Delete app.py

app.py

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-"""
-🚧 PROJECT STATUS DISCLAIMER 🚧
-
-This agentic comic generator project demonstrates a multi-agent workflow architecture
-but is NOT fully functional due to several technical limitations:
-
-KNOWN LIMITATIONS:
-1. 🔴 OpenAI API Rate Limits: Severe restrictions on GPT-4 usage (3 requests/min)
-2. 🔴 Modal .remote() Integration: Gradio doesn't support Modal's serverless functions properly
-3. 🔴 Deployment Constraints: Full pipeline requires Modal infrastructure not available in this demo
-
-WHAT WORKS:
-✅ Individual agent components (Brown & Bayko) function correctly
-✅ Agent-to-agent communication protocols are implemented
-✅ LlamaIndex workflow architecture is properly structured
-✅ UI components and chat interface work as intended
-✅ Tool calling and response handling mechanisms are functional
-
-WHAT'S INCOMPLETE:
-❌ End-to-end comic generation pipeline
-❌ Image generation via Modal serverless functions
-❌ Full multi-iteration refinement workflow
-❌ Production-ready error handling and recovery
-
-This represents a proof-of-concept implementation showcasing the architectural
-approach for multi-agent comic generation. The individual components communicate
-effectively, but the complete workflow requires infrastructure beyond the current
-deployment environment's capabilities.
-
-For a fully functional version, deploy with proper Modal credentials and
-infrastructure setup as outlined in the technical specifications.
-"""
-
-import gradio as gr
-import time
-import json
-import os
-import sys
-import io
-import logging
-import threading
-import queue
-from contextlib import redirect_stdout, redirect_stderr
-from agents.brown_workflow import create_brown_workflow
-from pathlib import Path
-
-
-# Initialize workflow
-workflow = create_brown_workflow(
-    max_iterations=3,
-    openai_api_key=os.getenv("OPENAI_API_KEY"),
-)
-
-
-def colorize_message(message, msg_type="default"):
-    """Add color coding to messages based on type"""
-    colors = {
-        "agent_brown": "#2E86AB",  # Blue for Agent Brown
-        "agent_bayko": "#A23B72",  # Purple for Agent Bayko
-        "tool_output": "#F18F01",  # Orange for tool outputs
-        "error": "#C73E1D",  # Red for errors
-        "success": "#2D5016",  # Green for success
-        "log": "#6C757D",  # Gray for logs
-        "analysis": "#8E44AD",  # Purple for analysis
-        "default": "#212529",  # Dark gray default
-    }
-
-    color = colors.get(msg_type, colors["default"])
-    return f'<span style="color: {color}; font-weight: bold;">{message}</span>'
-
-
-class LogCapture(io.StringIO):
-    """Capture all print statements and logs for Gradio display"""
-
-    def __init__(self, log_queue):
-        super().__init__()
-        self.log_queue = log_queue
-        self.original_stdout = sys.stdout
-
-    def write(self, text):
-        if text.strip():  # Only capture non-empty lines
-            self.log_queue.put(text.strip())
-        # Also write to original stdout so terminal still shows logs
-        self.original_stdout.write(text)
-        return len(text)
-
-    def flush(self):
-        self.original_stdout.flush()
-
-
-def comic_generator(prompt, style_preference, verbose=True):
-    """Verbose comic generation: stream all agent/tool messages and reasoning"""
-    chat = []
-    tool_calls = []
-    images = []
-    progress = 0
-
-    # Create log queue and capture
-    log_queue = queue.Queue()
-    log_capture = LogCapture(log_queue)
-
-    try:
-        # Start message
-        chat.append(
-            (
-                colorize_message(
-                    "🧐 Agent Brown: Starting comic generation...",
-                    "agent_brown",
-                ),
-            )
-        )
-        progress += 5
-        yield chat, images
-
-        # Start workflow in a separate thread to capture logs in real-time
-        def run_workflow():
-            with redirect_stdout(log_capture):
-                return workflow.process_comic_request(
-                    f"{prompt} Style preference: {style_preference}"
-                )
-
-        # Run workflow in thread
-        workflow_thread = threading.Thread(
-            target=lambda: setattr(run_workflow, "result", run_workflow())
-        )
-        workflow_thread.start()
-
-        # Stream logs while workflow is running
-        while workflow_thread.is_alive():
-            try:
-                # Get logs from queue with timeout
-                log_message = log_queue.get(timeout=0.1)
-                chat.append((colorize_message(f"📝 {log_message}", "log"),))
-                progress = min(progress + 2, 90)
-                yield chat, images
-            except queue.Empty:
-                continue
-
-        # Wait for thread to complete
-        workflow_thread.join()
-
-        # Get any remaining logs
-        while not log_queue.empty():
-            try:
-                log_message = log_queue.get_nowait()
-                chat.append((colorize_message(f"📝 {log_message}", "log"),))
-                yield chat, images
-            except queue.Empty:
-                break
-
-        # Get the result
-        result = getattr(run_workflow, "result", None)
-        if not result:
-            chat.append(("❌ No result from workflow",))
-            yield chat, images
-            return
-
-        response_data = (
-            json.loads(result) if isinstance(result, str) else result
-        )
-
-        # Show all tool outputs and chat history if available
-        tool_outputs = response_data.get("tool_outputs", [])
-        for tool_output in tool_outputs:
-            # Try to pretty-print tool output JSON if possible
-            try:
-                tool_json = json.loads(tool_output)
-                tool_msg = json.dumps(tool_json, indent=2)
-            except Exception:
-                tool_msg = str(tool_output)
-            chat.append(
-                (
-                    colorize_message(
-                        f"🛠️ Tool Output:\n{tool_msg}", "tool_output"
-                    ),
-                )
-            )
-            tool_calls.append("tool_call")
-            progress = min(progress + 10, 95)
-            yield chat, images
-
-        # Show error if any
-        if "error" in response_data:
-            chat.append(
-                (
-                    colorize_message(
-                        f"❌ Error: {response_data['error']}", "error"
-                    ),
-                )
-            )
-            progress = 100
-            yield chat, images
-            return
-
-        # Show Bayko's panel generation
-        if "bayko_response" in response_data:
-            bayko_data = response_data["bayko_response"]
-            panels = bayko_data.get("panels", [])
-            progress_per_panel = 50 / max(len(panels), 1)
-            for i, panel in enumerate(panels, 1):
-                chat.append(
-                    (
-                        colorize_message(
-                            f"🧸 Agent Bayko: Panel {i}: {panel.get('caption', '')}",
-                            "agent_bayko",
-                        ),
-                    )
-                )
-                tool_calls.append("generate_panel_content")
-                # Show image if available
-                if "image_path" in panel:
-                    img_path = Path(panel["image_path"])
-                    if img_path.exists():
-                        images.append(str(img_path.absolute()))
-                elif "image_url" in panel:
-                    images.append(panel["image_url"])
-                progress += progress_per_panel
-                yield chat, images
-                time.sleep(0.2)
-
-        # Show Brown's analysis and decision
-        if "analysis" in response_data:
-            chat.append(
-                (
-                    colorize_message(
-                        f"🧐 Agent Brown Analysis: {response_data['analysis']}",
-                        "analysis",
-                    ),
-                )
-            )
-            tool_calls.append("analyze_bayko_output")
-            progress = min(progress + 10, 99)
-            yield chat, images
-
-        # Final decision
-        if "decision" in response_data:
-            decision = response_data["decision"]
-            if decision == "APPROVE":
-                chat.append(
-                    (
-                        "✅ Agent Brown: Comic approved! All quality checks passed.",
-                    )
-                )
-            elif decision == "REFINE":
-                chat.append(
-                    (
-                        "🔄 Agent Brown: Comic needs refinement. Starting another iteration...",
-                    )
-                )
-            else:
-                chat.append(
-                    ("❌ Agent Brown: Comic rejected. Starting over...",)
-                )
-            tool_calls.append("final_decision")
-            progress = 100
-            yield chat, images
-
-        # If verbose, show the full response_data for debugging
-        if verbose:
-            chat.append(
-                (
-                    f"[DEBUG] Full response: {json.dumps(response_data, indent=2)}",
-                )
-            )
-            yield chat, images
-
-    except Exception as e:
-        chat.append(
-            (
-                colorize_message(
-                    f"❌ Error during generation: {str(e)}", "error"
-                ),
-            )
-        )
-        progress = 100
-        yield chat, images
-
-
-def set_api_key(api_key):
-    """Set the OpenAI API key as environment variable"""
-    if not api_key or not api_key.strip():
-        return colorize_message("❌ Please enter a valid API key", "error")
-
-    if not api_key.startswith("sk-"):
-        return colorize_message(
-            "❌ Invalid API key format (should start with 'sk-')", "error"
-        )
-
-    # Set the environment variable
-    os.environ["OPENAI_API_KEY"] = api_key.strip()
-
-    # Update the global workflow with the new key
-    global workflow
-    workflow = create_brown_workflow(
-        max_iterations=3,
-        openai_api_key=api_key.strip(),
-    )
-
-    return colorize_message("✅ API key set successfully!", "success")
-
-
-# Gradio UI
-with gr.Blocks() as demo:
-    gr.Markdown(
-        """
-        # 🚧 PROJECT STATUS DISCLAIMER 🚧
-        
-        **This agentic comic generator is a PROOF-OF-CONCEPT demonstration with known limitations:**
-        
-        ### 🔴 Known Issues:
-        - **OpenAI Rate Limits:** Severe restrictions (3 requests/min for GPT-4)
-        - **Modal Integration:** Gradio doesn't support Modal .remote() functions properly
-        - **Infrastructure:** Full pipeline requires Modal serverless deployment
-        
-        ### ✅ What Works:
-        - Individual agent components (Brown & Bayko) function correctly
-        - Agent-to-agent communication protocols are implemented
-        - UI components and workflow architecture are functional
-        
-        ### ❌ What's Incomplete:
-        - End-to-end comic generation pipeline
-        - Image generation via Modal serverless functions
-        - Full multi-iteration refinement workflow
-        
-        **This demonstrates the architectural approach but is NOT a complete working system.**
-        """
-    )
-
-    gr.Markdown(
-        """
-        ⚠️ **Additional Warning:** This demo is subject to OpenAI's strict rate limits (3 requests/min for gpt-4o). You may experience long waits (20+ seconds) between steps. If you see a rate limit error, please wait and try again, or use your own OpenAI API key with higher limits.
-        """
-    )
-
-    gr.Markdown(
-        """
-        # 🦙 Multi-Agent Comic Generator
-        Enter a story prompt and let Brown & Bayko create a comic!
-        Watch as the agents collaborate using LlamaIndex workflow and GPT-4V vision capabilities.
-        """
-    )
-
-    with gr.Row():
-        openai_key_box = gr.Textbox(
-            label="Enter your OpenAI API Key (optional)",
-            placeholder="sk-...",
-            type="password",
-            scale=4,
-        )
-        set_key_button = gr.Button("Set API Key 🔑", scale=1)
-        key_status = gr.Textbox(
-            label="Status",
-            value="No API key set",
-            interactive=False,
-            scale=2,
-        )
-
-    with gr.Row():
-        user_input = gr.Textbox(
-            label="Enter your comic prompt",
-            placeholder="A moody K-pop idol finds a puppy on the street...",
-            scale=4,
-        )
-        style_dropdown = gr.Dropdown(
-            ["Studio Ghibli", "Noir", "Manga", "Pixel Art"],
-            label="Art Style",
-            value="Studio Ghibli",
-        )
-        submit_button = gr.Button("Generate Comic 🎨")
-
-    with gr.Row():
-        chat_window = gr.Chatbot(
-            label="Agent Conversation",
-            bubble_full_width=False,
-            show_copy_button=True,
-            height=350,
-        )
-
-    with gr.Row():
-        image_gallery = gr.Gallery(
-            label="Comic Panels",
-            columns=2,
-            rows=2,
-            height=400,
-            object_fit="contain",
-        )
-        feedback = gr.Radio(
-            ["👍 Love it!", "👎 Try again"],
-            label="How's the comic?",
-            value=None,
-        )
-
-    def stream_comic(openai_key, prompt, style):
-        # Use user-provided OpenAI key if given, else fallback to env
-        key = openai_key or os.getenv("OPENAI_API_KEY")
-
-        # Set the API key as environment variable if provided
-        if openai_key:
-            os.environ["OPENAI_API_KEY"] = openai_key
-
-        # Re-create the workflow with the user key
-        workflow = create_brown_workflow(
-            max_iterations=3,
-            openai_api_key=key,
-        )
-        for chat, images in comic_generator(prompt, style, verbose=True):
-            chat_display = []
-            for msg in chat:
-                if isinstance(msg, tuple) and len(msg) >= 1:
-                    # If it's a single-element tuple, make it a proper chat message
-                    if len(msg) == 1:
-                        chat_display.append((msg[0], ""))
-                    else:
-                        chat_display.append(
-                            (msg[0], msg[1] if len(msg) > 1 else "")
-                        )
-                else:
-                    # Handle string messages
-                    chat_display.append((str(msg), ""))
-            yield chat_display, images
-
-    submit_button.click(
-        stream_comic,
-        inputs=[openai_key_box, user_input, style_dropdown],
-        outputs=[chat_window, image_gallery],
-    )
-
-    set_key_button.click(
-        set_api_key,
-        inputs=[openai_key_box],
-        outputs=[key_status],
-    )
-
-    gr.Markdown(
-        """
-        ---
-        <center>
-        <b>Built with 🦙 LlamaIndex, Modal Labs, MistralAI and Gradio for the Hugging Face Hackathon!</b>
-        <br>Using GPT-4 for intelligent comic analysis
-        </center>
-        """
-    )
-
-    gr.Markdown(
-        """
-        **🚀 Want the real version?**
-        1. Clone this Space
-        2. Set up Modal credentials 
-        3. Deploy with actual Modal functions
-        4. Enjoy serverless AI magic!
-        
-        *This demo shows the UI and MCP structure without Modal execution.*
-        """
-    )
-
-# Launch the app
-if __name__ == "__main__":
-    demo.launch()