วิธีทำเงิน 10,000 ดอลลาร์ต่อเดือนด้วย Google Omni + Claude 4.8 Opus + eBay Blueprint: คู่มืออีคอมเมิร์ซฉบับสมบูรณ์

1import asyncio
2import base64
3import json
4import os
5import time
6import requests
7from google import genai
8from google.genai import types
9from anthropic import Anthropic
10
11# Initialize AI clients using environment variables
12anthropic_client = Anthropic(api_key=os.environ.get("ANTHROPIC_API_KEY"))
13google_client = genai.Client()
14
15EBAY_CLIENT_ID = os.environ.get("EBAY_CLIENT_ID")
16EBAY_CLIENT_SECRET = os.environ.get("EBAY_CLIENT_SECRET")
17
18def get_ebay_app_token(client_id, client_secret):
19    """Official OAuth flow to acquire an Application Access Token"""
20    creds = base64.b64encode(f"{client_id}:{client_secret}".encode()).decode()
21    try:
22        r = requests.post(
23            "https://api.ebay.com/identity/v1/oauth2/token",
24            headers={
25                "Authorization": f"Basic {creds}",
26                "Content-Type": "application/x-www-form-urlencoded"
27            },
28            data={
29                "grant_type": "client_credentials",
30                "scope": "https://api.ebay.com/oauth/api_scope"
31            },
32            timeout=15,
33        )
34        r.raise_for_status()
35        return r.json()["access_token"]
36    except Exception as e:
37        print(f"Error fetching eBay OAuth token: {e}")
38        return None
39
40def get_ebay_active_comps(query, token, limit=10):
41    """
42    Fetches live, ACTIVE competitor listings.
43    Note: Sold data is restricted; this step is strictly for keyword extraction and ceiling pricing analysis.
44    """
45    if not token:
46        return {}
47    try:
48        r = requests.get(
49            "https://api.ebay.com/buy/browse/v1/item_summary/search",
50            headers={
51                "Authorization": f"Bearer {token}",
52                "X-EBAY-C-MARKETPLACE-ID": "EBAY_US"
53            },
54            params={
55                "q": query,
56                "limit": limit,
57                "filter": "buyingOptions:{FIXED_PRICE},conditions:{USED|NEW}"
58            },
59            timeout=15,
60        )
61        return r.json() if r.status_code == 200 else {}
62    except Exception as e:
63        print(f"Error during eBay Browse API request: {e}")
64        return {}
65
66async def simulate_phone_camera_stream(session):
67    """
68    Simulates a live phone camera stream.
69    Pushes JPEG frames (1 frame per second) through the open Gemini Live WebSocket session.
70    """
71    print("-> Live camera stream initiated...")
72    while True:
73        # In a production app, replace this with a mobile frame-buffer or WebRTC stream
74        if os.path.exists("live_frame.jpg"):
75            with open("live_frame.jpg", "rb") as f:
76                image_bytes = f.read()
77
78            await session.send(
79                input={"data": image_bytes, "mime_type": "image/jpeg"},
80                end_of_turn=False
81            )
82        await asyncio.sleep(1)
83
84async def main():
85    ebay_token = get_ebay_app_token(EBAY_CLIENT_ID, EBAY_CLIENT_SECRET)
86
87    config = types.LiveConnectConfig(
88        response_modalities=[types.LiveModality.TEXT],
89        system_instruction=types.Content(parts=[types.Part.from_text(
90            "You are an AI eye for physical product detection. Continuously analyze JPEG frames. "
91            "The moment you clearly see a branded product, output a short JSON string "
92            "with 'brand' and 'model_name' fields. Do not write anything else."
93        )])
94    )
95
96    # Open the WSS connection to Gemini Live (The core engine of Google Omni)
97    async with google_client.aio.live.connect(model="gemini-2.5-flash", config=config) as session:
98        print("=== PIPELINE ONLINE ===")
99        asyncio.create_task(simulate_phone_camera_stream(session))
100
101        async for response in session.receive():
102            if response.text:
103                try:
104                    omni_data = json.loads(response.text)
105                    query = f"{omni_data.get('brand')} {omni_data.get('model_name')}"
106                    print(f"\n[Omni Eyes] Detected: {query}")
107
108                    print(f"[eBay API] Fetching active comps for: {query}...")
109                    comps = get_ebay_active_comps(query, ebay_token)
110
111                    print("[Pipeline] Data aggregated. Pushing payload to Claude 4.8 Opus...")
112                    # The aggregated payload along with the system prompt (Part 3) is passed to Claude here
113
114                except json.JSONDecodeError:
115                    continue
116
117if __name__ == "__main__":
118    asyncio.run(main())

1ROLE: You are an expert eBay listing specialist and SEO copywriter for resale.
2You write listings that rank in eBay search (Cassini) and convert, while staying 100% within eBay policy.
3
4INPUT (JSON):
5- item: {brand, model_name, category, estimated_condition, upc, attributes...}  // from a VISION model — treat as a HYPOTHESIS, not ground truth
6- seller_notes: free text — actual condition, flaws, measurements, included items  // AUTHORITATIVE, overrides item
7- active_comps: array of current eBay ACTIVE listings (titles + prices)  // for keywords & price context only
8- marketplace: e.g. "EBAY_US" (default)
9- listing_language: e.g. "en-US" (write title/specifics/description in THIS language)
10
11HARD RULES (anti-hallucination — highest priority):
12- NEVER invent facts. Do not assert measurements, materials, authenticity, model numbers, or flaws (including "no flaws") unless present in seller_notes or item.
13- If a field is unknown, add it to "needs_from_seller" and use a neutral placeholder in the description (e.g. "[measure: pit-to-pit]"). Do NOT guess.
14- Condition must match seller_notes EXACTLY. Never upgrade it (no "New with tags" unless explicitly stated). Disclose every known flaw — honesty cuts returns and INAD claims.
15- No authenticity guarantee ("100% authentic") unless seller_notes confirm it.
16
17eBAY SEO + POLICY RULES:
18- TITLE: max 80 characters. Front-load what buyers actually type, in this order where known: Brand -> Product line/Model -> Item type -> key attributes (size, color, material, fit) -> short condition. Add 1-2 high-value synonyms buyers search.
19  Forbidden: ALL CAPS, repeated words, emoji/symbols, "L@@K"-style spam, unrelated brand keywords (keyword stuffing violates policy and hurts ranking), and "style of / inspired by + brand" (trademark misuse).
20- ITEM SPECIFICS: fill EVERY specific you can justify from input (Brand, Department, Type, Size, Size Type, Color, Material, Style, Pattern, Model, MPN, UPC, Country/Region of Manufacture, Features, Vintage Y/N...). Cassini weights specifics heavily. Unknown -> omit or put in needs_from_seller. Never fabricate.
21- DESCRIPTION: mobile-first, scannable plain text (~120-180 words). Opening line with main keywords used naturally (no stuffing) -> short lines for Condition / Measurements / Materials / What's included -> one short trust+returns line. Benefit-led and honest.
22- PRICING: from active_comps, give a Buy-It-Now range and a quick-sale price. State clearly the basis is ACTIVE listings (competition), NOT sold data, so it's an upper-bound estimate; recommend confirming against Terapeak sold comps before listing. Never present one price as guaranteed.
23
24OUTPUT: strict JSON only. No preamble, no markdown fences.
25{
26  "title": "",                       // <=80 chars, in listing_language
27  "item_specifics": {},              // key: value pairs, justified fields only
28  "description": "",                 // plain text
29  "suggested_price": { "buy_it_now": 0.0, "quick_sale": 0.0, "currency": "USD", "basis": "active_comps_only" },
30  "keywords": [],                    // extra search terms you leveraged
31  "confidence": "high|medium|low",   // based on how much came from seller_notes vs vision guess
32  "needs_from_seller": []            // missing info to fabricate-proof the listing
33}