Upload files to "backend"

backend/Dockerfile

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+FROM python:3.12-slim
+RUN apt-get update && apt-get install -y \
+    libnss3 libatk-bridge2.0-0 libdrm2 libxkbcommon0 libxcomposite1 libxdamage1 libxrandr2 libgbm1 libxss1 libasound2 \
+    && rm -rf /var/lib/apt/lists/*
+WORKDIR /app
+RUN pip install playwright && playwright install --with-deps chromium
+COPY requirements.txt .
+RUN pip install -r requirements.txt
+COPY app.py .
+EXPOSE 8000
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]

backend/app.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from playwright.sync_api import sync_playwright
+from bs4 import BeautifulSoup
+import requests
+from openai import OpenAI
+import random
+from datetime import datetime
+import pandas as pd
+import io
+import os
+import time
+
+app = FastAPI(title="Deal Hunter Backend")
+client = OpenAI(api_key=os.getenv("GROK_API_KEY"), base_url="https://api.x.ai/v1")
+TELEGRAM_TOKEN = os.getenv("TELEGRAM_TOKEN")
+TELEGRAM_CHAT_ID = os.getenv("TELEGRAM_CHAT_ID")
+
+class SearchRequest(BaseModel):
+    item: str = "iPhone 14 Pro Max"
+    location_radius: int = 50
+    min_price: float = 0
+    max_price: float = 1000
+    condition: str = "Any"
+    min_score: int = 7
+
+@app.post("/hunt")
+def hunt_deals(req: SearchRequest):
+    # Scrape FB
+    ua = random.choice([
+        "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36",
+        "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36"
+    ])
+    url = f"https://www.facebook.com/marketplace/search/?query={req.item.replace(' ', '+')}&radius={req.location_radius}"
+    listings = []
+    with sync_playwright() as p:
+        browser = p.chromium.launch(headless=True)
+        page = browser.new_page(user_agent=ua)
+        page.goto(url, wait_until="networkidle")
+        time.sleep(5)
+        for _ in range(3):
+            page.evaluate("window.scrollTo(0, document.body.scrollHeight)")
+            time.sleep(3)
+        soup = BeautifulSoup(page.content(), 'html.parser')
+        articles = soup.find_all('div', {'role': 'article'})[:20]
+        for article in articles:
+            try:
+                title = article.find('span', class_='x1lliihq').text.strip() or 'N/A'
+                price_elem = article.find('span', attrs={'aria-label': lambda v: v and '$' in v})
+                price = float(price_elem['aria-label'].replace('$', '').replace(',', '')) if price_elem else 0
+                if req.min_price <= price <= req.max_price:
+                    link = 'https://fb.com' + article.find('a')['href'] if article.find('a') else 'N/A'
+                    img = article.find('img')['src'] if article.find('img') else 'N/A'
+                    cond = 'New' if 'new' in title.lower() else 'Used'
+                    if req.condition == "Any" or cond == req.condition:
+                        listings.append({'title': title, 'price': price, 'link': link, 'image': img, 'condition': cond})
+            except:
+                continue
+        browser.close()
+
+    # Value & Score
+    deals = []
+    for listing in listings:
+        query = listing['title'].replace(' ', '+')
+        ebay_url = f"https://www.ebay.com/sch/i.html?_nkw={query}&LH_Sold=1&LH_Complete=1&rt=nc"
+        response = requests.get(ebay_url, headers={'User-Agent': ua}, timeout=10)
+        soup = BeautifulSoup(response.text, 'html.parser')
+        prices = [float(span.text.replace('$', '').replace(',', '')) for span in soup.find_all('span', class_='s-item__price')[:5] if span.text.replace('$', '').replace(',', '').replace('.', '').isdigit()]
+        resale = sum(prices) / len(prices) if prices else 0
+        offer = resale * (1 - 0.3)
+        score = min(10, (resale - listing['price']) / resale * 10) + (2 if listing['condition'] == 'New' else 0) if resale > 0 else 0
+        status = 'Hot Deal' if score >= req.min_score else 'Pass'
+
+        # AI Offer
+        prompt = f"Polite lowball offer for {listing['title']} at ${listing['price']}, condition {listing['condition']}, resale ${resale}. Offer ${offer}."
+        response = client.chat.completions.create(model="grok-beta", messages=[{"role": "user", "content": prompt}])
+        ai_offer = response.choices[0].message.content
+
+        deal = {**listing, 'resale_avg': resale, 'offer': offer, 'score': score, 'status': status, 'ai_offer': ai_offer}
+        deals.append(deal)
+
+        if status == 'Hot Deal':
+            caption = f"*Hot Deal!*\n*{listing['title']}*\nPrice: ${listing['price']}\nOffer: ${offer}\nScore: {score}/10\n{ai_offer}\n[Link]({listing['link']})"
+            requests.post(f"https://api.telegram.org/bot{TELEGRAM_TOKEN}/sendPhoto", data={'chat_id': TELEGRAM_CHAT_ID, 'caption': caption, 'parse_mode': 'Markdown'}, files={'photo': requests.get(listing['image']).content} if listing['image'].startswith('http') else {})
+
+    # CSV
+    df = pd.DataFrame(deals)
+    csv_buffer = io.StringIO()
+    df.to_csv(csv_buffer, index=False)
+    csv_data = csv_buffer.getvalue()
+
+    return {"deals": deals, "csv": csv_data, "hot_count": len([d for d in deals if d['status'] == 'Hot Deal'])}
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)

backend/requirements.txt

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+fastapi
+uvicorn
+playwright
+beautifulsoup4
+requests
+openai
+pandas