page in doc)
    elif ext == "txt":
        return file.read().decode("utf-8")
    return ""

def analyze_with_huggingface(text):
    try:
        result = phishing_pipe(text)
        label = result[0]['label']
        confidence = round(result[0]['score'] * 100, 2)
        threat_type = {
            "PHISHING": "Phishing",
            "SPAM": "Spam",
            "MALWARE": "Malware",
            "LEGITIMATE": "Safe"
        }.get(label.upper(), "Unknown")
        return label, confidence, threat_type
    except Exception as e:
        return "Error", 0, f"Error: {e}"

def semantic_analysis(text):
    response = client.chat.completions.create(
        model="llama3-8b-8192",
        messages=[
            {"role": "system", "content": "You are a cybersecurity assistant."},
            {"role": "user", "content": f"Explain this suspicious message for a {role} in {language} without ending in questions:\n{text}"}
        ]
    )
    raw = response.choices[0].message.content
    clean = re.sub(r"Is there anything else you'd like.*", "", raw, flags=re.I).strip()
    return clean

def translate_label(threat_type):
    return TRANSLATIONS.get(threat_type, {}).get(language, threat_type)

def text_to_speech(text):
    try:
        tts = gTTS(text=text, lang='en')
        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as fp:
            tts.save(fp.name)
            return fp.name
    except Exception as e:
        st.error(f"❌ Audio generation error: {e}")
        return None

def create_report(label, score, threat_type, explanation, text):
    ts = datetime.now().strftime("%Y%m%d_%H%M%S")
    filename = f"Zerophish_Report_{ts}.txt"
    report = f"""
🔍 AI Threat Detection Report

Input Message:
{text}

Prediction: {label}
Threat Type: {threat_type}
Confidence: {score}%

---

🧠 Explanation:
{explanation}
"""
    with open(filename, "w") as f:
        f.write(report)
    return filename

def render_history():
    with st.expander("🕓 View Analysis History", expanded=True):
        for i, record in enumerate(reversed(st.session_state.history)):
            with st.container():
                st.markdown(f"**🔢 Entry #{len(st.session_state.history) - i}**")
                st.markdown(f"**📝 Input:** {record['input'][:100]}...")
                st.markdown(f"**🔐 Type:** {record['threat']} | **📊 Confidence:** {record['score']}%")
                st.markdown(f"**📖 Summary:** {record['summary'][:200]}...")
                st.markdown("---")

# =======================
# Run Analysis
# =======================
if clear_btn:
    st.session_state.history.clear()
    st.session_state.audio_summary = ""
    st.session_state.report_sent = False
    st.success("✅ History cleared!")

if analyze_btn:
    st.session_state.report_sent = False
    st.session_state.chat_active = True
    combined_text = st.session_state.text_input
    if st.session_state.uploaded_file:
        extracted = extract_text_from_file(st.session_state.uploaded_file)
        combined_text += "\n" + extracted

    if not combined_text.strip():
        st.warning("⚠️ Please enter some text or upload a file to analyze.")
    else:
        label, score, threat_type = analyze_with_huggingface(combined_text)
        translated_threat = translate_label(threat_type)

        st.subheader("🔍 AI Threat Detection Result")
        st.markdown(f"**Prediction:** {label}")
        st.markdown(f"**Threat Type:** {threat_type} ({translated_threat})")
        st.markdown(f"**Confidence:** {score}%")

        summary = ""
        if threat_type.lower() != "safe":
            with st.expander("🧠 Semantic Reanalysis by LLaMA"):
                summary = semantic_analysis(combined_text)
                st.write(summary)

                st.session_state.audio_summary = summary  # Save for audio playback

                audio_path = text_to_speech(summary)
                if audio_path:
                    with open(audio_path, "rb") as f:
                        st.markdown("### 🔊 Audio Explanation")
                        st.audio(f.read(), format="audio/mp3")
                    os.remove(audio_path)

        # Save history
        st.session_state.history.append({
            "input": combined_text,
            "threat": threat_type,
            "score": score,
            "summary": summary
        })

        # Generate and offer download link
        if summary:
            report_path = create_report(label, score, threat_type, summary, combined_text)
            with open(report_path, "rb") as f:
                b64 = base64.b64encode(f.read()).decode()
                href = f'<a href="data:file/txt;base64,{b64}" download="{report_path}">📄 Download Full Report</a>'
                st.markdown(href, unsafe_allow_html=True)

        with st.expander("📜 Glossary Help"):
            for term, definition in GLOSSARY.items():
                st.markdown(f"**{term.capitalize()}**: {definition}")

# ✅ Report to IT section - only visible after analysis
if st.session_state.chat_active:
    st.markdown("---")
    report_it = st.button("📤 Report to IT", key="report_it_btn")
    if report_it:
        st.session_state.report_sent = True

    if st.session_state.report_sent:
        st.success("📨 Report sent to IT successfully.")

render_history()