upgrade

.dockerignore

@@ -0,0 +1,26 @@
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.Python
+env/
+venv/
+ENV/
+.env
+.venv
+.git
+.gitignore
+.DS_Store
+*.swp
+*.swo
+.vscode/
+.idea/
+*.log
+.cache/
+.coverage
+htmlcov/
+*.egg-info/
+build/
+dist/
+*.egg
+*.mod

.env.example

@@ -0,0 +1,10 @@
+# Hugging Face Token (required for model access)
+HF_TOKEN=your_huggingface_token_here
+
+# Modal Configuration (optional, for local development)
+# MODAL_TOKEN_ID=your_modal_token_id
+# MODAL_TOKEN_SECRET=your_modal_token_secret
+
+# Cache Configuration (optional)
+# HUGGINGFACE_HUB_CACHE=./cache
+# TRANSFORMERS_CACHE=./cache

.gitignore

@@ -0,0 +1,64 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Virtual Environment
+venv/
+env/
+ENV/
+.env
+.venv
+
+# IDE
+.idea/
+.vscode/
+*.swp
+*.swo
+*~
+
+# Logs
+*.log
+
+# Local development
+.DS_Store
+.env
+.cache/
+
+# Model files
+*.bin
+*.gguf
+*.safetensors
+
+# Hugging Face
+huggingface/
+
+# Modal
+.modal/
+
+# System Files
+Thumbs.db
+ehthumbs.db
+Desktop.ini
+$RECYCLE.BIN/
+
+# Project specific
+images/
+outputs/

Dockerfile

@@ -0,0 +1,29 @@
+FROM python:3.10-slim
+
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    git \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install Python dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the application code
+COPY . .
+
+# Set environment variables
+ENV PYTHONUNBUFFERED=1
+ENV HUGGINGFACE_HUB_CACHE=/app/cache
+ENV TRANSFORMERS_CACHE=/app/cache
+
+# Create cache directory
+RUN mkdir -p /app/cache
+
+# Expose the port the app runs on
+EXPOSE 7860
+
+# Command to run the application
+CMD ["python", "app.py"]

README.md

@@ -17,7 +17,119 @@ tags:
 - gradio
 ---
 
-# 🎵 AI Content Generator for Musicians 🎬
+# 🚀 Content Creation Agent
+
+An AI-powered content generation tool that creates engaging social media content for multiple platforms using advanced language models and image generation.
+
+[![Try the App](https://img.shields.io/badge/🚀-Try%20Live%20Demo-blue?style=for-the-badge)](https://huggingface.co/spaces/developerjeremylive/ContentCreationAgent-etheroi)
+
+## ✨ Features
+
+- 🤖 AI-powered content generation for LinkedIn, Instagram, TikTok, Twitter, and Facebook
+- 🖼️ AI image generation using Stable Diffusion XL
+- 🎯 Platform-optimized content tailored to each social network
+- ⚡ Fast local templates as fallback
+- 🎨 Customizable content based on business type and target audience
+
+## 🛠️ Setup
+
+### Prerequisites
+
+- Python 3.10 or higher
+- [Hugging Face Account](https://huggingface.co/join)
+- [Modal Account](https://modal.com/)
+- [Git](https://git-scm.com/)
+
+### Installation
+
+1. Clone the repository:
+   ```bash
+   git clone https://huggingface.co/spaces/developerjeremylive/ContentCreationAgent-etheroi
+   cd ContentCreationAgent-etheroi
+   ```
+
+2. Create and activate a virtual environment:
+   ```bash
+   python -m venv venv
+   source venv/bin/activate  # On Windows: .\venv\Scripts\activate
+   ```
+
+3. Install dependencies:
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+## 🔑 Environment Variables
+
+Create a `.env` file in the project root with the following variables:
+
+```
+# Required for Hugging Face Hub
+HF_TOKEN=your_huggingface_token_here
+
+# Optional: Modal configuration (for local development)
+MODAL_TOKEN_ID=your_modal_token_id
+MODAL_TOKEN_SECRET=your_modal_token_secret
+```
+
+## 🚀 Deployment
+
+### Local Development
+
+1. Make sure you have all the environment variables set in your `.env` file
+2. Run the application:
+   ```bash
+   python app.py
+   ```
+3. Open your browser to `http://localhost:7860`
+
+### Docker
+
+1. Build the Docker image:
+   ```bash
+   docker build -t content-creation-agent .
+   ```
+
+2. Run the container:
+   ```bash
+   docker run -p 7860:7860 --env-file .env content-creation-agent
+   ```
+
+### Hugging Face Spaces
+
+1. Push your code to a GitHub repository
+2. Create a new Space on Hugging Face
+3. Configure the following settings:
+   - Select "Docker" as the Space SDK
+   - Set the Dockerfile path to `Dockerfile`
+   - Add your `HF_TOKEN` as a secret in the Space settings
+4. Deploy your Space
+
+## 🤖 Technical Architecture
+
+### Core Stack
+- **Frontend**: Gradio for interactive UI
+- **Backend**: Python with Modal for serverless GPU inference
+- **AI Models**:
+  - Text Generation: Llama 2 7B Chat (GGUF)
+  - Image Generation: Stable Diffusion XL
+- **Deployment**: Docker container on Hugging Face Spaces
+
+## 🤝 Contributing
+
+Contributions are welcome! Please feel free to submit a Pull Request.
+
+## 📄 License
+
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+
+## 🙏 Acknowledgments
+
+- [Hugging Face](https://huggingface.co/) for the Transformers library and model hosting
+- [Modal](https://modal.com/) for serverless GPU inference
+- [Gradio](https://gradio.app/) for the beautiful UI components
+
+## 🎵 AI Content Generator for Musicians
 
 **An autonomous AI agent that transforms song lyrics into complete social media marketing campaigns across 6 platforms using Llama-2-7B and Modal Labs GPU infrastructure.**
 

app.py

@@ -8,7 +8,7 @@ logger = logging.getLogger(__name__)
 
 # Modal setup with proper secret handling
 MODAL_AVAILABLE = False
-generate_content_with_llm = None
+generate_content = None
 
 # Define the app name - must match your Modal app name in modal_backend.py
 MODAL_APP_NAME = "content-creation-agent"
@@ -18,22 +18,27 @@ try:
     from modal.exception import AuthError
     logger.info(f"Modal version: {modal.__version__}")
     
-    # Check for Modal tokens in environment
-    modal_token_id = os.environ.get("MODAL_TOKEN_ID")
-    modal_token_secret = os.environ.get("MODAL_TOKEN_SECRET")
+    # Check for Hugging Face token in environment
+    hf_token = os.environ.get("HF_TOKEN")
     
-    if not (modal_token_id and modal_token_secret):
-        logger.warning("⚠️ Modal tokens not found in environment variables. Running in local mode.")
-        logger.info("ℹ️ To enable Modal, set MODAL_TOKEN_ID and MODAL_TOKEN_SECRET in your environment")
+    if not hf_token:
+        logger.warning("⚠️ HF_TOKEN not found in environment variables. Running in local mode.")
+        logger.info("ℹ️ To enable AI features, set HF_TOKEN in your environment variables")
     else:
         try:
-            logger.info("🔍 Initializing Modal client...")
-            logger.info(f"Token ID: {modal_token_id[:4]}...{modal_token_id[-4:] if modal_token_id else ''}")
+            logger.info("🔍 Initializing Modal client with Hugging Face token...")
             
-            # Initialize the client with the token
+            # Initialize the client with the Hugging Face token
             try:
-                client = modal.Client(token_id=modal_token_id, token_secret=modal_token_secret)
-                logger.info("✅ Successfully authenticated with Modal")
+                # Create a secret with the Hugging Face token
+                hf_secret = modal.Secret.from_dict({"HUGGING_FACE_HUB_TOKEN": hf_token})
+                
+                # Initialize the client
+                client = modal.Client()
+                logger.info("✅ Successfully authenticated with Hugging Face")
+                
+                # Store the secret for later use
+                client.secret = hf_secret
                 
                 # List all functions to debug
                 logger.info("🔍 Looking for available functions...")
@@ -54,13 +59,14 @@ try:
                     
                     # If health check passes, get the main function
                     try:
-                        generate_content_with_llm = modal.Function.lookup(MODAL_APP_NAME, "generate_content_with_llm")
+                        generate_content = modal.Function.lookup(MODAL_APP_NAME, "generate_content")
+                        generate_image = modal.Function.lookup(MODAL_APP_NAME, "generate_image")
                         MODAL_AVAILABLE = True
                         logger.info("🚀 Successfully connected to Modal AI service")
                     except Exception as e:
-                        logger.error(f"❌ Could not find generate_content_with_llm function: {str(e)}")
+                        logger.error(f"❌ Could not find generate_content or generate_image function: {str(e)}")
                         MODAL_AVAILABLE = False
-                    
+                        
                 except Exception as e:
                     logger.error(f"❌ Health check failed: {str(e)}")
                     logger.info(f"ℹ️ Make sure your app name is correct: {MODAL_APP_NAME}")
@@ -213,69 +219,71 @@ As {brand_display}, we're excited to share our latest insights on {business_type
 {hashtags['facebook']}"""
     }    
 
-def generate_all_content(business_type, target_audience, content_goal, brand_name, key_message, use_modal):
-    """Generate content with Modal toggle"""
+def generate_all_content(business_type, target_audience, content_goal, brand_name, key_message, use_modal, generate_image_flag=False):
+    """Generate content for all platforms using Modal AI or local fallback"""
     if not all([business_type.strip(), target_audience.strip(), content_goal.strip(), key_message.strip()]):
-        error_msg = "❌ Please fill in all required fields"
-        return [""] * 5 + [error_msg]
+        return [""] * 5 + ["❌ Please fill in all required fields"] + [None]  # Add None for image
     
     start_time = time.time()
+    empty_outputs = [""] * 5
+    image_output = None
     
-    # Initial yield - empty outputs and loading status
-    empty_outputs = [""] * 5  # 5 platform outputs
-    if use_modal and MODAL_AVAILABLE and generate_content_with_llm is not None:
+    if use_modal and MODAL_AVAILABLE and generate_content is not None:
         status_msg = "⏳ Generating content with AI..."
     else:
         status_msg = "⏳ Generating content with local templates..."
-        use_modal = False  # Force local generation if Modal is not available
+        use_modal = False
     
-    logger.info(status_msg)
-    yield empty_outputs + [status_msg]  # This will be 6 values total (5 empty strings + 1 status message)
+    yield empty_outputs + [status_msg, None]  # Add None for image
     
     try:
-        if use_modal and MODAL_AVAILABLE and generate_content_with_llm is not None:
+        if use_modal and MODAL_AVAILABLE and generate_content is not None:
             try:
-                logger.info("🔄 Calling Modal AI...")
-                result = generate_content_with_llm.remote(
+                # Call the Modal function with image generation flag
+                result = generate_content.remote(
                     business_type, 
                     target_audience, 
                     content_goal, 
                     brand_name, 
-                    key_message
+                    key_message,
+                    generate_images=generate_image_flag
                 )
                 
-                # Validate and process the result
+                # Check if we got a valid response
                 if isinstance(result, dict) and all(platform in result for platform in ['linkedin', 'instagram', 'tiktok', 'twitter', 'facebook']):
                     content = result
                     status_msg = f"✅ Generated with AI in {time.time() - start_time:.2f}s"
-                    logger.info("Successfully generated content with Modal AI")
+                    
+                    # Check for generated image
+                    if generate_image_flag and 'image_url' in result:
+                        image_output = result['image_url']
+                        status_msg += " (with image)"
                 else:
                     raise ValueError("Unexpected response format from Modal AI")
                     
-            except Exception as modal_error:
-                logger.warning(f"⚠️ AI generation failed: {str(modal_error)}")
+            except Exception as e:
+                logger.error(f"Error generating content with Modal: {str(e)}")
                 content = generate_fallback_content(business_type, target_audience, content_goal, brand_name, key_message)
                 status_msg = f"⚠️ AI generation failed, using local templates (took {time.time() - start_time:.2f}s)"
         else:
-            # Use local generation
             content = generate_fallback_content(business_type, target_audience, content_goal, brand_name, key_message)
             status_msg = f"✅ Generated with local templates in {time.time() - start_time:.2f}s"
-    
+            
     except Exception as e:
-        logger.error(f"❌ Unexpected error during content generation: {str(e)}")
+        logger.error(f"Unexpected error: {str(e)}")
         content = generate_fallback_content(business_type, target_audience, content_goal, brand_name, key_message)
         status_msg = f"❌ Error occurred, using local templates (took {time.time() - start_time:.2f}s)"
     
-    # Prepare platform outputs
-    platforms = ['linkedin', 'instagram', 'tiktok', 'twitter', 'facebook']
-    platform_outputs = [content.get(platform, f"Could not generate {platform} content") for platform in platforms]
+    # Prepare the outputs for all platforms
+    platform_outputs = [
+        content.get('linkedin', "Could not generate LinkedIn content"),
+        content.get('instagram', "Could not generate Instagram content"),
+        content.get('tiktok', "Could not generate TikTok content"),
+        content.get('twitter', "Could not generate Twitter content"),
+        content.get('facebook', "Could not generate Facebook content")
+    ]
     
-    # Log completion
-    logger.info(f"Content generation completed in {time.time() - start_time:.2f}s")
-    
-    # Return only the platform outputs (5 values)
-    # The status message will be shown separately in the Gradio interface
-    return platform_outputs + [status_msg]  # This returns 6 values (5 platform outputs + 1 status message)
+    return platform_outputs + [status_msg, image_output]
 
 # Create Gradio interface
 with gr.Blocks(theme=gr.themes.Soft(), title="🚀 Content Creator Pro") as demo:
@@ -335,6 +343,14 @@ with gr.Blocks(theme=gr.themes.Soft(), title="🚀 Content Creator Pro") as demo
                 info="Choose between AI-powered generation or local templates"
             )
             
+            # Image generation toggle (only visible when AI generation is selected)
+            with gr.Group(visible=MODAL_AVAILABLE) as image_group:
+                generate_image_toggle = gr.Checkbox(
+                    label="🖼️ Generate AI Image",
+                    value=False,
+                    info="Check to generate an AI image based on your content"
+                )
+            
     run_button = gr.Button("🚀 Generate Content", variant="primary", size="lg")
     
     gr.Markdown("## 📱 Generated Social Media Content")
@@ -350,11 +366,55 @@ with gr.Blocks(theme=gr.themes.Soft(), title="🚀 Content Creator Pro") as demo
     
     status = gr.Markdown("Ready to generate content!")
     
+    # Image output component
+    with gr.Row():
+        generated_image = gr.Image(
+            label="🖼️ Generated Image",
+            visible=False,
+            height=512
+        )
+    
+    # Update image group visibility based on modal toggle
+    def update_image_group_visibility(use_modal):
+        return gr.Group(visible=use_modal and MODAL_AVAILABLE)
+    
+    modal_toggle.change(
+        fn=update_image_group_visibility,
+        inputs=[modal_toggle],
+        outputs=[image_group]
+    )
+    
     # Connect the function with proper outputs
     run_button.click(
         fn=generate_all_content,
-        inputs=[business_type, target_audience, content_goal, brand_name, key_message, modal_toggle],
-        outputs=[linkedin_output, instagram_output, tiktok_output, twitter_output, facebook_output, status]
+        inputs=[
+            business_type, 
+            target_audience, 
+            content_goal, 
+            brand_name, 
+            key_message, 
+            modal_toggle,
+            generate_image_toggle
+        ],
+        outputs=[
+            linkedin_output, 
+            instagram_output, 
+            tiktok_output, 
+            twitter_output, 
+            facebook_output, 
+            status,
+            generated_image
+        ]
+    )
+    
+    # Show/hide image based on whether an image was generated
+    def update_image_visibility(image_data):
+        return gr.Image(visible=image_data is not None), image_data or None
+    
+    generated_image.change(
+        fn=lambda x: (gr.Image(visible=x is not None), x or None),
+        inputs=[generated_image],
+        outputs=[generated_image, generated_image]
     )
     
     # Add examples

modal_backend.py

@@ -1,22 +1,37 @@
 import os
+import io
 import modal
-from typing import Dict
+import requests
+from typing import Dict, Optional
+from PIL import Image
 
 # Define the Modal app
 app = modal.App("content-creation-agent")  # Note: on a single line :D
 
 # Define the Docker image with necessary dependencies
-llama_image = (
+image = (
     modal.Image.debian_slim(python_version="3.11")
     .pip_install(
-        "llama-cpp-python==0.2.90",
+        "torch",
+        "transformers",
+        "diffusers",
+        "accelerate",
+        "safetensors",
+        "pillow",
         "huggingface_hub",
         "requests",
     )
-    # Remove any existing HUGGINGFACE_HUB environment variable
-    .env({"HUGGINGFACE_HUB_CACHE": "/root/cache/huggingface/hub"})
+    .env({
+        "HUGGINGFACE_HUB_CACHE": "/root/cache/huggingface/hub",
+        "TRANSFORMERS_CACHE": "/root/cache/huggingface/transformers",
+        "HF_HUB_DISABLE_PROGRESS_BARS": "1"
+    })
 )
 
+# Model configuration
+MODEL_NAME = "stabilityai/stable-diffusion-xl-base-1.0"
+CACHE_DIR = "/root/cache/huggingface/hub"
+
 # Constants
 MODEL_DIR = "/model"
 MODEL_NAME = "TheBloke/Llama-2-7B-Chat-GGUF"
@@ -128,17 +143,117 @@ def generate_content_with_llm(business_type: str, target_audience: str, content_
         # Return empty strings for all platforms if there's an error
         return {platform: f"Error generating content for {platform}" for platform in platforms}
 
+# Image generation function
+@app.function(
+    image=image,
+    gpu="A10G",
+    secrets=[modal.Secret.from_name("huggingface-secret")],
+    timeout=600,
+    keep_warm=1
+)
+def generate_image(prompt: str, negative_prompt: str = "", num_images: int = 1) -> list:
+    """Generate images using Stable Diffusion XL"""
+    from diffusers import StableDiffusionXLPipeline
+    import torch
+    
+    try:
+        # Load the model
+        pipe = StableDiffusionXLPipeline.from_pretrained(
+            MODEL_NAME,
+            torch_dtype=torch.float16,
+            use_safetensors=True,
+            variant="fp16",
+            cache_dir=CACHE_DIR
+        )
+        
+        # Move to GPU if available
+        if torch.cuda.is_available():
+            pipe = pipe.to("cuda")
+        
+        # Generate images
+        images = pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            num_images_per_prompt=num_images,
+            num_inference_steps=30,
+            guidance_scale=7.5,
+        ).images
+        
+        # Convert PIL images to base64
+        import base64
+        from io import BytesIO
+        
+        result = []
+        for img in images:
+            buffered = BytesIO()
+            img.save(buffered, format="PNG")
+            img_str = base64.b64encode(buffered.getvalue()).decode()
+            result.append(f"data:image/png;base64,{img_str}")
+            
+        return result
+        
+    except Exception as e:
+        return [f"Error generating image: {str(e)}"]
+
 # Health check endpoint
 @app.function()
 def health_check() -> Dict[str, str]:
     return {"status": "ok", "message": "Service is healthy"}
 
+# Combined function to generate both text and images
+@app.function(
+    image=image,
+    gpu="A10G",
+    secrets=[modal.Secret.from_name("huggingface-secret")],
+    timeout=1800
+)
+def generate_content(
+    business_type: str,
+    target_audience: str,
+    content_goal: str,
+    brand_name: str,
+    key_message: str,
+    generate_images: bool = False
+) -> Dict[str, any]:
+    """
+    Generate social media content and optionally images
+    """
+    try:
+        # First generate text content
+        content = generate_content_with_llm.remote(
+            business_type, target_audience, content_goal, brand_name, key_message
+        )
+        
+        # If image generation is requested
+        if generate_images:
+            # Generate a prompt for the image based on the content
+            image_prompt = (
+                f"{content_goal} for {target_audience}. {key_message}. "
+                f"Business type: {business_type}. Professional, high-quality image."
+            )
+            
+            # Generate the image
+            image_urls = generate_image.remote(
+                prompt=image_prompt,
+                negative_prompt="low quality, blurry, text, watermark, logo",
+                num_images=1
+            )
+            
+            # Add image URL to the content
+            if image_urls and not image_urls[0].startswith("Error"):
+                content["image_url"] = image_urls[0]
+        
+        return content
+        
+    except Exception as e:
+        return {"error": f"Failed to generate content: {str(e)}"}
+
 # Local testing (only runs when script is executed directly)
 if __name__ == "__main__":
     # Test health check
     print("Health check:", health_check.remote())
     
-    # Example usage
-    test_prompt = "Generate a short story about a robot learning to paint."
-    result = generate_content_with_llm.remote(test_prompt)
-    print("Generated content:", result.get("generated_text", "No content generated"))
+    # Test image generation
+    print("Testing image generation...")
+    test_image = generate_image.remote("A futuristic city at night, cyberpunk style")
+    print(f"Generated image: {test_image[0][:100]}..." if test_image else "Failed to generate image")

requirements.txt

@@ -1,5 +1,17 @@
 modal>=1.0.0
-gradio
-transformers
-torch
+gradio>=3.36.0
+python-dotenv
+requests
+Pillow>=9.0.0
+modal>=0.57.1
+huggingface-hub>=0.16.4
+transformers>=4.36.0
+diffusers>=0.24.0
+accelerate>=0.25.0
+safetensors>=0.4.0
+torch>=2.0.0
+torchvision
+torchaudio
+sentencepiece
+protobuf<=3.20.0
 hf_transfer