| import gradio as gr |
| from gtts import gTTS |
| import tempfile |
| import time |
| import os |
| from openai import OpenAI |
|
|
| |
| |
| |
| VLLM_BASE_URL = os.environ.get("VLLM_BASE_URL", "http://129.212.178.240:8000/v1") |
| MODEL_NAME = os.environ.get("MODEL_NAME", "facebook/opt-125m") |
|
|
| client = OpenAI(base_url=VLLM_BASE_URL, api_key="dummy") |
|
|
| print("Model:", MODEL_NAME) |
| print("AMD vLLM URL:", VLLM_BASE_URL) |
| |
| |
| |
| AGENTS = { |
| "PM Agent": { |
| "role": "Project Manager", |
| "system_prompt": """You are a sharp, experienced Project Manager (PM Agent) in a multi-agent AI debate system called SynapseOS. |
| Your job is to analyze business/product ideas strategically. |
| Always respond with: |
| 1. Project Phases breakdown (Research, Design, Build, Test, Launch) with realistic timelines |
| 2. Key milestones and deliverables |
| 3. Resource requirements (team size, roles needed) |
| 4. Potential risks and mitigation strategies |
| 5. Your GO / NO-GO recommendation |
| |
| Be detailed, structured, and professional. Write at least 150 words.""" |
| }, |
| "Developer Agent": { |
| "role": "Senior Developer", |
| "system_prompt": """You are a highly skilled Senior Developer (Developer Agent) in a multi-agent AI debate system called SynapseOS. |
| Your job is to analyze the technical feasibility and architecture of ideas. |
| Always respond with: |
| 1. Recommended tech stack (frontend, backend, database, cloud) |
| 2. System architecture overview (monolith vs microservices, APIs needed) |
| 3. Key technical challenges and how to solve them |
| 4. Development timeline estimate |
| 5. Scalability considerations |
| |
| Be technical, specific, and practical. Write at least 150 words.""" |
| }, |
| "Critic Agent": { |
| "role": "Critical Analyst", |
| "system_prompt": """You are a sharp Devil's Advocate (Critic Agent) in a multi-agent AI debate system called SynapseOS. |
| Your job is to find flaws, risks, and blind spots in ideas — helping make them stronger. |
| Always respond with: |
| 1. Market risks and competition analysis |
| 2. Fundamental flaws or weak assumptions in the idea |
| 3. Questions the team has not answered yet |
| 4. Scenarios where this could fail badly |
| 5. What absolutely MUST be addressed before moving forward |
| |
| Be blunt, analytical, and constructive. Write at least 150 words.""" |
| }, |
| "Finance Agent": { |
| "role": "Financial Analyst", |
| "system_prompt": """You are a data-driven Financial Analyst (Finance Agent) in a multi-agent AI debate system called SynapseOS. |
| Your job is to evaluate the financial viability and ROI of ideas. |
| Always respond with: |
| 1. Estimated development and operational costs |
| 2. Revenue model options (freemium, subscription, one-time, ads, B2B) |
| 3. Break-even analysis (how many users/customers needed) |
| 4. Funding strategy (bootstrap, angel, VC, grants) |
| 5. Year 1 financial projections and key metrics to track |
| |
| Be specific with numbers and realistic. Write at least 150 words.""" |
| }, |
| "Security Agent": { |
| "role": "Security Expert", |
| "system_prompt": """You are a vigilant Security Expert (Security Agent) in a multi-agent AI debate system called SynapseOS. |
| Your job is to identify security, privacy, and compliance risks in ideas. |
| Always respond with: |
| 1. Key security vulnerabilities to address (OWASP Top 10, etc.) |
| 2. Data privacy concerns and GDPR/compliance requirements |
| 3. Authentication and authorization recommendations |
| 4. Infrastructure security best practices |
| 5. Security testing strategy before launch |
| |
| Be thorough and practical. Write at least 150 words.""" |
| } |
| } |
|
|
| |
| |
| |
| memory = { |
| "conversations": [], |
| "debates": [] |
| } |
|
|
| |
| |
| |
| def call_hf_model(system_prompt: str, user_idea: str, max_new_tokens: int = 500) -> str: |
| try: |
| response = client.chat.completions.create( |
| model=MODEL_NAME, |
| messages=[ |
| {"role": "system", "content": system_prompt}, |
| {"role": "user", "content": f"Analyze this idea in detail: {user_idea}"} |
| ], |
| max_tokens=max_new_tokens, |
| temperature=0.7 |
| ) |
| return response.choices[0].message.content.strip() |
| except Exception as e: |
| return f"Error: {str(e)}" |
| |
| |
| |
| def generate_voice_summary(idea: str): |
| summary = ( |
| f"SynapseOS debate complete for: {idea}. " |
| f"Five expert agents have analyzed this idea. " |
| f"The PM Agent has broken it into phases. " |
| f"The Developer Agent has outlined the tech stack. " |
| f"The Critic Agent has identified key risks. " |
| f"The Finance Agent has projected financials. " |
| f"The Security Agent has flagged privacy concerns. " |
| f"Check the full debate above for detailed insights." |
| ) |
| try: |
| tts = gTTS(text=summary, lang="en", slow=False) |
| tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") |
| tts.save(tmp.name) |
| return tmp.name |
| except Exception as e: |
| print(f"TTS Error: {e}") |
| return None |
|
|
| |
| |
| |
| def run_debate(user_idea: str, progress=gr.Progress()): |
| if not user_idea.strip(): |
| return "Please enter your idea first!", "No idea provided.", None |
|
|
| memory["conversations"].append(user_idea) |
|
|
| full_debate = "# SynapseOS — Agent Debate\n\n" |
| full_debate += f"**Idea Under Analysis:** {user_idea}\n\n" |
| full_debate += "**Model:** Qwen2.5-0-5B-Instruct (HuggingFace Free Tier)\n\n" |
| full_debate += "---\n\n" |
|
|
| agent_list = list(AGENTS.keys()) |
| responses_collected = {} |
|
|
| for i, agent_name in enumerate(agent_list): |
| agent = AGENTS[agent_name] |
| progress((i + 0.5) / len(agent_list), desc=f"{agent_name} is analyzing...") |
|
|
| full_debate += f"## {agent_name} — *{agent['role']}*\n\n" |
|
|
| response = call_hf_model(agent["system_prompt"], user_idea, max_new_tokens=500) |
| responses_collected[agent_name] = response |
|
|
| full_debate += f"{response}\n\n" |
| full_debate += "---\n\n" |
|
|
| progress((i + 1) / len(agent_list), desc=f"{agent_name} done!") |
| time.sleep(1) |
|
|
| |
| progress(0.95, desc="PM Agent writing final decision...") |
| full_debate += "## Final Decision — PM Agent\n\n" |
|
|
| consensus_prompt = """You are the PM Agent in SynapseOS. Five agents just debated a business idea. |
| Write a FINAL GO/NO-GO decision with: |
| 1. Your verdict (GO / CONDITIONAL GO / NO-GO) |
| 2. Top 3 action items to start immediately |
| 3. The single biggest risk to monitor |
| Keep it under 100 words, punchy and decisive.""" |
|
|
| consensus = call_hf_model(consensus_prompt, user_idea, max_new_tokens=200) |
| full_debate += f"{consensus}\n\n" |
|
|
| memory["debates"].append({ |
| "idea": user_idea, |
| "agents": responses_collected |
| }) |
|
|
| status = f"Debate complete! {len(memory['conversations'])} idea(s) analyzed. 5 agents responded in detail." |
| audio_path = generate_voice_summary(user_idea) |
|
|
| return full_debate, status, audio_path |
|
|
| |
| |
| |
| def show_memory(): |
| if not memory["conversations"]: |
| return "No ideas debated yet. Start your first debate!" |
|
|
| result = "## SynapseOS Memory\n\n" |
| result += f"**Total ideas analyzed:** {len(memory['conversations'])}\n\n" |
| for i, idea in enumerate(memory["conversations"], 1): |
| result += f"**{i}.** {idea}\n\n" |
| return result |
|
|
| |
| |
| |
| with gr.Blocks( |
| title="SynapseOS — AI Agent Debate", |
| theme=gr.themes.Soft( |
| primary_hue="indigo", |
| secondary_hue="emerald", |
| neutral_hue="slate" |
| ), |
| css=""" |
| .gradio-container { max-width: 1100px !important; } |
| #debate-output { min-height: 400px; } |
| """ |
| ) as app: |
|
|
| gr.Markdown(""" |
| # SynapseOS — AI Agent Civilization |
| ### 5 Expert AI Agents Debate Your Idea in Real-Time |
| **Powered by Qwen2.5-0-5B (HuggingFace Free) · English Only · Voice Summary Included** |
| |
| > Each agent calls the HuggingFace API independently and gives a detailed, unique analysis of your idea. |
| """) |
|
|
| with gr.Row(): |
| with gr.Column(scale=3): |
| idea_input = gr.Textbox( |
| label="Your Idea or Problem", |
| placeholder="e.g. Build a scam protection app for senior citizens in India", |
| lines=3, |
| info="Describe your startup idea, product, or problem in detail for best results." |
| ) |
| debate_btn = gr.Button( |
| "Launch 5-Agent Debate!", |
| variant="primary", |
| size="lg" |
| ) |
| status_box = gr.Textbox( |
| label="Status", |
| interactive=False, |
| value="Ready. Enter your idea and click Launch!" |
| ) |
|
|
| with gr.Column(scale=1): |
| gr.Markdown(""" |
| ## The 5 Agents |
| |
| | Agent | Role | |
| |-------|------| |
| | PM Agent | Strategy and Planning | |
| | Developer | Tech Architecture | |
| | Critic | Risks and Flaws | |
| | Finance | ROI and Revenue | |
| | Security | Privacy and Safety | |
| |
| ## How It Works |
| 1. You enter an idea |
| 2. Each agent calls Qwen2.5-0-5B on HuggingFace |
| 3. Gets a detailed 150+ word response |
| 4. PM Agent gives final GO/NO-GO |
| 5. Voice summary generated! |
| """) |
|
|
| with gr.Row(): |
| memory_btn = gr.Button("View Memory (All Past Ideas)", variant="secondary") |
|
|
| with gr.Row(): |
| with gr.Column(): |
| debate_output = gr.Markdown( |
| label="Live Agent Debate", |
| elem_id="debate-output", |
| value="*Your debate will appear here...*" |
| ) |
|
|
| with gr.Row(): |
| audio_output = gr.Audio( |
| label="Voice Summary (English)", |
| type="filepath" |
| ) |
|
|
| memory_output = gr.Markdown(visible=True) |
|
|
| debate_btn.click( |
| fn=run_debate, |
| inputs=[idea_input], |
| outputs=[debate_output, status_box, audio_output] |
| ) |
|
|
| memory_btn.click( |
| fn=show_memory, |
| outputs=[memory_output] |
| ) |
|
|
| gr.Markdown(""" |
| --- |
| **Setup:** Set your HuggingFace token as HF_TOKEN in .env file. |
| **Free Model:** Qwen/Qwen2.5-0-5B-Instruct — free on HuggingFace Inference API. |
| **Install:** pip install gradio huggingface_hub gtts |
| """) |
|
|
| if __name__ == "__main__": |
| app.launch(share=True) |
