SMS Conversation Analysis

import os, requests, time
from datetime import datetime, timedelta

TW_SID = os.environ["TWILIO_ACCOUNT_SID"]
TW_TOKEN = os.environ["TWILIO_AUTH_TOKEN"]
TW_AUTH = (TW_SID, TW_TOKEN)
TW_CONV = "https://conversations.twilio.com/v1"
MV = os.environ["MAVERA_API_KEY"]
MV_BASE = "https://app.mavera.io/api/v1"
MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}

# 1. List recent conversations
r = requests.get(f"{TW_CONV}/Conversations", auth=TW_AUTH,
    params={"PageSize": 50})
r.raise_for_status()
conversations = r.json().get("conversations", [])
print(f"Found {len(conversations)} conversations")

# 2. Fetch messages for each conversation
all_threads = []
for conv in conversations[:30]:
    sid = conv.get("sid", "")
    label = conv.get("friendly_name", conv.get("unique_name", sid))

    mr = requests.get(f"{TW_CONV}/Conversations/{sid}/Messages", auth=TW_AUTH,
        params={"PageSize": 50, "Order": "asc"})
    if not mr.ok:
        continue
    msgs = mr.json().get("messages", [])

    thread_msgs = []
    for m in msgs:
        thread_msgs.append({
            "author": m.get("author", "unknown"),
            "body": m.get("body", "")[:300],
            "date": m.get("date_created", "")[:16],
        })

    if thread_msgs:
        all_threads.append({"label": label, "input": thread_msgs,
                           "msg_count": len(thread_msgs)})
    time.sleep(0.1)

print(f"Threads with input: {len(all_threads)}, "
      f"Total input: {sum(t['msg_count'] for t in all_threads)}")

# 3. Build corpus
corpus = []
for t in all_threads[:20]:
    corpus.append(f"\n--- THREAD: {t['label']} ({t['msg_count']} messages) ---")
    for m in t["messages"]:
        corpus.append(f"[{m['date']}] {m['author']}: {m['body']}")
corpus_text = "\n".join(corpus)

# 4. Mave analysis
analysis = requests.post(f"{MV_BASE}/mave/chat", headers=MV_H, json={
    "message": f"Conversation intelligence analyst. Analyze {len(all_threads)} SMS conversation threads ({sum(t['msg_count'] for t in all_threads)} total messages).\n\n"
        f"{corpus_text[:8000]}\n\n"
        "Produce a CONVERSATION INTELLIGENCE REPORT:\n\n"
        "1. **Response Patterns** — Average messages per thread, response time distribution, who initiates\n"
        "2. **Sentiment Analysis** — Overall tone, sentiment shifts within threads, frustration signals\n"
        "3. **Drop-off Points** — Where conversations end prematurely, common last messages before silence\n"
        "4. **Top Intents** — What customers are asking about (categories with counts)\n"
        "5. **Winning Messages** — Our messages that get the best responses (highest engagement)\n"
        "6. **Problem Messages** — Our messages that cause confusion, no-reply, or negative reactions\n"
        "7. **Optimization Recommendations** — 5 specific changes to improve conversion/satisfaction\n\n"
        "Include representative quotes."
}).json()

print(f"\n{'='*60}\nSMS CONVERSATION INTELLIGENCE\n{'='*60}")
print(analysis.get("content", "")[:3000])