| import gradio as gr |
| import requests |
| from bs4 import BeautifulSoup |
| import re |
| import os |
| import urllib.parse |
| from huggingface_hub import InferenceClient |
|
|
| MODEL_ID = "Qwen/Qwen2.5-7B-Instruct" |
| PRICE_RE = re.compile(r"(?:₹|Rs\.?|INR)\s*([\d,]+(?:\.\d+)?)") |
| ASIN_RE = re.compile(r"/(?:dp|gp/product)/([A-Z0-9]{10})") |
|
|
| DDG_HEADERS = { |
| "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/124.0.0.0 Safari/537.36", |
| "Accept-Language": "en-US,en;q=0.9", |
| "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8", |
| } |
|
|
|
|
| def get_client(): |
| token = os.environ.get("HF_TOKEN", "") |
| return InferenceClient(token=token) if token else InferenceClient() |
|
|
|
|
| def clean_price(text: str): |
| if not text: |
| return None |
| m = PRICE_RE.search(str(text)) |
| if m: |
| raw = m.group(1).replace(",", "") |
| try: |
| val = int(float(raw)) |
| if 100 < val < 10_000_000: |
| return f"₹{val:,}" |
| except ValueError: |
| pass |
| return None |
|
|
|
|
| def clean_amazon_link(raw_link: str) -> str: |
| """Extract ASIN and return a clean, working Amazon.in product URL.""" |
| if not raw_link: |
| return None |
| m = ASIN_RE.search(raw_link) |
| if m: |
| return f"https://www.amazon.in/dp/{m.group(1)}" |
| |
| try: |
| parsed = urllib.parse.urlparse(raw_link) |
| if "amazon" in parsed.netloc: |
| clean = parsed._replace(query="", fragment="").geturl() |
| return clean |
| except Exception: |
| pass |
| return raw_link |
|
|
|
|
| def clean_flipkart_link(raw_link: str) -> str: |
| """Keep only essential Flipkart URL params, strip tracking.""" |
| if not raw_link: |
| return None |
| try: |
| parsed = urllib.parse.urlparse(raw_link) |
| qs = urllib.parse.parse_qs(parsed.query) |
| kept = {} |
| for k in ("pid", "lid", "marketplace"): |
| if k in qs: |
| kept[k] = qs[k] |
| new_q = urllib.parse.urlencode(kept, doseq=True) |
| return parsed._replace(query=new_q, fragment="").geturl() |
| except Exception: |
| return raw_link |
|
|
|
|
| def ddg_search(query: str, num: int = 12): |
| try: |
| resp = requests.post( |
| "https://html.duckduckgo.com/html/", |
| data={"q": query, "b": "", "kl": "in-en"}, |
| headers=DDG_HEADERS, |
| timeout=15, |
| ) |
| soup = BeautifulSoup(resp.text, "lxml") |
| results = [] |
| for item in soup.select(".result")[:num]: |
| title_el = item.select_one(".result__title") |
| snippet_el = item.select_one(".result__snippet") |
| url_el = item.select_one(".result__url") |
| link_el = item.select_one(".result__title a") |
| title = title_el.get_text(" ", strip=True) if title_el else "" |
| snippet = snippet_el.get_text(" ", strip=True) if snippet_el else "" |
| url_txt = url_el.get_text(strip=True) if url_el else "" |
| link = link_el.get("href", "") if link_el else "" |
| if link and "duckduckgo.com" in link: |
| try: |
| qs = urllib.parse.urlparse(link).query |
| params = urllib.parse.parse_qs(qs) |
| link = urllib.parse.unquote(params.get("uddg", [link])[0]) |
| except Exception: |
| pass |
| results.append({"title": title, "snippet": snippet, "url": url_txt, "link": link}) |
| return results |
| except Exception: |
| return [] |
|
|
|
|
| def normalize_query(raw: str) -> str: |
| try: |
| client = get_client() |
| resp = client.chat_completion( |
| messages=[ |
| { |
| "role": "system", |
| "content": ( |
| "You are a product search query cleaner. " |
| "Output ONLY a short, clean product name (max 8 words) suitable for searching on " |
| "Amazon India, Flipkart, and Myntra. No explanation, no punctuation at the end." |
| ), |
| }, |
| {"role": "user", "content": f"Clean this product name: {raw}"}, |
| ], |
| model=MODEL_ID, |
| max_tokens=25, |
| temperature=0.05, |
| ) |
| cleaned = resp.choices[0].message.content.strip().strip('"').strip("'") |
| if cleaned and 3 < len(cleaned) < 100: |
| return cleaned |
| except Exception as e: |
| print(f"[normalize_query] {e}") |
| return raw.strip() |
|
|
|
|
| def hf_get_prices(query: str) -> dict: |
| try: |
| client = get_client() |
| resp = client.chat_completion( |
| messages=[ |
| { |
| "role": "system", |
| "content": ( |
| "You are a real-time Indian e-commerce price assistant. " |
| "You know current approximate prices on Amazon India, Flipkart, and Myntra. " |
| "Reply with ONLY three lines in this exact format:\n" |
| "Amazon: ₹PRICE\n" |
| "Flipkart: ₹PRICE\n" |
| "Myntra: ₹PRICE\n" |
| "If a product is not sold on a platform, write N/A. " |
| "No extra text. No explanations." |
| ), |
| }, |
| { |
| "role": "user", |
| "content": f"Current price of '{query}' on Amazon India, Flipkart, Myntra?", |
| }, |
| ], |
| model=MODEL_ID, |
| max_tokens=80, |
| temperature=0.05, |
| ) |
| text = resp.choices[0].message.content.strip() |
| result = {} |
| for line in text.splitlines(): |
| price = clean_price(line) |
| if not price: |
| continue |
| ll = line.lower() |
| if "amazon" in ll: |
| result["amazon"] = price |
| elif "flipkart" in ll: |
| result["flipkart"] = price |
| elif "myntra" in ll: |
| result["myntra"] = price |
| return result |
| except Exception as e: |
| print(f"[hf_get_prices] {e}") |
| return {} |
|
|
|
|
| def hf_ai_analysis(query: str, amazon: dict, flipkart: dict, myntra: dict) -> str: |
| lines = [] |
| for r in [amazon, flipkart, myntra]: |
| p = r.get("price") or "N/A" |
| lines.append(f"- {r['platform']}: {p}") |
| scraped_str = "\n".join(lines) |
| try: |
| client = get_client() |
| resp = client.chat_completion( |
| messages=[ |
| { |
| "role": "system", |
| "content": "You are a smart Indian price comparison assistant called 'Come & Compare'.", |
| }, |
| { |
| "role": "user", |
| "content": ( |
| f"Product: {query}\n\nPrices:\n{scraped_str}\n\n" |
| "Reply in this exact format:\n" |
| "🏆 BEST DEAL: [platform] at [price]\n\n" |
| "📊 PRICE RANKING:\n1. [platform] — [price]\n2. ...\n\n" |
| "💡 BUYING ADVICE:\n[2-3 line recommendation]\n\n" |
| "⚠️ NOTES:\n[any warnings about unavailable prices]" |
| ), |
| }, |
| ], |
| model=MODEL_ID, |
| max_tokens=350, |
| temperature=0.3, |
| ) |
| return resp.choices[0].message.content.strip() |
| except Exception as e: |
| return f"⚠️ AI analysis unavailable: {str(e)}" |
|
|
|
|
| def get_platform_link(results, domain: str, platform: str): |
| """Return a clean, working link for the platform.""" |
| for r in results: |
| url = r.get("url", "") |
| link = r.get("link", "") |
| if domain in url or domain in link: |
| raw = link if link.startswith("http") else ("https://" + url if url else None) |
| if not raw: |
| continue |
| if platform == "Amazon.in": |
| cleaned = clean_amazon_link(raw) |
| if cleaned: |
| return cleaned |
| elif platform == "Flipkart": |
| cleaned = clean_flipkart_link(raw) |
| if cleaned: |
| return cleaned |
| else: |
| return raw |
| return None |
|
|
|
|
| def get_platform_title(results, domain: str): |
| for r in results: |
| if domain in r.get("url", "") or domain in r.get("link", ""): |
| return r.get("title", "") |
| return "" |
|
|
|
|
| def get_product_image(query: str, ddg_results: list): |
| import json |
| for r in ddg_results: |
| link = r.get("link", "") |
| if "amazon.in" in link or "amazon.com" in link: |
| try: |
| resp = requests.get(link, headers=DDG_HEADERS, timeout=8) |
| soup = BeautifulSoup(resp.text, "lxml") |
| for sel in ["#landingImage", "#imgBlkFront", ".a-dynamic-image"]: |
| img = soup.select_one(sel) |
| if img: |
| src = img.get("src", "") |
| if src and src.startswith("http"): |
| return src |
| data = img.get("data-a-dynamic-image", "") |
| if data: |
| try: |
| d = json.loads(data) |
| return max(d.keys(), key=lambda u: d[u][0] * d[u][1]) |
| except Exception: |
| pass |
| except Exception: |
| pass |
| for r in ddg_results[:5]: |
| link = r.get("link", "") |
| if not link or "duckduckgo" in link: |
| continue |
| try: |
| resp = requests.get(link, headers=DDG_HEADERS, timeout=6) |
| soup = BeautifulSoup(resp.text, "lxml") |
| og = soup.select_one("meta[property='og:image']") |
| if og and og.get("content", "").startswith("http"): |
| return og["content"] |
| except Exception: |
| pass |
| return None |
|
|
|
|
| def compare_prices(product_name, product_details, selected_platforms, progress=gr.Progress()): |
| if not product_name or not product_name.strip(): |
| return ( |
| "<p style='color:#c62828;text-align:center;padding:20px;font-size:15px'>⚠️ Please enter a product name.</p>", |
| "❌ No product entered.", |
| "", |
| ) |
|
|
| query = product_name.strip() |
| if product_details and product_details.strip(): |
| query = f"{query} {product_details.strip()}" |
|
|
| progress(0.05, desc="🤖 Normalizing query with Qwen 7B...") |
| normalized = normalize_query(query) |
|
|
| progress(0.2, desc="🔍 Searching DuckDuckGo for product links...") |
| ddg_results = ddg_search(f"{normalized} buy online india amazon flipkart myntra price", num=12) |
|
|
| progress(0.5, desc="💰 Fetching prices via Qwen 7B...") |
| hf_prices = hf_get_prices(normalized) |
|
|
| progress(0.7, desc="🖼️ Finding product image...") |
| image_url = get_product_image(normalized, ddg_results) |
|
|
| progress(0.85, desc="🤖 Running AI analysis...") |
|
|
| enc = urllib.parse.quote_plus(normalized) |
| PLATFORMS = [ |
| {"platform": "Amazon.in", "domain": "amazon.in", "color": "#FF9900", "bg": "#FFF8EE", |
| "search": f"https://www.amazon.in/s?k={enc}", "price_key": "amazon"}, |
| {"platform": "Flipkart", "domain": "flipkart.com", "color": "#2874F0", "bg": "#EEF4FF", |
| "search": f"https://www.flipkart.com/search?q={enc}", "price_key": "flipkart"}, |
| {"platform": "Myntra", "domain": "myntra.com", "color": "#FF3F6C", "bg": "#FFF0F4", |
| "search": f"https://www.myntra.com/{enc}", "price_key": "myntra"}, |
| ] |
|
|
| active_keys = {p.lower(): p for p in (selected_platforms or [])} |
| results = [] |
| for p in PLATFORMS: |
| if active_keys and not any(k in p["platform"].lower() for k in active_keys): |
| continue |
| link = get_platform_link(ddg_results, p["domain"], p["platform"]) or p["search"] |
| title = get_platform_title(ddg_results, p["domain"]) |
| price = hf_prices.get(p["price_key"]) |
| results.append({**p, "price": price, "title": title, "link": link}) |
|
|
| ai_out = hf_ai_analysis(normalized, *results[:3]) if len(results) >= 3 else "Need all 3 platforms for AI analysis." |
|
|
| progress(1.0, desc="✅ Done!") |
|
|
| table_html = _build_cards(results, image_url, normalized) |
| links_html = _build_links(normalized, results) |
| return table_html, ai_out, links_html |
|
|
|
|
| def _find_best(results): |
| found = [r for r in results if r.get("price")] |
| if not found: |
| return "" |
| def val(r): |
| return int(r["price"].replace("₹","").replace(",","").strip()) |
| try: |
| return min(found, key=val)["platform"] |
| except Exception: |
| return "" |
|
|
|
|
| def _build_cards(results, image_url, query): |
| best = _find_best(results) |
|
|
| img_html = "" |
| if image_url: |
| img_html = ( |
| f'<div style="text-align:center;margin-bottom:24px">' |
| f'<img src="{image_url}" style="max-height:220px;max-width:300px;' |
| f'border-radius:16px;object-fit:contain;background:#fff;' |
| f'padding:12px;box-shadow:0 4px 20px rgba(0,0,0,.10)" /></div>' |
| ) |
|
|
| cards = "" |
| for r in results: |
| color = r["color"] |
| bg = r["bg"] |
| price = r.get("price") |
| title = (r.get("title") or "")[:72] |
| link = r.get("link", r["search"]) |
| is_best = best and r["platform"] == best and price |
|
|
| border = f"3px solid {color}" if is_best else f"2px solid {color}33" |
| shadow = f"0 8px 28px {color}30" if is_best else "0 4px 16px rgba(0,0,0,.08)" |
| trophy = '<div style="position:absolute;top:-12px;left:50%;transform:translateX(-50%);background:#FFD700;color:#333;border-radius:20px;padding:3px 14px;font-size:11px;font-weight:700;white-space:nowrap">🏆 BEST DEAL</div>' if is_best else "" |
|
|
| price_html = ( |
| f'<div style="font-size:2rem;font-weight:800;color:{color};margin:10px 0 6px;letter-spacing:-0.5px">{price}</div>' |
| if price else |
| '<div style="font-size:1rem;color:#aaa;font-weight:500;margin:10px 0 6px">Not Available</div>' |
| ) |
| title_html = f'<div style="font-size:11px;color:#666;margin-bottom:12px;line-height:1.4;min-height:28px">{title}</div>' if title else '<div style="min-height:28px"></div>' |
| btn_html = ( |
| f'<a href="{link}" target="_blank" style="display:inline-block;background:{color};color:#fff;' |
| f'text-decoration:none;border-radius:50px;padding:8px 20px;font-size:13px;font-weight:600;' |
| f'margin-top:4px">View on {r["platform"]} →</a>' |
| ) if price else "" |
|
|
| cards += f''' |
| <div style="position:relative;background:{bg};border:{border};border-radius:20px; |
| padding:24px 18px 20px;text-align:center;flex:1;min-width:180px;max-width:240px; |
| box-shadow:{shadow};transition:transform .2s"> |
| {trophy} |
| <div style="font-size:28px;margin-bottom:6px">{"🛒" if "Amazon" in r["platform"] else "🛍️" if "Flipkart" in r["platform"] else "👗"}</div> |
| <div style="font-size:16px;font-weight:700;color:{color}">{r["platform"]}</div> |
| {price_html} |
| {title_html} |
| {btn_html} |
| </div>''' |
|
|
| cards_row = f'<div style="display:flex;gap:16px;justify-content:center;flex-wrap:wrap;margin:8px 0">{cards}</div>' |
|
|
| has_price = any(r.get("price") for r in results) |
| no_token_warn = "" if has_price else ( |
| '<div style="background:#FFF3CD;border:1px solid #FFC107;border-radius:12px;' |
| 'padding:14px 18px;margin-bottom:18px;color:#856404;font-size:13px;text-align:center">' |
| '⚠️ No prices found — make sure <b>HF_TOKEN</b> is set in Space Secrets ' |
| '(Settings → Variables and secrets)</div>' |
| ) |
|
|
| heading = ( |
| f'<div style="text-align:center;margin-bottom:18px">' |
| f'<span style="background:#E3F2FD;color:#1565C0;border-radius:20px;' |
| f'padding:6px 18px;font-size:13px;font-weight:600">📦 Results for: {query}</span></div>' |
| ) |
|
|
| return f"{no_token_warn}{heading}{img_html}{cards_row}" |
|
|
|
|
| def _build_links(query, results): |
| q = urllib.parse.quote_plus(query) |
| chips = "".join( |
| f'<a href="{r["search"]}" target="_blank" style="display:inline-block;' |
| f'background:#fff;border:1.5px solid {r["color"]};color:{r["color"]};' |
| f'border-radius:20px;padding:6px 16px;font-size:13px;font-weight:600;' |
| f'text-decoration:none;margin:4px">{r["platform"]}</a>' |
| for r in results |
| ) |
| chips += ( |
| f'<a href="https://www.google.com/search?q={q}&tbm=shop" target="_blank" ' |
| f'style="display:inline-block;background:#fff;border:1.5px solid #34A853;color:#34A853;' |
| f'border-radius:20px;padding:6px 16px;font-size:13px;font-weight:600;' |
| f'text-decoration:none;margin:4px">🌐 Google Shopping</a>' |
| ) |
| return f'<div style="padding:14px 0 6px"><p style="color:#555;margin-bottom:10px;font-size:13px">🔗 Search directly on each platform:</p>{chips}</div>' |
|
|
|
|
| CSS = """ |
| @import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700;800&display=swap'); |
| |
| *, *::before, *::after { box-sizing: border-box; } |
| |
| body, .gradio-container { |
| font-family: 'Inter', sans-serif !important; |
| background: linear-gradient(135deg, #E0F7FA 0%, #E8F5E9 40%, #E3F2FD 100%) !important; |
| min-height: 100vh; |
| } |
| |
| .gradio-container { max-width: 1100px !important; margin: 0 auto !important; } |
| |
| /* Header */ |
| .app-header { |
| text-align: center; |
| padding: 36px 24px 20px; |
| background: linear-gradient(135deg, #ffffff 0%, #F0FFFE 100%); |
| border-radius: 0 0 28px 28px; |
| box-shadow: 0 4px 24px rgba(0,150,136,.12); |
| margin-bottom: 20px; |
| } |
| |
| .app-title { |
| font-size: clamp(2rem, 5vw, 3.2rem); |
| font-weight: 800; |
| letter-spacing: -1.5px; |
| margin: 0; |
| background: linear-gradient(90deg, #FF9900 0%, #00ACC1 50%, #43A047 100%); |
| -webkit-background-clip: text; |
| -webkit-text-fill-color: transparent; |
| background-clip: text; |
| } |
| |
| .app-subtitle { font-size: .95rem; color: #546E7A; margin-top: 8px; font-weight: 500; } |
| |
| .app-badges { |
| display: flex; gap: 8px; justify-content: center; margin-top: 14px; flex-wrap: wrap; |
| } |
| .badge { |
| background: linear-gradient(135deg, #E0F7FA, #E8F5E9); |
| border: 1px solid #B2DFDB; |
| border-radius: 20px; padding: 5px 14px; |
| font-size: .75rem; color: #00695C; font-weight: 600; |
| } |
| |
| /* Input panel */ |
| label, .label-wrap { color: #263238 !important; font-weight: 600 !important; font-size: .9rem !important; } |
| |
| textarea, input[type=text] { |
| background: #ffffff !important; |
| border: 2px solid #B2DFDB !important; |
| color: #263238 !important; |
| border-radius: 12px !important; |
| font-family: 'Inter', sans-serif !important; |
| font-size: 15px !important; |
| box-shadow: 0 2px 8px rgba(0,150,136,.06) !important; |
| } |
| textarea:focus, input[type=text]:focus { |
| border-color: #00ACC1 !important; |
| outline: none !important; |
| box-shadow: 0 0 0 3px rgba(0,172,193,.15) !important; |
| } |
| |
| /* Compare button */ |
| .compare-btn { |
| background: linear-gradient(135deg, #00ACC1, #00897B) !important; |
| color: white !important; |
| border: none !important; |
| border-radius: 14px !important; |
| font-size: 1rem !important; |
| font-weight: 700 !important; |
| padding: 14px 28px !important; |
| cursor: pointer !important; |
| width: 100% !important; |
| box-shadow: 0 4px 18px rgba(0,172,193,.35) !important; |
| letter-spacing: .3px !important; |
| } |
| .compare-btn:hover { filter: brightness(1.08) !important; } |
| |
| /* Tabs */ |
| .tab-nav button { color: #546E7A !important; font-weight: 600 !important; } |
| .tab-nav button.selected { color: #00ACC1 !important; border-bottom-color: #00ACC1 !important; } |
| |
| /* AI output box */ |
| textarea[readonly] { |
| background: #F1FFFE !important; |
| border: 2px solid #B2EBF2 !important; |
| color: #263238 !important; |
| line-height: 1.7 !important; |
| } |
| |
| /* Checkbox */ |
| .wrap-inner { |
| background: #ffffff !important; |
| border-radius: 12px !important; |
| border: 2px solid #B2DFDB !important; |
| } |
| |
| /* Footer */ |
| .app-footer { |
| text-align: center; padding: 20px; color: #78909C; |
| font-size: .8rem; margin-top: 10px; |
| border-top: 1px solid #B2DFDB; |
| } |
| |
| footer { display: none !important; } |
| ::-webkit-scrollbar { width: 6px; } |
| ::-webkit-scrollbar-track { background: #E0F7FA; } |
| ::-webkit-scrollbar-thumb { background: #80CBC4; border-radius: 3px; } |
| """ |
|
|
| HEADER_HTML = """ |
| <div class="app-header"> |
| <h1 class="app-title">Come & Compare 🛒</h1> |
| <p class="app-subtitle">AI-powered real-time price comparison across India's top e-commerce platforms</p> |
| <div class="app-badges"> |
| <span class="badge">🤖 Qwen2.5-7B</span> |
| <span class="badge">⚡ Under 32B Parameters</span> |
| <span class="badge">🇮🇳 Amazon · Flipkart · Myntra</span> |
| <span class="badge">🏆 HF Small Models Hackathon</span> |
| </div> |
| </div> |
| """ |
|
|
| FOOTER_HTML = """ |
| <div class="app-footer"> |
| Built for the HuggingFace Build Small Hackathon 2025 · |
| Model: Qwen/Qwen2.5-7B-Instruct (<32B) · |
| Search: DuckDuckGo HTML |
| </div> |
| """ |
|
|
| with gr.Blocks(css=CSS, title="Come & Compare — Price Comparison AI") as demo: |
| gr.HTML(HEADER_HTML) |
|
|
| with gr.Row(): |
| with gr.Column(scale=1): |
| gr.Markdown("### 🔍 Search Product") |
| product_name = gr.Textbox( |
| label="Product Name", |
| placeholder='e.g. "Nike Air Force 1 White" or "Samsung Galaxy S24 128GB"', |
| lines=1, |
| ) |
| product_details = gr.Textbox( |
| label="Additional Details (optional)", |
| placeholder="e.g. size, color, model number...", |
| lines=2, |
| ) |
| platform_select = gr.CheckboxGroup( |
| choices=["Amazon.in", "Flipkart", "Myntra"], |
| value=["Amazon.in", "Flipkart", "Myntra"], |
| label="Platforms to Search", |
| ) |
| compare_btn = gr.Button("🔍 Compare Prices Now", elem_classes=["compare-btn"]) |
| gr.Markdown("**💡 Tip:** Include brand + model for best results.") |
|
|
| with gr.Column(scale=2): |
| with gr.Tabs(): |
| with gr.TabItem("📊 Results"): |
| results_html = gr.HTML() |
| links_html = gr.HTML() |
| with gr.TabItem("🤖 AI Analysis"): |
| ai_output = gr.Textbox( |
| label="AI Recommendation (Qwen2.5-7B)", |
| lines=15, |
| interactive=False, |
| ) |
|
|
| gr.HTML(FOOTER_HTML) |
|
|
| gr.Examples( |
| examples=[ |
| ["iPhone 15 128GB", "Apple, Black"], |
| ["Nike Air Force 1", "White, Size 9 UK"], |
| ["Samsung 55 inch 4K TV","Smart TV"], |
| ["boAt Airdopes 141", ""], |
| ["OnePlus Nord CE 4", "8GB RAM 128GB"], |
| ], |
| inputs=[product_name, product_details], |
| label="🌟 Try these examples", |
| ) |
|
|
| compare_btn.click( |
| fn=compare_prices, |
| inputs=[product_name, product_details, platform_select], |
| outputs=[results_html, ai_output, links_html], |
| ) |
|
|
| if __name__ == "__main__": |
| demo.launch(share=False) |
|
|
