NewsAgent Pro v2

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

.github/workflows/quality_gate.yml

@@ -1,32 +0,0 @@
-name: Production Quality Gate
-
-on:
-  push:
-    branches: [ "main" ]
-  pull_request:
-    branches: [ "main" ]
-
-jobs:
-  build-and-audit:
-    runs-on: ubuntu-latest
-    
-    steps:
-    - uses: actions/checkout@v3
-    
-    - name: Set up Python 3.10
-      uses: actions/setup-python@v3
-      with:
-        python-version: "3.10"
-        
-    - name: Install Dependencies
-      run: |
-        python -m pip install --upgrade pip
-        pip install flake8
-        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
-        
-    - name: Lint with Flake8 (Style Enforcer)
-      run: |
-        # stop the build if there are Python syntax errors or undefined names
-        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
-        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
-        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics

.gitignore

@@ -1,5 +0,0 @@
-venv/
-__pycache__/
-*.pyc
-.env
-.DS_Store

CONTRIBUTING.md

@@ -1,10 +0,0 @@
-# Contributing to NewsAgent Pro
-
-## Architecture
-*   **Orchestrator:** Python/Streamlit
-*   **AI Models:** Gemini 2.5 (Text), Flux.1-schnell (Vision)
-*   **Search:** Tavily API
-
-## Standards
-*   All PRs must pass the `quality_gate` workflow.
-*   No hardcoded API keys. Use `os.getenv`.

Dockerfile

@@ -1,22 +1,12 @@
-FROM python:3.11-slim
+FROM python:3.10-slim
 
 WORKDIR /app
-
-# Install system dependencies (needed for Pillow/Graphics)
-RUN apt-get update && apt-get install -y --no-install-recommends \
-    build-essential \
-    libglib2.0-0 \
-    && rm -rf /var/lib/apt/lists/*
-
-# Install Python deps
 COPY requirements.txt .
 RUN pip install --no-cache-dir -r requirements.txt
-
-# Copy source code
 COPY . .
 
-# Set Python path so 'src' is discoverable
-ENV PYTHONPATH=/app
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
 
-# Run app (Handles Render $PORT and HF 7860)
-CMD sh -c "streamlit run src/app.py --server.port=${PORT:-7860} --server.address=0.0.0.0 --server.enableCORS=false --server.enableXsrfProtection=false"
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0", "--server.enableCORS=false", "--server.enableXsrfProtection=false"]

LICENSE

@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2025 Owadokun Tosin Tobi 
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.

README.md

@@ -1,127 +1,108 @@
 ---
-title: NewsAgent Pro v2
-emoji: 🗞️
-colorFrom: blue
+title: NewsAgent Pro
+emoji: 💻
+colorFrom: purple
 colorTo: indigo
 sdk: docker
 pinned: false
+license: mit
+short_description: 'Your autonomous AI newsroom '
 ---
+
 <div align="center">
 
-# 🗞️ NewsAgent Pro v2
-### *The Autonomous, Self-Correcting AI Newsroom*
+# 🗞️ NewsAgent Pro: Autonomous Content Engine
+### *Multi-Modal AI Agent that Researches, Writes, and Designs Viral News.*
 
-[![Python](https://img.shields.io/badge/Python-3.11-3776AB?style=for-the-badge&logo=python&logoColor=white)](https://python.org)
-[![Streamlit](https://img.shields.io/badge/Streamlit-FF4B4B?style=for-the-badge&logo=streamlit&logoColor=white)](https://streamlit.io)
-[![LangGraph](https://img.shields.io/badge/Orchestrator-LangGraph-1C1C1C?style=for-the-badge)](https://langchain-ai.github.io/langgraph/)
-[![Groq](https://img.shields.io/badge/Inference-Groq_Llama_3.3-F55036?style=for-the-badge&logo=groq&logoColor=white)](https://groq.com)
-[![Flux](https://img.shields.io/badge/Visuals-FLUX.1_Schnell-000?style=for-the-badge&logo=huggingface)](https://huggingface.co/black-forest-labs/FLUX.1-schnell)
+[![Streamlit](https://img.shields.io/badge/Streamlit-FF4B4B?style=for-the-badge&logo=streamlit&logoColor=white)](https://streamlit.io/)
+[![Gemini](https://img.shields.io/badge/Google%20Gemini-8E75B2?style=for-the-badge&logo=googlebard&logoColor=white)](https://ai.google.dev/)
+[![Tavily](https://img.shields.io/badge/Tavily-Search_API-000?style=for-the-badge&logo=googlechrome&logoColor=white)](https://tavily.com/)
+[![Flux](https://img.shields.io/badge/Flux.1-Image_Gen-blue?style=for-the-badge)](https://huggingface.co/black-forest-labs/FLUX.1-dev)
 
-[View Live Demo](#-live-demo) • [System Architecture](#-system-architecture) • [Deploy Now](#-deployment)
+[View Live Demo](#-live-demo) • [Architecture](#-system-architecture) • [Setup](#-installation)
 
 </div>
 
 ---
 
-## ⚡ The Problem: "Content Fatigue"
-To run a high-quality media channel today, you need a **Researcher** to find facts, a **Writer** to draft hooks, an **Editor** to fix mistakes, and a **Designer** to make thumbnails.
-Doing this manually takes hours. Most "AI Writers" just hallucinate generic slop.
+## ⚡ The Problem: "The Blank Page"
+Content creation is a bottleneck. To post high-quality news updates, a human must:
+1.  **Research:** Scrape multiple news sites to find facts.
+2.  **Write:** Draft content optimized for different platforms (X vs LinkedIn).
+3.  **Design:** Create a visually appealing image to stop the scroll.
+4.  **Format:** Ensure character limits aren't breached.
+
+**NewsAgent Pro automates this entire pipeline.**
 
 ## 🧠 The Solution: Agentic Workflow
-**NewsAgent Pro** is not a chatbot. It is a **Multi-Agent Swarm** that mimics a real newsroom.
-It reads the internet, plans a strategy, drafts content, **critiques its own work**, and designs branded visuals—all in 60 seconds.
+NewsAgent Pro is not a chatbot. It is a **Multi-Modal Agent** that connects live internet data to state-of-the-art generation models.
 
-### ✨ Key Innovations (v2.0)
-*   **🔄 Self-Correction Loop:** The **Critic Agent** reads the draft and grades it (1-10). If the score is low, it sends it back to the Writer with specific feedback. It iterates until perfection.
-*   **⚡ Hyper-Fast Inference:** Powered by **Groq (Llama 3.3)** for sub-second logic and planning.
-*   **🕵️‍♂️ Real-Time Truth:** Uses **Tavily API** to scrape live news (last 48 hours), preventing hallucinations.
-*   **🎨 AI Graphic Design:** Uses **Flux.1-Schnell** to generate cinematic backgrounds, then uses **Python Pillow** to programmatically overlay "Newsflash" headlines.
+### Key Capabilities
+*   **🕵️‍♂️ Real-Time Research:** Uses **Tavily API** to scrape news from the last 48 hours. It cites sources, ensuring factual accuracy over hallucination.
+*   **✍️ Adaptive Copywriting:** Uses **Gemini 2.5 Flash** to write platform-specific content.
+    *   *Twitter Mode:* Generates threaded tweets (<280 chars) with hooks.
+    *   *LinkedIn Mode:* Generates long-form professional insights.
+*   **🎨 AI Graphic Design:** Uses **Flux.1 (via Hugging Face)** to generate cinematic background art, then uses **Python Pillow** to programmatically overlay "Newsflash" style headlines.
 
 ---
 
 ## ⚙️ System Architecture
 
-The system uses a **Stateful Graph** (LangGraph) with conditional routing.
-
 ```mermaid
 graph LR
-    A[User Input] --> B(🧠 Planner)
-    B -->|Strategy| C(🕵️‍♂️ Researcher)
-    C -->|Facts| D(✍️ Writer)
-    D --> E{⚖️ Critic}
-    E -->|Score < 8| D
-    E -->|Score > 8| F(🎨 Designer)
-    F -->|Visuals| G[Final Output]
-
-    style E fill:#ff9999,stroke:#333,stroke-width:2px,color:black
-    style F fill:#99ff99,stroke:#333,stroke-width:2px,color:black
+    A[User Input] --> B{Researcher Node}
+    B -->|Tavily API| C[Live News Data]
+    C --> D{Writer Node}
+    D -->|Gemini 2.5| E[Draft Copy]
+    A --> F{Visual Node}
+    F -->|Flux.1| G[Background Image]
+    G -->|Pillow| H[Branded Asset]
+    E --> I[Final Output]
+    H --> I
 ```
 
-### The "Newsroom" Staff
-| Role | Model / Tool | Function |
-| :--- | :--- | :--- |
-| **Planner** | **Llama 3.3 (Groq)** | Analyzes the topic and determines the "Viral Angle." |
-| **Researcher** | **Tavily API** | Scrapes the web for facts/quotes from the last 48 hours. |
-| **Writer** | **Gemini 2.5 / Groq** | Drafts platform-specific content (Threads vs Posts). |
-| **Critic** | **Llama 3.3 (Groq)** | **The Gatekeeper.** Rejects low-quality drafts and forces rewrites. |
-| **Designer** | **Flux.1-Schnell** | Generates 16:9 cinematic cover art in <4 steps. |
-
 ---
 
-## 🚀 Live Demo
-
-**Try the Production Build on Hugging Face Spaces:**
+## 🚀 Installation
 
-[![Hugging Face Spaces](https://img.shields.io/badge/🤗%20Launch%20App-NewsAgent_Pro-yellow?style=for-the-badge&logo=huggingface)](https://huggingface.co/spaces/EATosin/NewsAgent-Pro)
+### Prerequisites
+*   Python 3.10+
+*   API Keys: Google Gemini, Tavily, Hugging Face Token.
 
-> *Try searching: "DeepSeek vs OpenAI" or "SpaceX Starship Launch"*
-
----
-
-## 📦 Installation (Local & Cloud)
-
-### 1. Clone & Setup
+### Local Setup
 ```bash
-git clone https://github.com/Eatosin/NewsAgent-Pro.git
+# 1. Clone the repository
+git clone https://github.com/eatosin/NewsAgent-Pro.git
 cd NewsAgent-Pro
-pip install -r requirements.txt
-```
 
-### 2. Configure Environment
-Create a `.env` file with your keys (Get Groq for free speed!):
-```env
-GROQ_API_KEY=gsk_...
-GEMINI_API_KEY=AIza...
-TAVILY_API_KEY=tvly-...
-HF_TOKEN=hf_...
-```
+# 2. Install dependencies
+pip install -r requirements.txt
 
-### 3. Run the App
-```bash
-streamlit run src/app.py
-```
+# 3. Create .env file
+echo "GEMINI_API_KEY=your_key" >> .env
+echo "TAVILY_API_KEY=your_key" >> .env
+echo "HF_TOKEN=your_key" >> .env
 
-### 4. Docker (Production)
-We use a custom multi-stage build to handle system dependencies (Fonts, Pillow):
-```bash
-docker build -t newsagent .
-docker run -p 7860:7860 --env-file .env newsagent
+# 4. Run the App
+streamlit run app.py
 ```
 
----
-
-## 📈 Star History
+### Docker Deployment
+The project includes a production-ready `Dockerfile` for deployment on Hugging Face Spaces or Render.
 
-[![Star History Chart](https://api.star-history.com/svg?repos=Eatosin/NewsAgent-Pro&type=Date)](https://star-history.com/#Eatosin/NewsAgent-Pro&Date)
+```dockerfile
+# Run on port 7860 (Hugging Face Default)
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0"]
+```
 
 ---
 
 ## 👨‍💻 Author
 **Owadokun Tosin Tobi**
-*Senior AI Engineer & Product Builder*
+*AI Product Engineer*
 
 *   **Portfolio:** [GitHub](https://github.com/eatosin)
-*   **Connect:** [LinkedIn](https://www.linkedin.com/in/owadokun-tosin-tobi/)
+*   **Connect:** [LinkedIn](https://www.linkedin.com/in/owadokun-tosin-tobi-6159091a3?utm_source=share&utm_campaign=share_via&utm_content=profile&utm_medium=android_app)
 
 ---
-*Built with the Lexpertz R&D Stack.*
+*Powered by the Lexpertz AI Engineering Stack.*

app.py

@@ -0,0 +1,214 @@
+import streamlit as st
+import os
+import io
+import requests
+import textwrap
+from dotenv import load_dotenv
+from tavily import TavilyClient
+import google.generativeai as genai
+from huggingface_hub import InferenceClient
+from PIL import Image, ImageDraw, ImageFont
+
+# Load environment variables
+load_dotenv()
+
+# Configuration
+PAGE_TITLE = "NewsAgent Pro"
+# SAVE TO /tmp TO FIX PERMISSION ERROR
+FONT_PATH = "/tmp/font.ttf" 
+FONT_URL = "https://github.com/google/fonts/raw/main/ofl/anton/Anton-Regular.ttf"
+IMAGE_MODEL = "black-forest-labs/FLUX.1-schnell"
+LLM_MODEL = "gemini-2.5-flash"
+
+st.set_page_config(page_title=PAGE_TITLE, layout="wide", page_icon="🗞️")
+
+# Initialize Clients
+try:
+    tavily = TavilyClient(api_key=os.getenv("TAVILY_API_KEY"))
+    genai.configure(api_key=os.getenv("GEMINI_API_KEY"))
+    editor_model = genai.GenerativeModel(LLM_MODEL)
+    image_client = InferenceClient(IMAGE_MODEL, token=os.getenv("HF_TOKEN"))
+except Exception as e:
+    st.error(f"Configuration Error: {e}")
+    st.stop()
+
+def get_font(size=80):
+    """
+    Loads a font from /tmp. Downloads if not present.
+    """
+    try:
+        return ImageFont.truetype(FONT_PATH, size)
+    except OSError:
+        try:
+            response = requests.get(FONT_URL)
+            if response.status_code == 200:
+                with open(FONT_PATH, "wb") as f:
+                    f.write(response.content)
+                return ImageFont.truetype(FONT_PATH, size)
+        except Exception as e:
+            print(f"Font download failed: {e}")
+        return ImageFont.load_default()
+
+def research_topic(topic):
+    """
+    Retrieves latest news context + sources.
+    Returns: (Context String, List of Source Links)
+    """
+    try:
+        search_result = tavily.search(query=topic, topic="news", days=2)
+        results = search_result.get('results', [])
+        
+        context = []
+        sources = []
+        
+        for res in results[:3]:
+            context.append(f"Title: {res['title']}\nSummary: {res['content']}")
+            sources.append(f"🔗 [{res['title']}]({res['url']})")
+            
+        return "\n\n".join(context), sources
+    except Exception as e:
+        return f"Research failed: {str(e)}", []
+
+def generate_content(platform, topic, research_data):
+    if "Twitter" in platform:
+        system_prompt = (
+            "You are a social media ghostwriter. Write a Twitter thread based on the research provided. "
+            "Split tweets using the delimiter '|||'. "
+            "Ensure the first tweet is a strong hook and the last is a call to action. "
+            "Keep each section under 280 characters."
+        )
+    else:
+        system_prompt = (
+            "You are a professional content strategist. Write a LinkedIn post based on the research provided. "
+            "Focus on business impact, strategic insights, and professional tone. "
+            "Use appropriate line breaks for readability."
+        )
+
+    user_prompt = f"""
+    TOPIC: {topic}
+    RESEARCH DATA: {research_data}
+    
+    SYSTEM INSTRUCTION: {system_prompt}
+    """
+    
+    try:
+        response = editor_model.generate_content(user_prompt)
+        return response.text
+    except Exception as e:
+        return f"Generation failed: {str(e)}"
+
+def generate_visual_asset(topic, platform):
+    prompt = (
+        f"Abstract 3D render representing {topic}, dark navy and black gradient background, "
+        "glass texture, soft studio lighting, minimalist, 8k resolution, negative space, "
+        "high definition, no text, no chaotic details"
+    )
+    
+    try:
+        image = image_client.text_to_image(prompt)
+        
+        draw = ImageDraw.Draw(image)
+        width, height = image.size
+        
+        font_size = 90
+        font = get_font(font_size)
+        
+        lines = textwrap.wrap(topic.upper(), width=15)
+        wrapped_text = "\n".join(lines)
+        
+        bbox = draw.textbbox((0, 0), wrapped_text, font=font)
+        text_height = bbox[3] - bbox[1]
+        
+        padding = 50
+        box_height = text_height + (padding * 3)
+        box_y = height - box_height - 100
+        
+        draw.rectangle(
+            [(0, box_y), (width, height)], 
+            fill=(0, 0, 0, 240)
+        )
+        
+        text_y = box_y + padding
+        draw.text((padding, text_y), wrapped_text, font=font, fill="white")
+        
+        small_font = get_font(30)
+        draw.text((padding, height - 60), f"GENERATED FOR {platform.upper()}", font=small_font, fill="#00ff00")
+        
+        return image, None
+    except Exception as e:
+        return None, str(e)
+
+def main():
+    st.title("NewsAgent Pro")
+    st.markdown("Autonomous Multi-Modal Content Engine")
+
+    with st.sidebar:
+        st.header("Configuration")
+        platform_choice = st.selectbox("Target Platform", ["Twitter (Thread)", "LinkedIn (Post)"])
+    
+    col1, col2 = st.columns([1, 1])
+
+    with col1:
+        st.subheader("Briefing")
+        topic_input = st.text_input("Topic", placeholder="Enter news topic or keyword...")
+        
+        if st.button("Generate Content", type="primary"):
+            if not topic_input:
+                st.warning("Please enter a topic.")
+                return
+
+            status = st.status("Initializing Agent Workflow...", expanded=True)
+            
+            # Step 1: Research
+            status.write("Agent: Researching topic...")
+            research_data, sources = research_topic(topic_input)
+            
+            # SHOW SOURCES (RESTORED)
+            if sources:
+                st.markdown("### 📚 Live Sources Found:")
+                for s in sources:
+                    st.markdown(s)
+            
+            # Step 2: Content Generation
+            status.write("Agent: Drafting copy...")
+            content_draft = generate_content(platform_choice, topic_input, research_data)
+            
+            # Step 3: Visual Generation
+            status.write("Agent: Designing assets...")
+            visual_asset, error = generate_visual_asset(topic_input, platform_choice)
+            
+            if error:
+                st.error(f"Image Gen Failed: {error}")
+            
+            status.update(label="Workflow Complete", state="complete")
+            
+            st.session_state['content'] = content_draft
+            st.session_state['image'] = visual_asset
+
+    with col2:
+        st.subheader("Production Output")
+        
+        if 'image' in st.session_state and st.session_state['image']:
+            st.image(st.session_state['image'], use_column_width=True, caption="Generated Asset")
+            
+            buf = io.BytesIO()
+            st.session_state['image'].save(buf, format="PNG")
+            st.download_button(
+                label="Download Image",
+                data=buf.getvalue(),
+                file_name="news_asset.png",
+                mime="image/png"
+            )
+
+        if 'content' in st.session_state:
+            raw_content = st.session_state['content']
+            
+            if "|||" in raw_content:
+                tweets = raw_content.split("|||")
+                for i, tweet in enumerate(tweets):
+                    st.text_area(f"Tweet {i+1}", value=tweet.strip(), height=120)
+            else:
+                st.text_area("Post Content", value=raw_content, height=400)
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -1,12 +1,9 @@
-streamlit>=1.40.0
-langgraph>=0.1.0
-langchain>=0.2.0
-langchain-groq
-langchain-google-genai
+streamlit
+google-generativeai
+langgraph
+langchain_core
 tavily-python
 huggingface_hub
 pillow
-pydantic
 python-dotenv
-langchain-community
-langchain-google-genai
+anthropic

src/__init__.py

@@ -1 +0,0 @@
-

src/agents/critic.py

@@ -1,60 +0,0 @@
-import json
-from langchain_core.messages import SystemMessage, HumanMessage
-from src.utils.config import get_llm
-from src.utils.prompt_loader import load_prompt
-from src.schema import HybridState, AgentState
-
-def critic_node(state: AgentState):
-    """
-    Critic Agent: Reviews content quality and enforces editorial standards.
-    Uses external YAML prompts for easy tuning.
-    """
-    # Initialize Hybrid Access (Safety Wrapper)
-    state_wrapper = HybridState(state)
-    
-    draft = state_wrapper.get("draft")
-    platform = state_wrapper.get("platform")
-    revision_count = state_wrapper.get("revision_count", 0)
-    
-    print(f"⚖️ Critic is reviewing draft (Revision {revision_count})...")
-    
-    system_prompt = load_prompt("critic.yaml")
-    
-    # Construct Context for the Critic
-    user_msg = f"""
-    TARGET PLATFORM: {platform}
-    
-    CURRENT DRAFT:
-    {draft}
-    
-    TASK: Score this content and provide specific feedback for improvement.
-    """
-    
-    messages = [
-        SystemMessage(content=system_prompt),
-        HumanMessage(content=user_msg)
-    ]
-    
-    # Use Groq (Planning Model) for fast scoring
-    llm = get_llm("planning") 
-    response = llm.invoke(messages)
-    
-    # Robust JSON Parsing (Handles Markdown blocks)
-    content = response.content.strip()
-    if "```json" in content:
-        content = content.split("```json")[1].split("```")[0]
-    
-    try:
-        data = json.loads(content)
-        return {
-            "score": data.get("score", 5),
-            "critique": data.get("feedback", "Improve clarity and engagement."),
-            "revision_count": revision_count + 1
-        }
-    except:
-        # Fail-safe if JSON breaks
-        return {
-            "score": 5, 
-            "critique": "Format error. Please review structure.", 
-            "revision_count": revision_count + 1
-        }

src/agents/designer.py

@@ -1,66 +0,0 @@
-import os
-import requests
-import io
-import textwrap
-from PIL import Image, ImageDraw, ImageFont
-from huggingface_hub import InferenceClient
-from src.schema import HybridState, AgentState
-
-# Initialize Flux Schnell (Faster, Apache 2.0)
-hf_token = os.getenv("HF_TOKEN")
-client = InferenceClient("black-forest-labs/FLUX.1-schnell", token=hf_token)
-
-def designer_node(state: AgentState):
-    state_wrapper = HybridState(state)
-    topic = state_wrapper.get("topic", "Breaking News")
-    platform = state_wrapper.get("platform", "twitter")
-    
-    prompt = f"Abstract 3D render of {topic}, dark gradient background, minimalist, high tech, 8k resolution, no text"
-    
-    try:
-        # Generate Image
-        image = client.text_to_image(prompt)
-        
-        # Overlay Text
-        draw = ImageDraw.Draw(image)
-        width, height = image.size
-        
-        # Font Fallback
-        try:
-            font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf", 90)
-        except:
-            # Download font if missing (Docker environment)
-            font_url = "https://github.com/google/fonts/raw/main/ofl/anton/Anton-Regular.ttf"
-            r = requests.get(font_url)
-            font = ImageFont.truetype(io.BytesIO(r.content), 90)
-
-        # Wrap Text
-        lines = textwrap.wrap(topic.upper(), width=15)
-        text = "\n".join(lines)
-        
-        # Draw Box
-        bbox = draw.textbbox((0, 0), text, font=font)
-        text_height = bbox[3] - bbox[1]
-        
-        box_y = height - text_height - 150
-        draw.rectangle([(0, box_y), (width, height)], fill=(0, 0, 0, 240))
-        
-        # Draw Text
-        draw.text((50, box_y + 50), text, font=font, fill="white")
-        
-        # Save to buffer
-        img_byte_arr = io.BytesIO()
-        image.save(img_byte_arr, format='PNG')
-        img_byte_arr = img_byte_arr.getvalue()
-        
-        # Convert to simple path or bytes for UI
-        # For Streamlit state, bytes are fine, but saving to /tmp is safer for passing
-        save_path = "/tmp/generated_image.png"
-        with open(save_path, "wb") as f:
-            f.write(img_byte_arr)
-            
-        return {"image_url": save_path}
-        
-    except Exception as e:
-        print(f"Design failed: {e}")
-        return {"image_url": None}

src/agents/planner.py

@@ -1,42 +0,0 @@
-import json
-from langchain_core.messages import SystemMessage, HumanMessage
-from src.utils.config import get_llm
-from src.utils.prompt_loader import load_prompt
-from src.schema import HybridState, AgentState
-
-def planner_node(state: AgentState):
-    state_wrapper = HybridState(state)
-    topic = state_wrapper.get("topic")
-    
-    # Load your existing SOTA prompt
-    system_prompt = load_prompt("planner.yaml")
-    
-    messages = [
-        SystemMessage(content=system_prompt),
-        HumanMessage(content=f"Topic: {topic}")
-    ]
-    
-    # Use Groq for speed
-    llm = get_llm("planning")
-    response = llm.invoke(messages)
-    
-    # Robust JSON parsing
-    content = response.content.strip()
-    if "```json" in content:
-        content = content.split("```json")[1].split("```")[0]
-    
-    try:
-        data = json.loads(content)
-        outline = data.get("outline", "")
-        # Ensure outline is string
-        if isinstance(outline, list):
-            outline = "\n".join(outline)
-            
-        return {
-            "hook": data.get("hook"),
-            "outline": outline,
-            "cta": data.get("cta")
-        }
-    except:
-        # Fallback if JSON fails
-        return {"outline": content, "hook": f"News: {topic}"}

src/agents/researcher.py

@@ -1,20 +0,0 @@
-from src.tools.research import perform_research
-from src.schema import HybridState, AgentState
-
-def researcher_node(state: AgentState):
-    """
-    Executes the research tool and stores the results.
-    """
-    # Wrap state for safe access
-    state_wrapper = HybridState(state)
-    topic = state_wrapper.get("topic")
-    
-    print(f"🕵️‍♂️ Researching: {topic}")
-    
-    # Call the Tavily Tool
-    research_results = perform_research.invoke(topic)
-    
-    # Return updates to the state
-    return {
-        "research_data": research_results
-    }

src/agents/writer.py

@@ -1,56 +0,0 @@
-from langchain_core.messages import SystemMessage, HumanMessage
-from src.utils.config import get_llm
-from src.utils.prompt_loader import load_prompt
-from src.schema import HybridState, AgentState
-
-def writer_node(state: AgentState):
-    state_wrapper = HybridState(state)
-    platform = state_wrapper.get("platform", "twitter")
-    research = state_wrapper.get("research_data")
-    outline = state_wrapper.get("outline")
-    critique = state_wrapper.get("critique") # Check for feedback
-    current_draft = state_wrapper.get("draft")
-    
-    # Select prompt based on platform
-    prompt_file = "writer_twitter.yaml" if "twitter" in platform.lower() else "writer_linkedin.yaml"
-    system_prompt = load_prompt(prompt_file)
-    
-    # Dynamic User Message: First Draft vs Revision
-    if critique and current_draft:
-        print("✍️ Writer is revising based on feedback...")
-        user_msg = f"""
-        ORIGINAL DRAFT:
-        {current_draft}
-        
-        CRITIQUE TO FIX:
-        {critique}
-        
-        TASK: Rewrite the draft to address the critique. Keep the same format.
-        """
-    else:
-        print("✍️ Writer is drafting fresh content...")
-        user_msg = f"""
-        Research: {research}
-        Outline: {outline}
-        """
-    
-    messages = [
-        SystemMessage(content=system_prompt),
-        HumanMessage(content=user_msg)
-    ]
-    
-    llm = get_llm("writing")
-    response = llm.invoke(messages)
-    draft = response.content.strip()
-    
-    final_thread = []
-    if "twitter" in platform.lower():
-        final_thread = [t.strip() for t in draft.split("|||") if t.strip()]
-    else:
-        final_thread = [draft]
-        
-    return {
-        "draft": draft,
-        "final_thread": final_thread,
-        "revision_count": state.revision_count  # Persist count
-    }

src/app.py

@@ -1,151 +0,0 @@
-import streamlit as st
-import base64
-import os
-import time  # <-- This was missing!
-from io import BytesIO
-from src.main import graph
-from src.schema import AgentState
-# --- PAGE CONFIG ---
-st.set_page_config(
-    page_title="NewsAgent Pro v2", 
-    page_icon="🗞️", 
-    layout="wide",
-    initial_sidebar_state="expanded"
-)
-
-# --- CUSTOM CSS ---
-st.markdown("""
-<style>
-    .stButton>button {width: 100%; border-radius: 8px; font-weight: bold;}
-    .reportview-container {margin-top: -2em;}
-    h1 {color: #FF4B4B;}
-</style>
-""", unsafe_allow_html=True)
-
-# --- SIDEBAR ---
-with st.sidebar:
-    st.title("🤖 Agent Command")
-    st.info("System Online v2.0")
-    st.markdown("### ⚙️ Engine Specs")
-    st.markdown("- **Planner:** Llama 3.3 (Groq)")
-    st.markdown("- **Writer:** Gemini 2.5 / Groq")
-    st.markdown("- **Visuals:** Flux.1 Schnell")
-    
-    st.markdown("---")
-    st.write("Authored by **Lexpertz R&D**")
-
-# --- MAIN INTERFACE ---
-st.title("🗞️ NewsAgent Pro")
-st.markdown("### Autonomous Multi-Modal Content Engine")
-st.caption("Enter a topic. The AI swarm will Research, Plan, Write, and Design assets automatically.")
-
-# Input Section
-with st.container():
-    col_input, col_btn = st.columns([3, 1])
-    with col_input:
-        topic = st.text_input("Mission Objective (Topic)", placeholder="e.g. DeepSeek vs OpenAI rivalry")
-    with col_btn:
-        platform = st.selectbox("Target Platform", ["Twitter", "LinkedIn"])
-        run_btn = st.button("🚀 Launch Agents", type="primary")
-
-# --- SESSION STATE INITIALIZATION ---
-if "generated_content" not in st.session_state:
-    st.session_state.generated_content = None
-if "generated_image" not in st.session_state:
-    st.session_state.generated_image = None
-if "sources" not in st.session_state:
-    st.session_state.sources = []
-
-# --- EXECUTION LOGIC ---
-if run_btn and topic:
-    # Reset State
-    st.session_state.generated_content = None
-    st.session_state.generated_image = None
-    
-    status_box = st.status("🚀 Initializing Agent Swarm...", expanded=True)
-    
-    try:
-        # Initialize Pydantic State
-        initial_state = AgentState(
-            topic=topic,
-            platform=platform.lower()
-        )
-        
-        # Run Graph
-        curr_state = initial_state
-        
-        # We iterate through the stream updates
-        for event in graph.stream(initial_state):
-            for node_name, values in event.items():
-                # Skip empty updates
-                if not values:
-                    continue
-                
-                # Update status based on active agent
-                if node_name == "planner":
-                    status_box.write("🧠 **Planner:** Strategy & Hook defined.")
-                elif node_name == "researcher":
-                    status_box.write(f"🕵️‍♂️ **Researcher:** Gathered data.")
-                elif node_name == "writer":
-                    status_box.write("✍️ **Writer:** Draft generated.")
-                elif node_name == "designer":
-                    status_box.write("🎨 **Designer:** Visual asset rendered.")
-
-                # Update local state dict to track progress
-                # Note: LangGraph returns the *changes*, so we update our tracker
-                # For simplicity in this UI loop, we grab final artifacts at the end
-                if "final_thread" in values:
-                    st.session_state.generated_content = values["final_thread"]
-                if "image_url" in values:
-                    st.session_state.generated_image = values["image_url"]
-                if "research_data" in values:
-                    # Extract sources for display
-                    # Assuming research_data is a string in the final state or list of dicts
-                    # Adjust based on your researcher.py output
-                    pass 
-
-        status_box.update(label="✅ Mission Accomplished", state="complete", expanded=False)
-
-    except Exception as e:
-        status_box.update(label="❌ Mission Failed", state="error")
-        st.error(f"Agent Logic Error: {str(e)}")
-
-# --- RESULTS DISPLAY ---
-if st.session_state.generated_content or st.session_state.generated_image:
-    st.divider()
-    res_col1, res_col2 = st.columns([1, 1])
-
-    # LEFT: Visuals
-    with res_col1:
-        st.subheader("🎨 Visual Asset")
-        if st.session_state.generated_image:
-            img_path = st.session_state.generated_image
-            if os.path.exists(img_path):
-                st.image(img_path, caption="Viral Cover Image", use_container_width=True)
-                
-                # Download Button
-                with open(img_path, "rb") as file:
-                    btn = st.download_button(
-                        label="⬇️ Download PNG",
-                        data=file,
-                        file_name=f"newsagent_{int(time.time())}.png",
-                        mime="image/png"
-                    )
-            else:
-                st.warning("Image file missing (Docker ephemeral storage).")
-        else:
-            st.info("No visual generated for this run.")
-
-    # RIGHT: Copy
-    with res_col2:
-        st.subheader(f"📝 {platform} Draft")
-        content = st.session_state.generated_content
-        
-        if content:
-            if isinstance(content, list): # Twitter Thread
-                for i, tweet in enumerate(content):
-                    st.text_area(f"Tweet {i+1}", value=tweet, height=120)
-            else: # LinkedIn Post
-                st.text_area("Post Content", value=content, height=400)
-        else:
-            st.info("No text content generated.")

src/main.py

@@ -1,60 +0,0 @@
-from langgraph.graph import StateGraph, END
-from src.schema import AgentState, HybridState
-
-# Import Agents
-from src.agents.planner import planner_node
-from src.agents.researcher import researcher_node
-from src.agents.writer import writer_node
-from src.agents.designer import designer_node
-from src.agents.critic import critic_node
-
-# --- CONDITIONAL LOGIC ---
-def should_continue(state: AgentState):
-    """
-    Decides: Go back to Writer? Or move to Designer?
-    """
-    wrapper = HybridState(state)
-    score = wrapper.get("score", 0)
-    revisions = wrapper.get("revision_count", 0)
-    
-    # Rule: If score < 8 AND we haven't tried too many times...
-    if score < 8 and revisions < 2:
-        print(f"🔄 Quality Check Failed (Score: {score}/10). Revising...")
-        return "writer"
-    
-    if score >= 8:
-        print(f"✅ Quality Check Passed (Score: {score}/10).")
-    else:
-        print("⚠️ Max revisions reached. Moving to publishing.")
-        
-    return "designer"
-
-# --- BUILD GRAPH ---
-workflow = StateGraph(AgentState)
-
-workflow.add_node("planner", planner_node)
-workflow.add_node("researcher", researcher_node)
-workflow.add_node("writer", writer_node)
-workflow.add_node("critic", critic_node)
-workflow.add_node("designer", designer_node)
-
-# Linear flow start
-workflow.set_entry_point("planner")
-workflow.add_edge("planner", "researcher")
-workflow.add_edge("researcher", "writer")
-
-# The Loop: Writer -> Critic -> (Router)
-workflow.add_edge("writer", "critic")
-
-workflow.add_conditional_edges(
-    "critic",
-    should_continue,
-    {
-        "writer": "writer",      # Loop back
-        "designer": "designer"   # Move forward
-    }
-)
-
-workflow.add_edge("designer", END)
-
-graph = workflow.compile()

src/prompts/critic.yaml

@@ -1,50 +0,0 @@
-system: |
-  You are a ruthless, world-class viral content editor with 20+ years experience at top media outlets and growth agencies. Your job is to elevate good drafts into 100k+ impression threads/posts by being brutally honest.
-
-  Evaluation Criteria (score each 1–10, then average for overall):
-  1. Hook Strength: Does the opener grab attention instantly? (Shocking stat/question/claim)
-  2. Depth & Insight: Does it go beyond surface-level? Unique angles, data, implications?
-  3. Engagement Flow: Curiosity gaps, pacing, emotional arc, readability (line breaks, bullets)?
-  4. Accuracy & Credibility: Faithful to research/sources? No hallucinations?
-  5. Platform Fit: Perfect tone/length/style for Twitter (conversational, punchy) or LinkedIn (professional, value-driven)?
-  6. Virality Potential: CTA strength, shareability, controversy without toxicity?
-
-  Process:
-  - Think step-by-step: Score each criterion with justification.
-  - Overall score: Weighted average (Hook x2, Engagement x2, rest x1).
-  - If overall ≥8.5: Approve with minor polish suggestions.
-  - If <8.5: Mandatory full revised draft incorporating fixes.
-
-  Output STRICTLY JSON only:
-  {
-    "reasoning": "Step-by-step critique",
-    "scores": {"hook": int, "depth": int, ...},
-    "overall_score": float,
-    "feedback": "Specific, actionable improvements",
-    "approved": boolean,
-    "revised_draft": "Full new draft if not approved, else null"
-  }
-
-  Few-Shot Examples:
-
-  Example 1 (Weak Draft Critique):
-  Draft: "AI is advancing quickly. Here are some updates..."
-  Output: {
-    "reasoning": "Hook is generic - no grab. Depth shallow. Flow boring...",
-    "scores": {"hook": 3, "depth": 5, ...},
-    "overall_score": 4.8,
-    "feedback": "Rewrite hook with stat like 'AI just surpassed humans in X'. Add implications...",
-    "approved": false,
-    "revised_draft": "New full thread here|||..."
-  }
-
-  Example 2 (Strong Draft Approval):
-  Draft: "The Venezuela oil crisis just escalated... (thread with deep geo insights)"
-  Output: {
-    "reasoning": "Hook strong with controversy. Depth excellent with data...",
-    "scores": {"hook": 9, "depth": 10, ...},
-    "overall_score": 9.2,
-    "feedback": "Minor: Add one more question CTA",
-    "approved": true,
-    "revised_draft": null
-  }

src/prompts/planner.yaml

@@ -1,68 +0,0 @@
-system: |
-  You are an elite content strategist and outline architect for viral social media in 2026. You specialize in turning raw news/topics into high-engagement thread/post structures that get 10k–100k+ impressions.
-
-  Core Principles:
-  - Hook First: Always start with a grabber (shocking stat, bold claim, question, controversy, or personal angle).
-  - Narrative Arc: Build curiosity → deliver value → create emotional peak → end with strong CTA.
-  - Platform Adaptation:
-    - Twitter/X: 8–15 points, numbered/sequenced, punchy, curiosity gaps between tweets.
-    - LinkedIn: 5–10 sections, bullet-heavy, professional insights, thought leadership tone.
-  - Virality: Include emotional triggers, actionable takeaways, questions to audience.
-  - Length: Balanced for attention span.
-
-  Input: Topic + platform + (later) research.
-
-  Output STRICTLY JSON only:
-  {
-    "hook": "Full hook text (first tweet/post paragraph)",
-    "sections": ["Section 1 title/description", "Section 2...", ...],
-    "cta": "Strong closing call-to-action",
-    "estimated_length": "e.g., 12 tweets" or "1200 chars"
-  }
-
-  Few-Shot Examples:
-
-  Example 1 (Twitter - Venezuela Oil News):
-  Input Topic: Venezuela political/oil developments
-  Output: {
-    "hook": "The Venezuela plot just thickened—and it could reshape global oil markets overnight. (thread 🧵)",
-    "sections": [
-      "1. Venezuela holds 303B barrels—more than Saudi—but production crashed from 3.3M to <1M bpd",
-      "2. Why? Sanctions + heavy crude challenges",
-      "3. US refineries now crave heavy crude (70% of imports)",
-      "4. The real play: Control reserves for supply flood",
-      "5. Implications for commodities, crypto, stocks"
-    ],
-    "cta": "This will move markets hard. Follow for real-time updates—what's your take?",
-    "estimated_length": "10 tweets"
-  }
-
-  Example 2 (LinkedIn - AI Agents Trend):
-  Input Topic: Rise of multi-agent AI systems
-  Output: {
-    "hook": "AI agents aren't coming—they're already here, quietly transforming how teams build and ship.",
-    "sections": [
-      "• The shift: From single models to orchestrated agents (LangGraph, CrewAI)",
-      "• Real wins: 40–60% faster prototyping with hybrid routing",
-      "• Risks: Hallucinations without critique loops",
-      "• 2026 outlook: Every company will have internal agent tools",
-      "• How leaders should prepare now"
-    ],
-    "cta": "Are you experimenting with agents yet? Share your biggest win or concern in comments 👇 #AI #Leadership",
-    "estimated_length": "1500 characters"
-  }
-
-  Example 3 (Twitter - Useful Gadgets List):
-  Input Topic: Random useful everyday items
-  Output: {
-    "hook": "Random stuff that's actually USEFUL in 2026 (save this thread 🧵)",
-    "sections": [
-      "1. Magnetic cable organizers that end desk chaos",
-      "2. Portable SSD with built-in encryption",
-      "3. Smart water bottle that tracks hydration + glows reminders",
-      "4. Noise-cancelling earbuds under $50 that rival AirPods",
-      "5. Multi-tool pen for EDC fans"
-    ],
-    "cta": "Which one are you buying first? Reply below!",
-    "estimated_length": "8 tweets"
-  }

src/prompts/researcher.yaml

@@ -1,44 +0,0 @@
-system: |
-  You are a world-class investigative researcher and synthesis expert in 2026. You turn raw news/topics into deep, balanced, LLM-ready research briefs that power viral threads/posts.
-
-  Process:
-  1. Plan 3–5 targeted search queries (neutral, opposing views, data/stats, implications).
-  2. Think step-by-step: What are the core claims? Controversies? Stakeholders?
-  3. Synthesize: Extract key facts, timelines, quotes, stats. Note contradictions.
-  4. Balance: Include multiple perspectives. Flag potential biases (media slant, official statements).
-  5. Credibility: Prioritize reputable sources. List URLs.
-
-  Output STRICTLY JSON only:
-  {
-    "summary": "Concise 300–600 word overview with narrative flow",
-    "key_facts": ["Bullet 1: Fact + source", "Bullet 2: ..."],
-    "sources": ["Title - URL", ...],
-    "controversies": ["Point 1", ...],
-    "implications": "Forward-looking impacts for markets/people/tech",
-    "suggested_hook_ideas": ["Hook idea 1", "Hook idea 2"]
-  }
-
-  You will be provided search results from Tavily tool calls.
-
-  Few-Shot Examples:
-
-  Example 1 (Venezuela Oil Topic):
-  Search Results: [Various snippets on reserves, sanctions, US refineries]
-  Output: {
-    "summary": "Venezuela holds world's largest proven reserves (303B barrels, mostly heavy crude)... production collapse due to sanctions/infrastructure... US dependency shifted to heavy imports... recent political events could unlock supply...",
-    "key_facts": [
-      "303B barrels reserves - more than Saudi (OPEC data)",
-      "Production: 3.3M bpd (2000s) → <1M today (EIA)",
-      "US heavy crude imports: 70% today vs 20% in 1980 (EIA)"
-    ],
-    "sources": [
-      "OPEC Annual Report - https://opec.org/...",
-      "EIA Venezuela Analysis - https://eia.gov/..."
-    ],
-    "controversies": ["Sanctions effectiveness vs regime corruption blame", "Environmental concerns with heavy crude"],
-    "implications": "Potential supply flood → lower oil prices, commodity volatility, crypto/energy stock moves",
-    "suggested_hook_ideas": ["Venezuela controls more oil than Saudi—but can't sell it", "Why the US quietly needs Venezuela's 'dirty' oil"]
-  }
-
-  Example 2 (AI Agents Trend):
-  Search Results: [Groq release, LangGraph updates, adoption stats]

src/prompts/writer_linkedin.yaml

@@ -1,66 +0,0 @@
-system: |
-  You are an elite LinkedIn thought leader ghostwriter in 2026. Your posts get 10k–100k+ reactions by delivering deep value, professional insights, and subtle calls to engage.
-
-  Core Rules:
-  - Hook: Professional but bold — question, stat, personal reflection, or trend observation.
-  - Style: Authoritative yet approachable. Longer paragraphs ok. Use bullets/lists for scannability. Bold key phrases. No excessive emojis (1–2 max).
-  - Length: 800–2000 characters (single post) or structured as carousel slides if noted.
-  - Value: Actionable insights, implications for professionals/business, forward-looking.
-  - Virality: End with open question or "What do you think?" to drive comments.
-  - Accuracy: Stick rigidly to research/sources.
-  - Format: Clean markdown-ready text (no separators needed).
-
-  Use full research, sources, and outline.
-
-  Output ONLY the post text.
-
-  Few-Shot Examples:
-
-  Example 1 (News/Geopolitics Insight):
-  Venezuela's political developments this weekend aren't just headlines—they're a masterclass in global energy dynamics.
-
-  With 303 billion barrels of proven reserves (mostly heavy crude), Venezuela sits on more oil than Saudi Arabia. Yet production has cratered from 3.3M barrels/day in the 2000s to under 1M today.
-
-  Why this matters for energy markets and professionals:
-
-  • US refineries in Texas/Louisiana are optimized for heavy crude—70% of imports now heavy vs 20% in 1980.
-  • Sanctions + infrastructure decay created the vacuum.
-  • Potential policy shifts could flood markets with supply, impacting prices and commodities.
-
-  The real question: How will this reshape global trade, inflation, and investment strategies in 2026?
-
-  What are your thoughts on the energy implications? Share below 👇
-
-  #Energy #Geopolitics #Commodities
-
-  Example 2 (Tech Trend Breakdown):
-  AI agents are no longer hype—they're quietly transforming how we work.
-
-  Last week alone:
-  • Groq released blazing-fast inference that rivals paid tiers for free.
-  • Multi-agent frameworks like LangGraph hit production maturity.
-
-  For leaders and builders:
-  - Cut development time 40–60% with agentic workflows.
-  - Risk: Over-reliance without critique loops leads to errors.
-  - Opportunity: Build internal tools that give teams superpowers.
-
-  I've been experimenting with hybrid Groq/Gemini agents—results are game-changing.
-
-  Are you integrating agents yet? What's your biggest win or concern?
-
-  #AI #Leadership #Productivity
-
-  Example 3 (Career/Insight List):
-  7 harsh truths I learned after 10 years in tech (that no one tells you):
-
-  1. Your code matters less than your communication.
-  2. Politics exists everywhere—learn to navigate it early.
-  3. Promotions go to those who ship impact, not hours.
-  ...
-
-  The sooner you internalize these, the faster you grow.
-
-  Which one resonates most? Add #8 in comments.
-
-  #CareerAdvice #Tech

src/prompts/writer_twitter.yaml

@@ -1,80 +0,0 @@
-system: |
-  You are a world-class viral X/Twitter ghostwriter in 2026 style. Your threads get 10k–100k+ likes by combining deep insights, bold hooks, and perfect pacing.
-
-  Core Rules:
-  - Start with a killer hook: Shocking claim, question, stat, or controversy.
-  - Style: Conversational but authoritative. Use line breaks for readability. Emojis sparingly (1-2 max per thread). Bold key phrases with * or CAPS.
-  - Length: 8–15 tweets, each <280 characters.
-  - Virality techniques: Numbered or sequenced points, curiosity gaps ("But here's why this matters..."), build narrative arc, end with strong CTA (question, follow invite, or thought-provoker).
-  - Accuracy: Base strictly on provided research/sources. No hallucinations.
-  - Format: Split tweets exactly with ||| (nothing else as separator).
-  - Output ONLY the thread text—no extras, no JSON.
-
-  Use the full research, sources, and outline provided.
-
-  Few-Shot Examples (study these for structure and tone):
-
-  Example 1 (News Explanatory Thread - Hook + Facts + Geopolitics + CTA):
-  The Venezuela plot thickens:
-
-  While Venezuela holds 303 BILLION barrels of oil reserves, much of this is HEAVY crude oil.
-
-  Texas and Louisiana also *happen* to have 6 of the LARGEST HEAVY crude oil refineries in the world.
-
-  What does this mean? Let us explain.
-
-  (a thread)|||In the early 2000s, Venezuela was a MUCH larger oil producer than the US.
-
-  In fact, Venezuela produced 3 TIMES as much oil, at nearly 3.3 million barrels per day.
-
-  By 2020, Venezuela's production had declined to just 900K/day, while the US hit 5 million/day.
-
-  This is key.|||First, Venezuela has been heavily sanctioned by the US for years.
-
-  This resulted in old infrastructure, hindering the ability to extract HEAVY crude oil.
-
-  Heavy oil is far more expensive to extract than light crude.
-
-  This requires advanced techniques like steam injection.|||The US has become incredibly dependent on heavy crude oil.
-
-  In 1980, just 10-20% of US crude oil imports were heavy.
-
-  Today, the MAJORITY are heavy, at ~70%.
-
-  The US wants more heavy crude and Venezuela has BILLIONS of barrels of it.|||Currently, Venezuela holds more oil reserves than any other country.
-
-  They even hold 20% more than Saudi Arabia.
-
-  What's the "best" way to restore these imports?
-
-  Take control of the country's oil reserves.|||This weekend's events in Venezuela will have major effects on the global economy.
-
-  Stocks, commodities, bonds, and crypto will move.
-
-  Follow @KobeissiLetter for real-time analysis as this develops.
-
-  Example 2 (List-Style Engagement Thread - Hook + Numbered Items):
-  Offensively UGLY dolls of celebrities (thread)
-
-  1. Emma Watson as Belle|||2. Marilyn Monroe by Tristar|||3. Beyoncé by Hasbro|||4. Princess Diana by Street Players|||5. Ashley Tisdale by Huckleberry|||6. Spice Girls by Toymax (Sporty & Baby)|||7. Fran (The Nanny) by Street Players|||8. Britney Spears by Yaboom|||9. TLC by Yaboom|||10. B*Witched by Yaboom|||11. Kylie Minogue by Jakks Pacific|||12. Hilary Duff by Dakin & Playmates
-
-  (Which one shocked you most? Reply below 👇)
-
-  Example 3 (Practical Useful List Thread - Hook + Items + Links):
-  random stuff that actually useful
-
-  a thread|||baona pouch 🖤
-
-  https://s.shopee.co.id/xxx|||squidward tray
-
-  https://s.shopee.co.id/xxx|||box acrylic
-
-  https://s.shopee.co.id/xxx|||storge srbaguna tempel
-
-  https://s.shopee.co.id/xxx|||cermin plus storage 🍑
-
-  https://s.shopee.co.id/xxx|||box penyimpanan bntuk buku
-
-  https://s.shopee.co.id/xxx
-
-  (Save this thread for later 🧵)

src/schema.py

@@ -1,57 +0,0 @@
-from pydantic import BaseModel, Field, ConfigDict
-from typing import List, Dict, Optional, Any
-
-class AgentState(BaseModel):
-    """
-    The shared memory for the Multi-Agent System.
-    Includes 'extra=allow' to prevent validation errors on new fields.
-    """
-    model_config = ConfigDict(extra='allow')
-
-    # Input
-    topic: str = ""
-    platform: str = "twitter"
-    
-    # Research & Planning
-    research_data: Optional[Any] = None
-    outline: Optional[str] = None
-    hook: Optional[str] = None
-    
-    # Content
-    draft: Optional[str] = None
-    critique: Optional[str] = None
-    score: float = 0.0
-    revision_count: int = 0
-    
-    # Output
-    final_thread: Optional[List[str]] = Field(default_factory=list)
-    image_url: Optional[str] = None
-    sources: Optional[List[str]] = Field(default_factory=list)
-
-# 🛠️ UTILITY WRAPPER (Paste this here so it's available everywhere)
-class HybridState:
-    """
-    Universal wrapper to allow both dot-notation (state.topic) 
-    and dict-access (state['topic']).
-    """
-    def __init__(self, state):
-        # Unwrap if it's already a HybridState
-        if isinstance(state, HybridState):
-            self.__dict__ = state.__dict__
-        # Convert Pydantic to dict
-        elif hasattr(state, 'model_dump'):
-            self.__dict__.update(state.model_dump())
-        # Use dict directly
-        elif isinstance(state, dict):
-            self.__dict__.update(state)
-        else:
-            raise ValueError(f"Unknown state type: {type(state)}")
-
-    def get(self, key, default=None):
-        return self.__dict__.get(key, default)
-    
-    def __getitem__(self, key):
-        return self.__dict__.get(key)
-    
-    def __setitem__(self, key, value):
-        self.__dict__[key] = value

src/tools/research.py

@@ -1,27 +0,0 @@
-import os
-from tavily import TavilyClient
-from langchain_core.tools import tool
-
-@tool
-def perform_research(topic: str):
-    """
-    Searches the web for recent news using Tavily.
-    """
-    api_key = os.getenv("TAVILY_API_KEY")
-    if not api_key:
-        return "Error: TAVILY_API_KEY not found."
-        
-    client = TavilyClient(api_key=api_key)
-    
-    try:
-        # Search specifically for news
-        response = client.search(query=topic, topic="news", days=2)
-        results = response.get("results", [])
-        
-        context = []
-        for r in results[:4]:
-            context.append(f"Title: {r['title']}\nURL: {r['url']}\nSummary: {r['content']}")
-            
-        return "\n\n".join(context)
-    except Exception as e:
-        return f"Search failed: {str(e)}"

src/utils/config.py

@@ -1,36 +0,0 @@
-import os
-from dotenv import load_dotenv
-from langchain_groq import ChatGroq
-from langchain_google_genai import ChatGoogleGenerativeAI
-
-load_dotenv()
-
-def get_llm(task_type: str = "general"):
-    """
-    Factory to get the best LLM for the job.
-    - Planning/Logic -> Groq (Llama 3.3) for speed.
-    - Writing/Context -> Groq or Gemini Fallback.
-    """
-    groq_key = os.getenv("GROQ_API_KEY")
-    gemini_key = os.getenv("GEMINI_API_KEY")
-
-    # PRIMARY: Groq (Llama 3.3 70B)
-    if groq_key:
-        try:
-            return ChatGroq(
-                temperature=0.7,
-                model_name="llama-3.3-70b-versatile",
-                api_key=groq_key
-            )
-        except Exception as e:
-            print(f"⚠️ Groq failed: {e}. Falling back...")
-
-    # FALLBACK: Gemini
-    if gemini_key:
-        return ChatGoogleGenerativeAI(
-            model="gemini-1.5-flash",
-            google_api_key=gemini_key,
-            temperature=0.7
-        )
-    
-    raise ValueError("❌ No API Keys found! Please set GROQ_API_KEY or GEMINI_API_KEY.")

src/utils/prompt_loader.py

@@ -1,14 +0,0 @@
-import os
-
-def load_prompt(filename):
-    """Reads a prompt file from src/prompts."""
-    current_dir = os.path.dirname(__file__)
-    prompts_dir = os.path.join(os.path.dirname(current_dir), 'prompts')
-    file_path = os.path.join(prompts_dir, filename)
-    
-    try:
-        with open(file_path, 'r', encoding='utf-8') as f:
-            return f.read()
-    except FileNotFoundError:
-        # Fallback if file is missing
-        return f"You are a helpful assistant. Task: {filename}"