app.py

import os
import time
import re
import requests
import phonenumbers
import pandas as pd
import urllib.parse
from bs4 import BeautifulSoup

import torch
from transformers import (
    AutoTokenizer,
    AutoModelForTokenClassification,
    AutoModelForSeq2SeqLM,
    pipeline
)

import gradio as gr
from concurrent.futures import ThreadPoolExecutor, as_completed
from email.message import EmailMessage
import smtplib
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText

# ============================
# CONFIG 
# ============================
API_KEY = os.environ.get("GOOGLE_API_KEY", "YOUR_GOOGLE_API_KEY")
CX      = os.environ.get("GOOGLE_CSE_ID", "YOUR_CSE_ID")
DEFAULT_COUNTRY = "Ghana"

RESULTS_PER_QUERY = int(os.environ.get("RESULTS_PER_QUERY", 4))
MAX_SCRAPE_WORKERS = int(os.environ.get("MAX_SCRAPE_WORKERS", 6))

ALLY_AI_NAME = os.environ.get("ALLY_AI_NAME", "Ally AI")
ALLY_AI_LOGO_URL_DEFAULT = os.environ.get("ALLY_AI_LOGO_URL",
                                         "https://imgur.com/a/lVxnQke")

COUNTRY_TLD_MAP   = {"Ghana":"gh","Nigeria":"ng","Kenya":"ke","South Africa":"za","USA":"us","United Kingdom":"uk"}
COUNTRY_REGION_MAP= {"Ghana":"GH","Nigeria":"NG","Kenya":"KE","South Africa":"ZA","USA":"US","United Kingdom":"GB"}

HEADERS = {"User-Agent":"Mozilla/5.0 (X11; Linux x86_64)"}
EMAIL_REGEX = re.compile(r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}")

# ============================
# MODELS (lightweight & CPU-friendly)
# ============================
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
print("Device set to use", DEVICE)

# NER model (people/orgs/locs)
ner_model_id = "dslim/bert-base-NER"
ner_tokenizer = AutoTokenizer.from_pretrained(ner_model_id)
ner_model = AutoModelForTokenClassification.from_pretrained(ner_model_id)
ner_pipe = pipeline("ner", model=ner_model, tokenizer=ner_tokenizer, aggregation_strategy="simple",
                    device=0 if DEVICE=="cuda" else -1)

# Summarizer / anonymizer
text_model_id = "google/flan-t5-large"
text_tokenizer = AutoTokenizer.from_pretrained(text_model_id)
text_model = AutoModelForSeq2SeqLM.from_pretrained(text_model_id).to(DEVICE)

# ============================
# TAXONOMY & HELPERS
# ============================
PROFESSION_KEYWORDS = ["lawyer","therapist","doctor","counselor","social worker",
                       "advocate","psychologist","psychiatrist","consultant","nurse","hotline","gbv"]

PROBLEM_PROFESSION_MAP = {
    "rape": ["lawyer","therapist","counselor","doctor"],
    "sexual assault": ["lawyer","therapist","counselor"],
    "domestic violence": ["lawyer","social worker","therapist"],
    "abuse": ["counselor","social worker","therapist","lawyer"],
    "trauma": ["therapist","psychologist","psychiatrist"],
    "depression": ["therapist","psychologist","doctor"],
    "violence": ["lawyer","counselor","social worker"],
}

def get_region_for_country(country: str) -> str:
    return COUNTRY_REGION_MAP.get(country, "GH")

def get_tld_for_country(country: str) -> str:
    return COUNTRY_TLD_MAP.get(country, "")

def build_country_biased_query(core: str, country: str) -> str:
    tld = get_tld_for_country(country)
    suffix = f" in {country}"
    if tld:
        return f"{core}{suffix} site:.{tld} OR {country}"
    return f"{core}{suffix}"

def dedup_by_url(items):
    seen, out = set(), []
    for it in items:
        u = it.get("link") or it.get("url")
        if u and u not in seen:
            seen.add(u)
            out.append(it)
    return out

# ============================
# SEARCH & SCRAPING
# ============================
def google_search(query, num_results=5):
    if not API_KEY or not CX or "YOUR_GOOGLE_API_KEY" in API_KEY or "YOUR_CSE_ID" in CX:
        raise RuntimeError("Google API key and CSE ID must be set as environment variables.")
    url = "https://www.googleapis.com/customsearch/v1"
    params = {"q":query, "key":API_KEY, "cx":CX, "num":num_results}
    r = requests.get(url, params=params, timeout=20)
    r.raise_for_status()
    items = r.json().get("items", []) or []
    return [{"title":i.get("title",""), "link":i.get("link",""), "snippet":i.get("snippet","")} for i in items]

def extract_phones(text, region="GH"):
    phones = []
    for match in phonenumbers.PhoneNumberMatcher(text, region):
        try:
            phones.append(phonenumbers.format_number(match.number, phonenumbers.PhoneNumberFormat.INTERNATIONAL))
        except Exception:
            pass
    return list(set(phones))

def _domain_from_url(url):
    try:
        return urllib.parse.urlparse(url).netloc
    except Exception:
        return url

def scrape_contacts(url, region="GH"):
    """
    Extended scraping: returns emails, phones, and a guessed 'org' name
    extracted from meta tags, headings, or via NER on page text.
    """
    try:
        res = requests.get(url, headers=HEADERS, timeout=12)
        if not res.ok or not res.text:
            return {"emails": [], "phones": [], "org": None}
        soup = BeautifulSoup(res.text, "html.parser")

        # raw text for phone/email/NER
        text = soup.get_text(separator=" ")
        text = " ".join(text.split())[:300000]

        # emails & phones
        emails = list(set(EMAIL_REGEX.findall(text)))
        phones = extract_phones(text, region)

        # try meta og:site_name or twitter site meta
        org_name = None
        meta_og = soup.find("meta", property="og:site_name") or soup.find("meta", attrs={"name":"og:site_name"})
        if meta_og and meta_og.get("content"):
            org_name = meta_og.get("content").strip()

        # fallback to <title> or first <h1>
        if not org_name:
            title_tag = soup.find("title")
            if title_tag and title_tag.get_text(strip=True):
                org_name = title_tag.get_text(strip=True)
        if not org_name:
            h1 = soup.find("h1")
            if h1 and h1.get_text(strip=True):
                org_name = h1.get_text(strip=True)

        # run NER to find ORG mentions in the page text and prefer that if short and clean
        try:
            people, orgs, locs = extract_entities(text)
            if orgs:
                # choose first short/clean org
                for o in orgs:
                    if len(o) > 1 and len(o) < 80:
                        org_name = o
                        break
        except Exception:
            pass

        # final fallback: domain
        if not org_name:
            org_name = _domain_from_url(url)

        return {"emails": emails, "phones": phones, "org": org_name}
    except Exception as e:
        print(f"[scrape error] {url} -> {e}")
        return {"emails": [], "phones": [], "org": _domain_from_url(url)}

# ============================
# NER + STORY → PROFESSIONS
# ============================
def extract_entities(text):
    if not text:
        return [],[],[]
    try:
        ner_results = ner_pipe(text)
    except Exception as e:
        print("[ner error]", e)
        return [],[],[]
    people = [e["word"] for e in ner_results if e.get("entity_group") == "PER"]
    orgs   = [e["word"] for e in ner_results if e.get("entity_group") == "ORG"]
    locs   = [e["word"] for e in ner_results if e.get("entity_group") == "LOC"]
    return list(set(people)), list(set(orgs)), list(set(locs))

def professions_from_story(story: str):
    s = (story or "").lower()
    found = set([p for p in PROFESSION_KEYWORDS if p in s])
    for prob, profs in PROBLEM_PROFESSION_MAP.items():
        if prob in s:
            found.update(profs)
    if not found:
        return ["gbv","counselor"]
    order = ["lawyer","therapist","counselor","social worker","psychologist","psychiatrist","doctor","advocate","nurse","hotline","gbv"]
    return [p for p in order if p in found]

def build_queries(story: str, country: str):
    profs = professions_from_story(story)
    cores = []
    for p in profs:
        if p == "gbv":
            cores += ["GBV support organizations", "gender based violence help"]
        else:
            cores += [f"{p} for GBV", f"{p} for sexual assault", f"{p} near me {p} {country}"]
    unique_cores, seen = [], set()
    for c in cores:
        if c not in seen:
            unique_cores.append(c); seen.add(c)
    return [build_country_biased_query(core, country) for core in unique_cores], profs

# ============================
# TEXT GEN: anonymize + result summary
# ============================
def anonymize_story(story: str, max_sentences: int = 2):
    if not story or not story.strip():
        return ""
    prompt = (
        "Anonymize and shorten the following personal story for contacting professionals. "
        "Remove names, exact ages, dates, locations and any identifying details. "
        f"Keep only the essential problem and the type of help requested. Output <= {max_sentences} sentences.\n\n"
        f"Story: {story}\n\nSummary:"
    )
    inputs = text_tokenizer(prompt, return_tensors="pt").to(DEVICE)
    with torch.no_grad():
        outputs = text_model.generate(**inputs, max_new_tokens=120, temperature=0.2)
    return text_tokenizer.decode(outputs[0], skip_special_tokens=True).strip()

def generate_summary(query, people, orgs, locs):
    prompt = (
        "Write a short, empathetic summary of these search results for a person seeking GBV help.\n"
        f"Query: {query}\nPeople: {', '.join(people) or '—'}\nOrgs: {', '.join(orgs) or '—'}\nLocations: {', '.join(locs) or '—'}\n\n"
        "Explain how the organizations/professionals can help in 3-4 sentences."
    )
    inputs = text_tokenizer(prompt, return_tensors="pt").to(DEVICE)
    with torch.no_grad():
        outputs = text_model.generate(**inputs, max_new_tokens=150, temperature=0.7)
    return text_tokenizer.decode(outputs[0], skip_special_tokens=True).strip()

# ============================
# MAIN PIPELINE
# ============================
def find_professionals_from_story(story, country=DEFAULT_COUNTRY, results_per_query=RESULTS_PER_QUERY):
    region = get_region_for_country(country)
    queries, profs = build_queries(story, country)

    # Search
    search_results = []
    for q in queries:
        try:
            items = google_search(q, num_results=results_per_query)
            for it in items:
                it["query"] = q
            search_results.extend(items)
        except Exception as e:
            print("[search error]", q, e)

    search_results = dedup_by_url(search_results)
    if not search_results:
        return {"summary":"No results found. Try a different country or wording.",
                "professionals":[], "queries_used":queries}

    # NER on titles/snippets for context
    all_people, all_orgs, all_locs = [], [], []
    for r in search_results:
        ctx = f"{r.get('title','')}. {r.get('snippet','')}"
        p,o,l = extract_entities(ctx)
        all_people += p; all_orgs += o; all_locs += l

    # Scrape contacts concurrently, extracting org names from page content
    professionals = []
    with ThreadPoolExecutor(max_workers=MAX_SCRAPE_WORKERS) as ex:
        futures = {ex.submit(scrape_contacts, r["link"], region): r for r in search_results}
        for fut in as_completed(futures):
            r = futures[fut]
            contacts = {"emails": [], "phones": [], "org": None}
            try:
                contacts = fut.result()
            except Exception as e:
                print("[scrape future error]", r["link"], e)

            # choose a single email/phone if available
            email = contacts["emails"][0] if contacts.get("emails") else None
            phone = contacts["phones"][0] if contacts.get("phones") else None
            org_name = contacts.get("org") or ""
            # attempt to choose profession tag from the query used
            prof_tag = None
            qlower = (r.get("query") or "").lower()
            for p in professions_from_story(story):
                if p in qlower:
                    prof_tag = p
                    break
            prof_tag = prof_tag or (professions_from_story(story)[0] if professions_from_story(story) else "gbv")

            professionals.append({
                "org": org_name,
                "url": r.get("link",""),
                "email": email if email else "Not found",
                "phone": phone if phone else "Not found",
                "profession": prof_tag,
                "source_query": r.get("query","")
            })

    summary = generate_summary("; ".join(queries[:3]) + (" ..." if len(queries)>3 else ""),
                               list(set(all_people)), list(set(all_orgs)), list(set(all_locs)))

    # Sort by availability of email/phone
    professionals.sort(key=lambda it: (0 if it["email"]!="Not found" else 1,
                                       0 if it["phone"]!="Not found" else 1))
    return {"summary": summary, "professionals": professionals, "queries_used": queries}

# ============================
# DRAFT (mailto + .eml)
# ============================
def build_mailto_and_eml(to_addr, subject, body, default_from="noreply@ally.ai"):
    """
    Creates a proper .eml file and returns (mailto_link, absolute_eml_path).
    Ensures the file is actually written and non-empty. If .eml fails, writes a .txt fallback.
    """
    # sanitize inputs
    to_addr = (to_addr or "").strip()
    subject = subject or ""
    body = body or ""

    # Create EmailMessage object
    msg = EmailMessage()
    msg["From"] = default_from
    msg["To"] = to_addr
    msg["Subject"] = subject
    msg.set_content(body)

    # ensure output dir exists and use absolute path (more robust for HF Spaces / Colab)
    out_dir = os.path.abspath("tmp")
    os.makedirs(out_dir, exist_ok=True)

    fname = os.path.join(out_dir, f"email_draft_{int(time.time())}.eml")

    try:
        # write bytes
        with open(fname, "wb") as f:
            f.write(msg.as_bytes())

        # verify file exists and is non-empty
        if os.path.exists(fname) and os.path.getsize(fname) > 0:
            mailto = f"mailto:{urllib.parse.quote(to_addr)}?subject={urllib.parse.quote(subject)}&body={urllib.parse.quote(body)}"
            return mailto, fname

        # fallback: create a plain text copy (useful for debugging)
        fallback = fname + ".txt"
        with open(fallback, "w", encoding="utf-8") as f:
            f.write(f"To: {to_addr}\nSubject: {subject}\n\n{body}")
        mailto = f"mailto:{urllib.parse.quote(to_addr)}?subject={urllib.parse.quote(subject)}&body={urllib.parse.quote(body)}"
        return mailto, fallback

    except Exception as e:
        # If writing .eml fails entirely, create a .txt fallback and return that path
        fallback = os.path.join(out_dir, f"email_draft_{int(time.time())}.txt")
        try:
            with open(fallback, "w", encoding="utf-8") as f:
                f.write(f"Error writing .eml: {e}\n\nTo: {to_addr}\nSubject: {subject}\n\n{body}")
            mailto = f"mailto:{urllib.parse.quote(to_addr)}?subject={urllib.parse.quote(subject)}&body={urllib.parse.quote(body)}"
            return mailto, fallback
        except Exception as e2:
            # ultimate fallback: return no file and an informative mailto
            mailto = f"mailto:{urllib.parse.quote(to_addr)}?subject={urllib.parse.quote(subject)}&body={urllib.parse.quote(body)}"
            return mailto, None

# ============================
# SENDER (SMTP) — Ally AI branding
# ============================
def send_ally_ai_email(to_email, subject, body, user_email,
                       sender_email, sender_password,
                       ai_name=ALLY_AI_NAME,
                       logo_url=ALLY_AI_LOGO_URL_DEFAULT):
    if not to_email or to_email == "Not found":
        return "❌ No recipient email found — choose a contact or provide a manual email."

    msg = MIMEMultipart("alternative")
    msg["Subject"] = subject or "Request for support"
    msg["From"] = f"{ai_name} <{sender_email}>"
    msg["To"] = to_email

    html_content = f"""
    <html>
    <body style="font-family: Arial, sans-serif; color: #333;">
        <div style="padding: 20px; border: 1px solid #eee; border-radius: 10px; max-width: 640px; margin: auto;">
            <div style="text-align: center;">
                <img src="{logo_url}" alt="{ai_name} Logo" width="120" style="margin-bottom: 20px;" />
            </div>
            <p>{body}</p>
            <p style="margin-top:20px;">
                <b>Contact the survivor back at:</b> <a href="mailto:{user_email}">{user_email}</a>
            </p>
            <hr style="border:none;border-top:1px solid #eee;margin:24px 0;">
            <p style="font-size: 12px; color: gray; text-align: center;">
                This message was prepared with the help of <b>{ai_name}</b> — connecting survivors with help safely.
            </p>
        </div>
    </body>
    </html>
    """
    msg.attach(MIMEText(html_content, "html"))

    try:
        server = smtplib.SMTP("smtp.gmail.com", 587)
        server.starttls()
        server.login(sender_email, sender_password)
        server.sendmail(sender_email, [to_email], msg.as_string())
        server.quit()
        return f"✅ Email sent successfully to {to_email}"
    except Exception as e:
        return f"❌ Failed to send email: {str(e)}"

# ============================
# GRADIO UI
# ============================
def run_search(story, country):
    try:
        out = find_professionals_from_story(story, country=country, results_per_query=RESULTS_PER_QUERY)
    except Exception as e:
        err_msg = f"Search failed: {e}"
        placeholder = ["0 — No results (search failed)"]
        return err_msg, [], placeholder, ""

    pros = out.get("professionals", []) or []

    # build table records with org instead of article title
    try:
        records = pd.DataFrame(pros).to_dict(orient="records") if pros else []
    except Exception:
        records = []

    # build dropdown options as list of strings
    options = []
    for i, r in enumerate(pros):
        label_contact = r.get("email") if r.get("email") and r.get("email") != "Not found" else (r.get("phone", "No contact"))
        org_label = r.get("org") or r.get("url") or "(no org)"
        label = f"{i} — {org_label} ({label_contact})"
        options.append(label)

    if not options:
        options = ["0 — No results (try a different country/query)"]

    # anonymize safely
    try:
        anon = anonymize_story(story) or "I am seeking confidential support regarding gender-based violence."
    except Exception as e:
        print("[anonymize error]", e)
        anon = "I am seeking confidential support regarding gender-based violence."

    summary = out.get("summary", "No results found.")
    return summary, records, options, anon

def make_body(anon_text, full_story, use_anon, user_email):
    core = (anon_text or "").strip() if use_anon else (full_story or "").strip()
    lines = [
        core,
        "",
        f"Reply contact: {user_email}",
        "",
        "Thank you."
    ]
    return "\n".join([l for l in lines if l is not None])

def preview_contact(dropdown_value, df_json, subject, message_text, manual_email):
    if not dropdown_value:
        return "No contact selected.", ""
    try:
        idx = int(str(dropdown_value).split(" — ")[0])
        rows = pd.DataFrame(df_json)
        contact = rows.iloc[idx].to_dict()

        # choose recipient from manual_email if provided & valid, else scraped email
        recipient = None
        if manual_email and EMAIL_REGEX.search(manual_email):
            recipient = manual_email.strip()
        else:
            recipient = contact.get("email") if contact.get("email") and contact.get("email")!="Not found" else "[no email]"

        org_display = contact.get('org') or contact.get('url') or "(no org)"
        html = f"""
        <h3>Preview</h3>
        <b>To:</b> {recipient}<br/>
        <b>Organization:</b> <a href="{contact.get('url')}" target="_blank" rel="noopener">{org_display}</a><br/>
        <b>Profession tag:</b> {contact.get('profession')}<br/>
        <b>Subject:</b> {subject}<br/>
        <hr/>
        <pre style="white-space:pre-wrap;">{message_text}</pre>
        """
        text = f"To: {recipient}\nOrganization: {org_display}\nSubject: {subject}\n\n{message_text[:600]}{'...' if len(message_text)>600 else ''}"
        return text, html
    except Exception as e:
        return f"Preview error: {e}", ""

def confirm_action(mode, dropdown_value, df_json, subject, message_text,
                   user_email, sender_email, sender_password, logo_url, manual_email):
    """
    mode: "Draft only" or "Send via SMTP (Gmail)"
    manual_email: optional override to use when scraped email not found
    """
    if not dropdown_value:
        return "❌ No contact selected.", "", None

    # locate contact
    try:
        idx = int(str(dropdown_value).split(" — ")[0])
        rows = pd.DataFrame(df_json)
        contact = rows.iloc[idx].to_dict()
    except Exception as e:
        return f"❌ Selection error: {e}", "", None

    scraped_recipient = contact.get("email")
    # use manual if valid
    recipient = None
    if manual_email and EMAIL_REGEX.search(manual_email):
        recipient = manual_email.strip()
    elif scraped_recipient and scraped_recipient != "Not found":
        recipient = scraped_recipient

    if mode.startswith("Send"):
        # Validate required fields
        if not recipient:
            return "❌ No recipient email found — either pick a contact with an email or provide a manual email.", "", None
        if not user_email or "@" not in user_email:
            return "❌ Please enter your email (so the organisation can contact you).", "", None
        if not sender_email or not sender_password:
            return "❌ Sender email and app password are required for SMTP sending.", "", None

        status = send_ally_ai_email(
            to_email=recipient,
            subject=subject,
            body=message_text,
            user_email=user_email,
            sender_email=sender_email,
            sender_password=sender_password,
            ai_name=ALLY_AI_NAME,
            logo_url=logo_url or ALLY_AI_LOGO_URL_DEFAULT
        )
        _, eml_path = build_mailto_and_eml(recipient, subject, message_text, default_from=sender_email)
        file_out = eml_path if eml_path and os.path.exists(eml_path) else None
        return status, "", file_out
    else:
        # Draft-only path (mailto + .eml)
        recip_for_draft = recipient or ""
        mailto, eml_path = build_mailto_and_eml(recip_for_draft, subject, message_text, default_from="noreply@ally.ai")
        if eml_path and os.path.exists(eml_path) and os.path.getsize(eml_path) > 0:
            html_link = f'<a href="{mailto}" target="_blank" rel="noopener">Open draft in email client</a>'
            file_out = eml_path
            return "✅ Draft created (no email sent).", html_link, file_out
        elif eml_path and os.path.exists(eml_path):
            # file exists but is empty
            return "⚠️ Draft file created but it's empty. Check the message body or try manual email.", "", eml_path
        else:
            return "❌ Failed to create draft file.", "", None


with gr.Blocks() as demo:
    gr.Markdown("## Ally AI — GBV Help Finder & Email Assistant\n"
                "This tool searches local professionals/organizations lets you select a contact or enter an email manually, and creates an email draft or sends a branded email via Gmail"
                "**Privacy tip:** Prefer anonymized summaries unless you’re comfortable sharing details.")

    with gr.Row():
        story_in   = gr.Textbox(label="Your story (free text)", lines=6, placeholder="Describe your situation and the help you want...")
        country_in = gr.Textbox(value=DEFAULT_COUNTRY, label="Country (to bias search)")

    search_btn = gr.Button("Search for professionals")
    summary_out = gr.Textbox(label="Search summary (AI)", interactive=False)
    # updated headers: use org (organization name) instead of article title
    results_table = gr.Dataframe(headers=["org","url","email","phone","profession","source_query"], label="Search results")

    dropdown_sel = gr.Dropdown(label="Select organization (from results)", choices=[])

    with gr.Row():
        use_anon = gr.Checkbox(value=True, label="Use anonymized summary (recommended)")
        anon_out = gr.Textbox(label="Anonymized summary", lines=3)
    user_email_in = gr.Textbox(label="Your email (for the organisation to reply to you)")

    gr.Markdown("### Compose message")
    subject_in = gr.Textbox(value="Request for GBV support", label="Email subject")
    message_in = gr.Textbox(label="Message body", lines=10)

    # Manual override for organization email (new)
    manual_email_in = gr.Textbox(label="Manual org email (optional)")

    with gr.Accordion("Sending options (for automatic sending via Ally AI SMTP)", open=False):
        mode = gr.Radio(choices=["Draft only (mailto + .eml)", "Send via SMTP (Gmail)"], value="Draft only (mailto + .eml)", label="Delivery mode")
        sender_email_in = gr.Textbox(label="Ally AI sender email (Gmail account)")
        sender_pass_in  = gr.Textbox(label="Ally AI sender app password", type="password")
        logo_url_in     = gr.Textbox(value=ALLY_AI_LOGO_URL_DEFAULT, label="Ally AI logo URL")

    with gr.Row():
        preview_btn = gr.Button("Preview")
        confirm_btn = gr.Button("Confirm (Create Draft or Send)")

    preview_text_out = gr.Textbox(label="Preview (text)", interactive=False)
    preview_html_out = gr.HTML()
    status_out = gr.Textbox(label="Status", interactive=False)
    mailto_html_out = gr.HTML()
    eml_file_out = gr.File(label="Download .eml")

    # Wire: Search
    def _on_search(story, country):
        s, records, options, anon = run_search(story, country)
        prefill = make_body(anon, story, True, "")  # user email unknown yet
        # return updated dropdown choices (value is first option)
        return s, records, gr.update(choices=options, value=(options[0] if options else None)), anon, prefill

    search_btn.click(_on_search,
                     inputs=[story_in, country_in],
                     outputs=[summary_out, results_table, dropdown_sel, anon_out, message_in])

    # When user toggles anonymized vs full story, refresh the message body
    def _refresh_body(use_anon_flag, anon_text, story, user_email):
        return make_body(anon_text, story, use_anon_flag, user_email)

    use_anon.change(_refresh_body, inputs=[use_anon, anon_out, story_in, user_email_in], outputs=message_in)
    user_email_in.change(_refresh_body, inputs=[use_anon, anon_out, story_in, user_email_in], outputs=message_in)
    anon_out.change(_refresh_body, inputs=[use_anon, anon_out, story_in, user_email_in], outputs=message_in)
    story_in.change(_refresh_body, inputs=[use_anon, anon_out, story_in, user_email_in], outputs=message_in)

    # Preview
    preview_btn.click(preview_contact,
                      inputs=[dropdown_sel, results_table, subject_in, message_in, manual_email_in],
                      outputs=[preview_text_out, preview_html_out])

    # Confirm (create draft or send) - manual_email_in passed as last arg
    confirm_btn.click(confirm_action,
                      inputs=[mode, dropdown_sel, results_table, subject_in, message_in,
                              user_email_in, sender_email_in, sender_pass_in, logo_url_in, manual_email_in],
                      outputs=[status_out, mailto_html_out, eml_file_out])

demo.launch(share=False)