update blogs

Dockerfile

@@ -1,30 +1,29 @@
-# Dockerfile for Weather Assistant Demo
-# This file configures how the application will run on Hugging Face Spaces
+# Start with Python 3.12 base image
+FROM python:3.12-slim
 
-# Start with Python 3.12 as our base image
-# This gives us a clean Python environment to work with
-FROM python:3.12
+# Install system dependencies required by Playwright + Chromium
+RUN apt-get update && apt-get install -y \
+    wget curl gnupg ca-certificates fonts-liberation libappindicator3-1 \
+    libasound2 libatk-bridge2.0-0 libatk1.0-0 libcups2 \
+    libdbus-1-3 libgdk-pixbuf2.0-0 libnspr4 libnss3 libx11-xcb1 \
+    libxcomposite1 libxdamage1 libxrandr2 xdg-utils libgbm1 libxshmfence1 \
+    libxss1 libxtst6 libdrm2 libgtk-3-0 \
+ && apt-get clean && rm -rf /var/lib/apt/lists/*
 
 # Install UV package manager
-# UV is a fast, reliable Python package installer written in Rust
-# It's much faster than pip for installing dependencies
 RUN pip install uv
 
-
-# Copy all files from our project into the container
-# This includes app.py, pyproject.toml, and other necessary files
+# Copy project files
 COPY . .
 
-# Use UV to install all dependencies specified in pyproject.toml
-# This ensures we have the same packages as in local development
+# Install Python dependencies
 RUN uv sync
 
-# Configure the container to listen on port 7860
-# This is the default port that Hugging Face Spaces expects
+# Install Playwright and Chromium
+RUN playwright install --with-deps
+
+# Expose Shiny port
 EXPOSE 7860
 
-# Command to run when the container starts
-# This launches the Shiny app with specific host and port settings
-# - host 0.0.0.0 allows connections from outside the container
-# - port 7860 matches our EXPOSE setting above
-CMD ["/.venv/bin/shiny", "run", "app.py", "--host", "0.0.0.0", "--port", "7860"]
+# Run the Shiny app
+CMD ["/.venv/bin/shiny", "run", "app.py", "--host", "0.0.0.0", "--port", "7860"]

pyproject.toml

@@ -22,4 +22,6 @@ dependencies = [
     "pytrends>=4.9.2",
     "requests>=2.32.4",
     "tweepy>=4.16.0",
+    "bs4>=0.0.2",
+    "playwright>=1.53.0",
 ]

server/blog.py

@@ -1,115 +1,195 @@
 from shiny import reactive, render, ui
-import sys, os
-from dotenv import load_dotenv
-import requests
-import warnings
+import os, sys
+from bs4 import BeautifulSoup
 from pytrends.request import TrendReq
+from playwright.async_api import async_playwright
+import requests
+import re, ast
+import time
+
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "code")))
+from llm_connect import get_response
 
+from dotenv import load_dotenv
 load_dotenv()
-warnings.filterwarnings("ignore", category=FutureWarning)
 
 SHOPIFY_STORE = "ultima-supply.myshopify.com"
 SHOPIFY_TOKEN = os.getenv("SHOPIFY_TOKEN")
 SHOPIFY_API_VERSION = "2024-04"
 BLOG_ID = "73667707064"
-BRAVE_API_KEY = os.getenv("BRAVE_API_KEY")
 
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "code")))
-from llm_connect import get_response  # assumed to exist
+# === Async JS-rendered scraping ===
+async def scrape_div_content_from_url(url: str) -> str:
+    try:
+        async with async_playwright() as p:
+            browser = await p.chromium.launch(headless=True)
+            page = await browser.new_page()
+            await page.goto(url, wait_until="networkidle")
+            html = await page.content()
+            await browser.close()
+
+        soup = BeautifulSoup(html, "html.parser")
+        divs = soup.find_all("div", class_="article-body")
+        if not divs:
+            print("[WARN] No <div class='article-body'> found.")
+            return ""
+
+        texts = [div.get_text(separator=" ", strip=True) for div in divs]
+        return "\n\n".join(texts)
+    except Exception as e:
+        print(f"[ERROR] Failed to render or scrape: {e}")
+        return ""
 
-# === UTILS ===
+# === Async keyword + scrape + fallback logic ===
+async def get_keywords_and_content(url: str, top_n=5, llm_n=25):
+    scraped_text = await scrape_div_content_from_url(url)
+    if not scraped_text:
+        print("[ERROR] No scraped content. Cannot proceed.")
+        return [], ""
 
-def get_brave_snippets(query, num_results=20):
-    if not BRAVE_API_KEY:
-        print("[ERROR] Missing BRAVE_API_KEY in environment.")
-        return []
+    # === Step 1: Extract condensed topic keywords ===
+    try:
+        condensed_prompt = (
+            "From the content below, extract 5 to 7 mid-specific Google search phrases that reflect real user intent. "
+            "They should describe product types, use cases, or collector topics — not brand names alone. "
+            "Avoid single-word topics and overly broad terms like 'pokemon'. Each phrase should be 2–5 words, lowercase, and ASCII only.\n\n"
+            "You MUST return ONLY a valid Python list of strings. Do not use bullet points, newlines, or any explanation. "
+            "Your response must look exactly like this format:\n"
+            "['phrase one', 'phrase two', 'phrase three']\n\n"
+            f"Content:\n{scraped_text}"
+        )
 
-    query = query.strip()
-    if not query or len(query) < 3:
-        print("[ERROR] Invalid or empty Brave search query.")
-        return []
+        condensed_topic_raw = get_response(
+            input=condensed_prompt,
+            template=lambda x: x.strip(),
+            llm="gemini",
+            md=False,
+            temperature=0.6,
+            max_tokens=100,
+            model_name="gemini-2.0-flash-lite"
+        )
+        print(condensed_topic_raw)
 
-    url = "https://api.search.brave.com/res/v1/web/search"
-    headers = {
-        "Accept": "application/json",
-        "X-Subscription-Token": BRAVE_API_KEY
-    }
-    params = {
-        "q": query,
-        "count": min(num_results, 30),
-        "search_lang": "en"
-    }
+        match = re.search(r"\[.*?\]", condensed_topic_raw, re.DOTALL)
+        condensed_topic = ast.literal_eval(match.group(0)) if match else []
+        if not condensed_topic:
+            condensed_topic = ["trading cards"]
 
-    try:
-        response = requests.get(url, headers=headers, params=params)
-        if response.status_code == 422:
-            print(f"[ERROR] Brave rejected the query: {query}")
-            print(f"[DETAIL] Response: {response.text}")
-            return []
-
-        response.raise_for_status()
-        data = response.json()
-        return [
-            r.get("description", "")
-            for r in data.get("web", {}).get("results", [])
-            if "description" in r
-        ]
+        print(f"[INFO] Condensed topic keywords: {condensed_topic}")
     except Exception as e:
-        print(f"[ERROR] Brave search failed: {e}")
-        return []
+        print(f"[WARN] Could not infer topics: {e}")
+        condensed_topic = ["trading cards"]
 
+    # === Step 2: Pull suggestions from PyTrends ===
+    all_suggestions = set()
+    try:
+        pytrends = TrendReq(hl="en-US", tz=360, timeout=10)
+        for topic in condensed_topic:
+            time.sleep(5)
+            suggestions = pytrends.suggestions(keyword=topic)
+            if suggestions:
+                titles = [s["title"] for s in suggestions]
+                all_suggestions.update(titles)
+                print(f"[INFO] Suggestions for '{topic}': {titles[:3]}")
+    except Exception as e:
+        print(f"[WARN] PyTrends suggestions failed: {e}")
+
+    all_suggestions = list(all_suggestions)
+
+    # === Step 3: Let Gemini filter suggestions for relevance ===
+    filtered_keywords = []
+    if all_suggestions:
+        filter_prompt = (
+            f"The following article was scraped:\n\n{scraped_text[:1500]}\n\n"
+            f"Here is a list of keyword suggestions:\n{all_suggestions}\n\n"
+            "Return only the keywords that are clearly relevant to the article topic. "
+            "Return a valid Python list of strings only. No explanation, bullets, or formatting."
+        )
 
-def get_keywords_and_research(topic: str, top_n=5, llm_n=25):
-    research_snippets = get_brave_snippets(topic)
-    research_context = "\n".join(research_snippets)
+        raw_filtered = get_response(
+            input=filter_prompt,
+            template=lambda x: x.strip(),
+            llm="gemini",
+            md=False,
+            temperature=0.3,
+            max_tokens=200
+        )
 
-    pytrend_keywords = []
-    try:
-        pytrends = TrendReq(
-            hl="en-US",
-            tz=360,
-            proxies=[],
-            retries=5,
-            backoff_factor=0.5,
-            requests_args={"verify": False},
-            timeout=(10, 30)
+        match = re.search(r"\[.*?\]", raw_filtered)
+        if match:
+            try:
+                filtered_keywords = ast.literal_eval(match.group(0))
+            except:
+                filtered_keywords = []
+
+    # === Step 4: Fallback to Gemini keyword generation if needed ===
+    if not filtered_keywords:
+        fallback_prompt = (
+            f"You are an SEO expert. Generate {llm_n} niche-relevant SEO keywords "
+            f"based on this content:\n\n{scraped_text}\n\n"
+            "Return a comma-separated list of lowercase 2–5 word search phrases. No formatting."
+        )
+        fallback_keywords_raw = get_response(
+            input=fallback_prompt,
+            template=lambda x: x.strip(),
+            llm="gemini",
+            md=False,
+            temperature=0.7,
+            max_tokens=400
+        )
+        filtered_keywords = [kw.strip() for kw in fallback_keywords_raw.split(",") if kw.strip()]
+        print(f"[INFO] Fallback keywords used: {filtered_keywords[:top_n]}")
+
+    # === Step 5: Enforce minimum of 30 keywords ===
+    combined_keywords = list(dict.fromkeys(filtered_keywords))  # remove duplicates
+    if len(combined_keywords) < 30:
+        needed = 30 - len(combined_keywords)
+        print(f"[INFO] Need {needed} more keywords to reach 30. Using Gemini to pad.")
+
+        pad_prompt = (
+            f"The following article content is missing SEO keyword coverage:\n\n"
+            f"{scraped_text}\n\n"
+            f"Generate exactly {needed} additional SEO keyword phrases. "
+            "Each keyword must be:\n"
+            "- 2 to 5 words long\n"
+            "- lowercase only\n"
+            "- written in ASCII (no symbols or accents)\n"
+            "- clearly relevant to the article\n"
+            "- not overlapping with any common generic terms like 'pokemon'\n\n"
+            "You MUST return a valid Python list of strings. DO NOT include any explanation, extra text, markdown, or formatting.\n"
+            "Format example:\n"
+            "['keyword one', 'keyword two', 'keyword three']"
         )
 
-        pytrends.build_payload([topic], geo="US", timeframe="today 1-m")
-        related = pytrends.related_queries()
 
-        top_df = related.get(topic, {}).get("top")
-        if top_df is not None and not top_df.empty:
-            pytrend_keywords = top_df["query"].head(top_n).tolist()
-        else:
-            print("[INFO] PyTrends related_queries() returned empty.")
-    except Exception as e:
-        print(f"[WARN] PyTrends failed: {e}")
+        pad_raw = get_response(
+            input=pad_prompt,
+            template=lambda x: x.strip(),
+            llm="gemini",
+            md=False,
+            temperature=0.7,
+            max_tokens=200
+        )
+
+        pad_keywords = []
 
-    # Generate LLM keywords
-    prompt = (
-        f"You are an SEO assistant. Given the blog topic: '{topic}',\n"
-        f"and this research context:\n{research_context[:1000]}\n\n"
-        f"List {llm_n} high-impact, niche-relevant SEO keywords. Respond with a comma-separated list only."
-    )
+        pad_match = re.search(r"\[[^\]]+\]", pad_raw)  # greedy, non-linebreaking
+        if pad_match:
+            try:
+                pad_keywords = ast.literal_eval(pad_match.group(0))
+            except Exception as e:
+                print(f"[WARN] ast.literal_eval failed: {e}")
+                pad_keywords = []
 
-    llm_keywords_raw = get_response(
-        input=prompt,
-        template=lambda x: x,
-        llm="gemini",
-        md=False,
-        temperature=0.7,
-        max_tokens=1000
-    )
+        combined_keywords = list(dict.fromkeys(combined_keywords + pad_keywords))
+        print(f"[INFO] Padded {len(pad_keywords)} keywords:", pad_keywords)
 
-    llm_keywords = [kw.strip() for kw in llm_keywords_raw.split(",") if kw.strip()]
 
-    # Combine + deduplicate
-    all_keywords = list(dict.fromkeys(pytrend_keywords + llm_keywords))[:30]
+    return combined_keywords[:30], scraped_text
 
-    return all_keywords, research_context
 
 
+# === Shopify publisher ===
 def publish_blog_post(title: str, html_body: str, blog_id: str = BLOG_ID):
     url = f"https://{SHOPIFY_STORE}/admin/api/{SHOPIFY_API_VERSION}/blogs/{blog_id}/articles.json"
     headers = {
@@ -129,60 +209,64 @@ def publish_blog_post(title: str, html_body: str, blog_id: str = BLOG_ID):
     else:
         return False, response.text
 
-# === SERVER ===
-
+# === SHINY SERVER ===
 def server(input, output, session):
     related_keywords = reactive.Value([])
-    generated_blog = reactive.Value(("", ""))  # (seo_title, html_content)
+    generated_blog = reactive.Value(("", ""))  # (title, html_content)
 
     @output
     @render.ui
     @reactive.event(input.generate_btn)
-    def blog_result():
-        topic = input.topic()
-        if not topic:
-            return ui.HTML("<p><strong>⚠️ Please enter a topic before generating.</strong></p>")
+    async def blog_result():
+        url = input.url()
+        if not url:
+            return ui.HTML("<p><strong>⚠️ Please enter a URL.</strong></p>")
 
-        keywords, research = get_keywords_and_research(topic)
+        keywords, scraped = await get_keywords_and_content(url)
         related_keywords.set(keywords)
         keyword_str = ", ".join(keywords)
 
-        # Generate blog content
+        # Title generation from scraped text
+        infer_topic_prompt = (
+            f"Based on the following article content:\n\n{scraped[:2000]}\n\n"
+            f"Return a short, descriptive blog post title (max 70 characters)."
+            f"Return ONLY the TITLE"
+        )
+        seo_title = get_response(
+            input=infer_topic_prompt,
+            template=lambda x: x.strip().replace('"', ''),
+            llm="gemini",
+            md=False,
+            temperature=0.5,
+            max_tokens=20
+        )
+
+        # Blog generation with injected SEO
         prompt = (
-            f"You are a blog writer for a hobby brand called 'Ultima Supply'.\n"
-            f"Write an SEO-optimized blog post using these keywords: {keyword_str}.\n"
-            f"Use <h1>, <h2>, and <p> tags appropriately.\n\n"
-            f"Base your blog on the following research:\n{research}\n\n"
-            f"Topic: {topic}\n"
-            f"Make sure to be very detailed, and have enough content to capture search."
+            f"You are a content writer for a collectibles brand called 'Ultima Supply'.\n"
+            f"Given the following scraped content:\n\n{scraped}\n\n"
+            f"Adapt this into an engaging, original, and heavily detailed SEO-optimized blog post.\n"
+            f"Inject the following SEO keywords naturally and organically throughout the content:\n{keyword_str}\n\n"
+            f"Use proper HTML structure: <h1> for the title, <h2> for section headers, and <p> for all paragraphs.\n"
+            f"Do NOT include any markdown, code blocks, or triple backticks. Do NOT use ```html or any formatting fences.\n"
+            f"Just return the raw HTML.\n\n"
+            f"DO NOT include any hyperlinks or images inside the body of the blog post.\n"
+            f"At the very end, add a single call-to-action in a new <p> tag:\n"
+            f"Visit <a href='https://ultima-supply.myshopify.com'>Ultima Supply</a> to explore more collectibles."
         )
 
         blog_html = get_response(
             input=prompt,
-            template=lambda x: x,
+            template=lambda x: x.strip(),
             llm="gemini",
             md=False,
-            temperature=0.8,
+            temperature=0.9,
             max_tokens=5000
         )
 
-        # Generate SEO-optimized title
-        title_prompt = (
-            f"You are an SEO expert helping a blog called 'Ultima Supply'.\n"
-            f"Given the following topic: {topic}\n"
-            f"And these SEO keywords: {keyword_str}\n"
-            f"Generate a short, engaging, SEO-optimized blog title (max 70 characters).\n"
-            f"Return only the title."
-        )
+        blog_html = re.sub(r"```[a-zA-Z]*\n?", "", blog_html).strip()
+        blog_html = blog_html.replace("```", "").strip()
 
-        seo_title = get_response(
-            input=title_prompt,
-            template=lambda x: x.strip().replace('"', ''),
-            llm="gemini",
-            md=False,
-            temperature=0.5,
-            max_tokens=20
-        )
 
         generated_blog.set((seo_title, blog_html))
 
@@ -192,10 +276,18 @@ def server(input, output, session):
         )
 
     @output
-    @render.text
+    @render.ui
     def keywords_used():
         kws = related_keywords()
-        return f"SEO Keywords Injected: {', '.join(kws)}" if kws else "No SEO keywords retrieved yet."
+        if not kws:
+            return ui.HTML("<p><strong>No SEO keywords retrieved yet.</strong></p>")
+
+        return ui.HTML(
+            f"<p><strong>✅ SEO Keywords Injected ({len(kws)}):</strong></p><ul>"
+            + "".join(f"<li>{kw}</li>" for kw in kws) +
+            "</ul>"
+        )
+
 
     @reactive.effect
     @reactive.event(input.post_btn)

ui/blog.py

@@ -2,8 +2,10 @@ from shiny import ui
 
 ui = ui.nav_panel(
     "Blog Writer",
-    ui.input_text("topic", "Enter Blog Topic", placeholder="e.g. Best One Piece Cards in 2025"),
-    
+
+    # Only need URL input now
+    ui.input_text("url", "Enter Article URL", placeholder="e.g. https://example.com/some-article"),
+
     ui.div(
         ui.input_action_button("generate_btn", "Generate Blog"),
         ui.input_action_button("post_btn", "Post to Shopify", class_="ms-3"),
@@ -11,5 +13,5 @@ ui = ui.nav_panel(
     ),
 
     ui.output_ui("blog_result"),
-    ui.output_text('keywords_used')
+    ui.output_ui("keywords_used")
 )

uv.lock

@@ -173,6 +173,19 @@ wheels = [
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 ]
 
+[[package]]
+name = "beautifulsoup4"
+version = "4.13.4"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "soupsieve" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/d8/e4/0c4c39e18fd76d6a628d4dd8da40543d136ce2d1752bd6eeeab0791f4d6b/beautifulsoup4-4.13.4.tar.gz", hash = "sha256:dbb3c4e1ceae6aefebdaf2423247260cd062430a410e38c66f2baa50a8437195", size = 621067 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/50/cd/30110dc0ffcf3b131156077b90e9f60ed75711223f306da4db08eff8403b/beautifulsoup4-4.13.4-py3-none-any.whl", hash = "sha256:9bbbb14bfde9d79f38b8cd5f8c7c85f4b8f2523190ebed90e950a8dea4cb1c4b", size = 187285 },
+]
+
 [[package]]
 name = "black"
 version = "25.1.0"
@@ -225,6 +238,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/01/b6/dcd0fd188cc28d772e0df23a31ce50af4d358ef31bfee969dc5a033482a5/botocore-1.39.0-py3-none-any.whl", hash = "sha256:d8e72850d3450aeca355b654efb32c8370bf824c1945a61cad2395dc2688581e", size = 13753356 },
 ]
 
+[[package]]
+name = "bs4"
+version = "0.0.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "beautifulsoup4" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/c9/aa/4acaf814ff901145da37332e05bb510452ebed97bc9602695059dd46ef39/bs4-0.0.2.tar.gz", hash = "sha256:a48685c58f50fe127722417bae83fe6badf500d54b55f7e39ffe43b798653925", size = 698 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/51/bb/bf7aab772a159614954d84aa832c129624ba6c32faa559dfb200a534e50b/bs4-0.0.2-py2.py3-none-any.whl", hash = "sha256:abf8742c0805ef7f662dce4b51cca104cffe52b835238afc169142ab9b3fbccc", size = 1189 },
+]
+
 [[package]]
 name = "cachetools"
 version = "5.5.2"
@@ -1532,10 +1557,12 @@ wheels = [
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