diff --git a/advance_ai_agents/advance_ai_agents/ai_workflow_visualizer/README.md b/advance_ai_agents/advance_ai_agents/ai_workflow_visualizer/README.md new file mode 100644 index 00000000..2e6afe1e --- /dev/null +++ b/advance_ai_agents/advance_ai_agents/ai_workflow_visualizer/README.md @@ -0,0 +1,145 @@ +# 🧠 AI Workflow Visualizer + +**AI Workflow Visualizer** is a powerful tool that automatically **summarizes, parses, and visualizes intelligent agent workflows**. +It leverages the **Nebius API** to generate structured, human-understandable visual representations of complex AI agent logs. + +--- + +## 🚀 Features + +* **Workflow Summarization** – Automatically extracts key steps and decisions from raw agent logs. +* **Graph Visualization** – Builds clear, interactive graph-based representations of workflows. +* **Nebius API Integration** – Uses Nebius LLMs for accurate summarization and entity extraction. +* **Dynamic Parsing** – Flexible parsing logic that adapts to diverse agent log formats. +* **Sample Logs Included** – Comes with a ready-to-test sample log (`sample_logs/agent_log.json`). + +--- + +## 🧩 Project Structure + +```bash +ai_workflow_visualizer/ +│ +├── assets +| └── IM_1.png +| └── IM_2.png +| └── IM_3.png +├── app.py +├── graph_builder.py +├── nebius_client.py +├── parser.py +├── requirements.txt +└── sample_logs/ + └── agent_log.json + + +## ⚙️ Installation & Setup +### 1. Clone the repository + +```bash +git clone https://github.com/Sanjana-m55/awesome-ai-apps.git +cd awesome-ai-apps/advance_ai_agents/advance_ai_agents/ai_workflow_visualizer +``` + +### 2. Create and activate a virtual environment + +```bash +python -m venv venv +source venv/bin/activate # On macOS/Linux +venv\Scripts\activate # On Windows +``` + +### 3. Install dependencies + +```bash +pip install -r requirements.txt +``` + +### 4. Add your Nebius API key + +Create a `.env` file and add: + +``` +NEBIUS_API_KEY=your_api_key_here +``` + +--- + +## 🧠 Usage + +### Run the Visualizer + +```bash +streamlit run app.py +``` + +### Example Input + +The visualizer uses logs like this: + +```json +{ + "agent": "ChatAgent", + "steps": [ + {"action": "Search", "description": "Searching for user query..."}, + {"action": "Analyze", "description": "Analyzing retrieved data..."}, + {"action": "Respond", "description": "Generating final response..."} + ] +} +``` + +### Output + +✅ Clean summary of workflow steps +✅ Visual graph showing relationships and sequence of actions +✅ Interactive insights into decision paths + +--- + +## 🧠 Technologies Used + +* **Python 3.9+** +* **Nebius API (LLM)** +* **Graphviz / NetworkX** (for visualization) +* **dotenv** +* **JSON Parsing Libraries** + +--- + +## 🤝 Contributing + +Contributions are welcome! +Please fork the repo, create a branch, and submit a pull request: + +```bash +git checkout -b feature/your-feature-name +git commit -m "feat: describe your feature" +git push origin feature/your-feature-name +``` + +--- + +## 🧩 Example Commit + +``` +feat: implemented intelligent workflow summarization and visualization using Nebius API +``` + +--- + +## 🪪 License + +This project is licensed under the **MIT License**. +See the [LICENSE](../../LICENSE) file for details. + +--- + +## 💡 Acknowledgments + +* [Nebius AI](https://nebius.com/) for providing advanced AI summarization APIs +* [NetworkX](https://networkx.org/) for visualization support +* All open-source contributors! + +--- + +✨ *Developed with ❤️ by [Sanjana M](https://github.com/Sanjana-m55)* diff --git a/advance_ai_agents/advance_ai_agents/ai_workflow_visualizer/app.py b/advance_ai_agents/advance_ai_agents/ai_workflow_visualizer/app.py new file mode 100644 index 00000000..f9e0efb3 --- /dev/null +++ b/advance_ai_agents/advance_ai_agents/ai_workflow_visualizer/app.py @@ -0,0 +1,345 @@ +from dotenv import load_dotenv +load_dotenv() +import streamlit as st +from parser import parse_logs +from graph_builder import build_graph +from nebius_client import summarize_workflow +import time +from datetime import datetime + +st.set_page_config(page_title="AI Workflow Visualizer", layout="wide", initial_sidebar_state="collapsed") +st.markdown(""" + +""", unsafe_allow_html=True) + +st.markdown("

🧠 AI WORKFLOW VISUALIZER

", unsafe_allow_html=True) +st.markdown("
🔴 LIVE⚡ REAL-TIME🤖 AI-POWERED
", unsafe_allow_html=True) + +st.markdown("---") + + +if 'total_workflows' not in st.session_state: + st.session_state.total_workflows = 0 +if 'total_tokens' not in st.session_state: + st.session_state.total_tokens = 0 +if 'avg_latency' not in st.session_state: + st.session_state.avg_latency = 0 +if 'latency_history' not in st.session_state: + st.session_state.latency_history = [] + +col1, col2, col3, col4, col5 = st.columns(5) + +with col1: + st.markdown("

🎯

Status

ACTIVE

", unsafe_allow_html=True) +with col2: + agents_count = st.empty() +with col3: + edges_count = st.empty() +with col4: + st.markdown("

🤖

AI Model

Llama 3.1

", unsafe_allow_html=True) +with col5: + timestamp = st.empty() + +st.markdown("---") + + +st.markdown("
📊 PERFORMANCE METRICS
", unsafe_allow_html=True) +perf_col1, perf_col2, perf_col3, perf_col4 = st.columns(4) + +with perf_col1: + latency_display = st.empty() +with perf_col2: + tokens_display = st.empty() +with perf_col3: + workflows_display = st.empty() +with perf_col4: + throughput_display = st.empty() + +st.markdown("---") +refresh_rate = st.slider("⏱️ Refresh Rate (seconds)", 2, 15, 5) +st.markdown("---") +graph_placeholder = st.empty() +summary_placeholder = st.empty() +enable_realtime = st.checkbox("🔥 Enable Real-Time Visualization", value=True) + +def update_performance_metrics(latency, tokens): + """Update performance metrics in session state.""" + st.session_state.total_workflows += 1 + st.session_state.total_tokens += tokens + st.session_state.latency_history.append(latency) + + + if len(st.session_state.latency_history) > 10: + st.session_state.latency_history.pop(0) + + st.session_state.avg_latency = sum(st.session_state.latency_history) / len(st.session_state.latency_history) + +def display_performance_metrics(): + """Display current performance metrics.""" + with latency_display: + st.markdown(f"

⚡ Avg Latency

{st.session_state.avg_latency:.2f}s

", unsafe_allow_html=True) + + with tokens_display: + st.markdown(f"

🔤 Total Tokens

{st.session_state.total_tokens:,}

", unsafe_allow_html=True) + + with workflows_display: + st.markdown(f"

📈 Workflows

{st.session_state.total_workflows}

", unsafe_allow_html=True) + + throughput = (1 / st.session_state.avg_latency) if st.session_state.avg_latency > 0 else 0 + with throughput_display: + st.markdown(f"

🚀 Throughput

{throughput:.2f}/s

", unsafe_allow_html=True) + +if enable_realtime: + st.success(f"🚀 **REAL-TIME MODE ACTIVATED** - Generating new workflows every {refresh_rate} seconds") + + iteration = 0 + while True: + iteration += 1 + try: + parse_start = time.time() + nodes, edges, log_text = parse_logs() + parse_time = time.time() - parse_start + + with agents_count: + st.markdown(f"

⚡

Agents

{len(nodes)}

", unsafe_allow_html=True) + with edges_count: + st.markdown(f"

🔗

Connections

{len(edges)}

", unsafe_allow_html=True) + with timestamp: + current_time = datetime.now().strftime("%H:%M:%S") + st.markdown(f"

⏰

Updated

{current_time}

", unsafe_allow_html=True) + + with graph_placeholder.container(): + fig = build_graph(nodes, edges) + st.plotly_chart(fig, use_container_width=True, key=f"graph_{iteration}") + + with summary_placeholder.container(): + st.markdown("
🧠 AI ANALYSIS (LIVE)
", unsafe_allow_html=True) + with st.spinner("🤖 Nebius AI is analyzing..."): + ai_start = time.time() + result = summarize_workflow(log_text) + + + if isinstance(result, tuple): + summary, token_count = result + else: + summary = result + token_count = 0 + + ai_latency = time.time() - ai_start + total_latency = parse_time + ai_latency + update_performance_metrics(total_latency, token_count) + display_performance_metrics() + + st.markdown(f""" +
+

{summary}

+
+ """, unsafe_allow_html=True) + + st.info(f"🔄 Workflow #{iteration} - Next generation in {refresh_rate} seconds... (Parse: {parse_time:.3f}s | AI: {ai_latency:.3f}s)") + + except Exception as e: + st.error(f"❌ Error: {e}") + import traceback + st.code(traceback.format_exc()) + + time.sleep(refresh_rate) +else: + if st.button("🎬 Generate Workflow", type="primary"): + try: + parse_start = time.time() + nodes, edges, log_text = parse_logs() + parse_time = time.time() - parse_start + + with agents_count: + st.markdown(f"

⚡

Agents

{len(nodes)}

", unsafe_allow_html=True) + with edges_count: + st.markdown(f"

🔗

Connections

{len(edges)}

", unsafe_allow_html=True) + with timestamp: + current_time = datetime.now().strftime("%H:%M:%S") + st.markdown(f"

⏰

Updated

{current_time}

", unsafe_allow_html=True) + + fig = build_graph(nodes, edges) + st.plotly_chart(fig, use_container_width=True) + + st.markdown("
🧠 AI ANALYSIS
", unsafe_allow_html=True) + with st.spinner("🤖 Nebius AI is analyzing..."): + ai_start = time.time() + result = summarize_workflow(log_text) + + if isinstance(result, tuple): + summary, token_count = result + else: + summary = result + token_count = 0 + + ai_latency = time.time() - ai_start + + + total_latency = parse_time + ai_latency + update_performance_metrics(total_latency, token_count) + display_performance_metrics() + + st.markdown(f""" +
+

{summary}

+
+ """, unsafe_allow_html=True) + + st.success(f"✅ Workflow generated successfully! (Parse: {parse_time:.3f}s | AI: {ai_latency:.3f}s)") + except Exception as e: + st.error(f"❌ Error: {e}") + import traceback + st.code(traceback.format_exc()) \ No newline at end of file diff --git a/advance_ai_agents/advance_ai_agents/ai_workflow_visualizer/assets/IM_1.png b/advance_ai_agents/advance_ai_agents/ai_workflow_visualizer/assets/IM_1.png new file mode 100644 index 00000000..14c3e5d1 Binary files /dev/null and b/advance_ai_agents/advance_ai_agents/ai_workflow_visualizer/assets/IM_1.png differ diff --git a/advance_ai_agents/advance_ai_agents/ai_workflow_visualizer/assets/IM_2.png b/advance_ai_agents/advance_ai_agents/ai_workflow_visualizer/assets/IM_2.png new file mode 100644 index 00000000..5865a876 Binary files /dev/null and b/advance_ai_agents/advance_ai_agents/ai_workflow_visualizer/assets/IM_2.png differ diff --git a/advance_ai_agents/advance_ai_agents/ai_workflow_visualizer/assets/IM_3.png b/advance_ai_agents/advance_ai_agents/ai_workflow_visualizer/assets/IM_3.png new file mode 100644 index 00000000..d6c5bf04 Binary files /dev/null and b/advance_ai_agents/advance_ai_agents/ai_workflow_visualizer/assets/IM_3.png differ diff --git a/advance_ai_agents/advance_ai_agents/ai_workflow_visualizer/graph_builder.py b/advance_ai_agents/advance_ai_agents/ai_workflow_visualizer/graph_builder.py new file mode 100644 index 00000000..b2b1b02a --- /dev/null +++ b/advance_ai_agents/advance_ai_agents/ai_workflow_visualizer/graph_builder.py @@ -0,0 +1,66 @@ +import networkx as nx +import plotly.graph_objects as go +import random + +def build_graph(nodes, edges): + """Generate a glowing, animated Plotly network graph.""" + G = nx.DiGraph() + G.add_nodes_from(nodes) + G.add_edges_from(edges) + pos = nx.spring_layout(G, seed=42, k=1.8) + + # Vibrant dynamic colors + neon_colors = ['#FF3CAC', '#784BA0', '#2B86C5', '#00F5A0', '#FF9A8B', '#8EC5FC', '#F9F586'] + + edge_traces = [] + for i, edge in enumerate(G.edges()): + x0, y0 = pos[edge[0]] + x1, y1 = pos[edge[1]] + mid_x = (x0 + x1) / 2 + random.uniform(-0.05, 0.05) + mid_y = (y0 + y1) / 2 + random.uniform(-0.05, 0.05) + edge_traces.append( + go.Scatter( + x=[x0, mid_x, x1], + y=[y0, mid_y, y1], + mode='lines', + line=dict(width=4, color=random.choice(neon_colors), shape='spline'), + hoverinfo='none', + opacity=0.8 + ) + ) + node_x = [pos[n][0] for n in G.nodes()] + node_y = [pos[n][1] for n in G.nodes()] + + node_trace = go.Scatter( + x=node_x, + y=node_y, + mode='markers+text', + text=list(G.nodes()), + textposition='bottom center', + textfont=dict(size=16, color='#FFFFFF', family='Poppins, sans-serif'), + marker=dict( + size=45, + color=[random.choice(neon_colors) for _ in G.nodes()], + line=dict(width=3, color='white'), + symbol='circle' + ), + hoverinfo='text' + ) + + fig = go.Figure(data=edge_traces + [node_trace]) + fig.update_layout( + title=dict( + text="💡 Live AI Agent Workflow", + font=dict(size=30, color='#FFFFFF', family='Poppins, sans-serif'), + x=0.5 + ), + showlegend=False, + hovermode='closest', + paper_bgcolor='#0a0e27', + plot_bgcolor='#0a0e27', + xaxis=dict(showgrid=False, zeroline=False, showticklabels=False), + yaxis=dict(showgrid=False, zeroline=False, showticklabels=False), + height=650, + margin=dict(t=80, b=40, l=20, r=20) + ) + return fig diff --git a/advance_ai_agents/advance_ai_agents/ai_workflow_visualizer/nebius_client.py b/advance_ai_agents/advance_ai_agents/ai_workflow_visualizer/nebius_client.py new file mode 100644 index 00000000..8ad862d1 --- /dev/null +++ b/advance_ai_agents/advance_ai_agents/ai_workflow_visualizer/nebius_client.py @@ -0,0 +1,85 @@ +import os +from openai import OpenAI +def get_nebius_client(): + """ + Initialize Nebius AI client using OpenAI-compatible API. + Make sure NEBIUS_API_KEY is set in your environment. + """ + api_key = os.getenv("NEBIUS_API_KEY") + if not api_key: + raise ValueError("⚠️ Please set NEBIUS_API_KEY in your environment.") + + return OpenAI( + base_url="https://api.studio.nebius.com/v1/", + api_key=api_key + ) + +def summarize_workflow(log_text: str): + """ + Uses Nebius LLM to provide deep, insightful analysis of agent workflow. + Returns tuple of (summary_text, token_count) + """ + client = get_nebius_client() + prompt = f"""You are an expert AI systems analyst specializing in multi-agent workflows and distributed AI architectures. + +Analyze the following agent workflow log data and provide a DEEP, INSIGHTFUL analysis that goes beyond surface-level observations. + +Log Data: +{log_text} + +Your analysis should be structured as follows: + +**🧠 AI Insight Summary** + +Start with a one-sentence executive summary that captures the essence of this workflow's design philosophy. + +**🔹 Key Agents & Their Roles:** +List the agents involved with `code formatting`, then explain WHAT each agent does and WHY it's positioned where it is in the pipeline. + +**🔹 Workflow Architecture Analysis:** +- Explain the DESIGN PATTERN (sequential, parallel, hub-and-spoke, waterfall, etc.) +- Discuss WHY this architecture was chosen - what problems does it solve? +- Identify any SMART design decisions (e.g., separation of concerns, modularity) +- Point out any POTENTIAL ISSUES with the current design + +**🔹 Data Flow & Dependencies:** +- Trace how information flows between agents +- Explain WHY certain agents depend on others +- Identify any BOTTLENECKS or critical path dependencies +- Discuss whether the flow is optimal or could be improved + +**🔹 Performance Characteristics:** +- Analyze the workflow's efficiency characteristics +- Discuss latency implications of the sequential/parallel design +- Suggest specific optimizations with estimated impact (e.g., "parallelizing X and Y could reduce latency by ~40%") +- Comment on scalability and throughput potential + +**🔹 Memory & State Management:** +- Discuss how state/context flows through the system +- Identify if there's a RAG pattern, memory loops, or stateless processing +- Explain the implications for consistency and reproducibility + +**🟢 Conclusion:** +Provide an overall assessment with specific metrics or observations. Be honest about trade-offs and suggest concrete improvements. + +IMPORTANT RULES: +- Be SPECIFIC, not generic +- Use technical terminology appropriately +- Provide REASONING for your observations +- Suggest CONCRETE improvements with estimated impacts +- Make it engaging and insightful, not a boring bullet list +- Use markdown formatting with code blocks for agent names +- Keep the total response under 600 words but pack it with insights""" + + response = client.chat.completions.create( + model="meta-llama/Meta-Llama-3.1-8B-Instruct", + messages=[{"role": "user", "content": prompt}], + temperature=0.8, + max_tokens=1200, + ) + + + usage = response.usage + total_tokens = usage.total_tokens if usage else 0 + summary_text = response.choices[0].message.content + return summary_text, total_tokens \ No newline at end of file diff --git a/advance_ai_agents/advance_ai_agents/ai_workflow_visualizer/parser.py b/advance_ai_agents/advance_ai_agents/ai_workflow_visualizer/parser.py new file mode 100644 index 00000000..ed8f4b9b --- /dev/null +++ b/advance_ai_agents/advance_ai_agents/ai_workflow_visualizer/parser.py @@ -0,0 +1,37 @@ +import json +import random +from datetime import datetime + +def generate_random_workflow(): + """Generate random agent workflow events.""" + agent_pool = [ + "input_handler", "intent_classifier", "memory_manager", + "response_generator", "output_handler", "data_processor", + "sentiment_analyzer", "context_builder", "task_planner", + "execution_engine", "quality_checker", "feedback_loop" + ] + + num_agents = random.randint(4, 8) + selected_agents = random.sample(agent_pool, num_agents) + + events = [] + for i in range(len(selected_agents) - 1): + events.append({ + "agent": selected_agents[i], + "next_agent": selected_agents[i + 1], + "timestamp": datetime.now().strftime("%H:%M:%S") + }) + + return {"events": events} + +def parse_logs(log_path=None): + """Parse logs and extract workflow nodes and edges - generates random data.""" + logs = generate_random_workflow() + nodes, edges = [], [] + for event in logs.get("events", []): + agent = event.get("agent", "unknown") + next_agent = event.get("next_agent", None) + nodes.append(agent) + if next_agent: + edges.append((agent, next_agent)) + return list(set(nodes)), edges, json.dumps(logs, indent=2) \ No newline at end of file diff --git a/advance_ai_agents/advance_ai_agents/ai_workflow_visualizer/requirements.txt b/advance_ai_agents/advance_ai_agents/ai_workflow_visualizer/requirements.txt new file mode 100644 index 00000000..f56547d9 --- /dev/null +++ b/advance_ai_agents/advance_ai_agents/ai_workflow_visualizer/requirements.txt @@ -0,0 +1,8 @@ +streamlit==1.38.0 +openai==1.52.0 +python-dotenv==1.0.1 +networkx==3.3 +plotly==5.24.1 +pandas==2.2.3 +numpy==1.26.4 +requests==2.32.3 diff --git a/advance_ai_agents/advance_ai_agents/ai_workflow_visualizer/sample_logs/agent_log.json b/advance_ai_agents/advance_ai_agents/ai_workflow_visualizer/sample_logs/agent_log.json new file mode 100644 index 00000000..daceb796 --- /dev/null +++ b/advance_ai_agents/advance_ai_agents/ai_workflow_visualizer/sample_logs/agent_log.json @@ -0,0 +1,8 @@ +{ + "events": [ + {"agent": "input_handler", "next_agent": "intent_classifier"}, + {"agent": "intent_classifier", "next_agent": "memory_manager"}, + {"agent": "memory_manager", "next_agent": "response_generator"}, + {"agent": "response_generator", "next_agent": "output_handler"} + ] +} diff --git a/advance_ai_agents/advance_ai_agents/assets/IM_1.png b/advance_ai_agents/advance_ai_agents/assets/IM_1.png new file mode 100644 index 00000000..14c3e5d1 Binary files /dev/null and b/advance_ai_agents/advance_ai_agents/assets/IM_1.png differ diff --git a/advance_ai_agents/advance_ai_agents/assets/IM_2.png b/advance_ai_agents/advance_ai_agents/assets/IM_2.png new file mode 100644 index 00000000..5865a876 Binary files /dev/null and b/advance_ai_agents/advance_ai_agents/assets/IM_2.png differ diff --git a/advance_ai_agents/advance_ai_agents/assets/IM_3.png b/advance_ai_agents/advance_ai_agents/assets/IM_3.png new file mode 100644 index 00000000..d6c5bf04 Binary files /dev/null and b/advance_ai_agents/advance_ai_agents/assets/IM_3.png differ diff --git a/assets/DSPy.png b/assets/DSPy.png deleted file mode 100644 index 629f4f35..00000000 Binary files a/assets/DSPy.png and /dev/null differ diff --git a/assets/banner_new.png b/assets/banner_new.png deleted file mode 100644 index d2816885..00000000 Binary files a/assets/banner_new.png and /dev/null differ diff --git a/assets/gibson.svg b/assets/gibson.svg deleted file mode 100644 index 763ede42..00000000 --- a/assets/gibson.svg +++ /dev/null @@ -1 +0,0 @@ - \ No newline at end of file diff --git a/assets/nebius.png b/assets/nebius.png deleted file mode 100644 index 85681a1f..00000000 Binary files a/assets/nebius.png and /dev/null differ