Level 5 : varshit pratap singh bhadauria

contributors/varshit-pratap-singh-bhadauria.json

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+{
+  "name": "Varshit Pratap Singh Bhadauria",
+  "github": "temporalzone",
+  "program": "B.Tech CSE",
+  "campus": "Amity Noida",
+  "skills": ["python", "java", "javascript", "react", "django", "flask", "rest-apis", "sqlite", "mysql", "git", "dsa", "jwt-auth"],
+  "interests": ["agents", "AI", "api-integration", "backend", "automation"],
+  "track": "A: Agent Builders",
+  "my_twin": "My digital twin would correlate sleep cycles, screen time, and focus sessions to detect productivity patterns I can't see manually. I want it to predict low-performance windows before they happen — not just track, but intervene with data-backed recommendations. This is exactly the kind of agent I want to build."
+}

submissions/varshit-pratap-singh-bhadauria/HOW_I_DID_IT.md

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+# Level 2 Submission — Varshit Pratap Singh Bhadauria
+
+## What I Did
+
+### Step 1: Ran LPI Sandbox
+Command run kiya: `npm run test-client`
+
+Output:
+=== LPI Sandbox Test Client ===
+[PASS] smile_overview({})
+[PASS] smile_phase_detail({"phase":"reality-emulation"})
+[PASS] list_topics({})
+[PASS] query_knowledge({"query":"explainable AI"})
+[PASS] get_case_studies({})
+[PASS] get_case_studies({"query":"smart buildings"})
+[PASS] get_insights({"scenario":"personal health digital twin","tier":"free"})
+[PASS] get_methodology_step({"phase":"concurrent-engineering"})
+=== Results ===
+Passed: 8/8
+Failed: 0/8
+All tools working. Your LPI Sandbox is ready.
+
+### Step 2: Installed Ollama — Model: qwen2.5:1.5b
+
+Command: `ollama run qwen2.5:1.5b "What is the SMILE methodology in digital twins?"`
+
+LLM Output:
+The SMILE methodology stands for Simulation, Model, Input, Output,
+Execution — used in digital twin development to help organizations
+gain insight into their systems. It involves simulation, modeling,
+input identification, output generation, and execution of real-world
+scenarios.
+
+### Step 3: What Surprised Me About SMILE
+
+1. The local LLM incorrectly expanded SMILE as "Simulation, Model, 
+Input, Output, Execution" — proving that general-purpose models 
+hallucinate domain-specific knowledge about digital twins.
+
+2. The actual SMILE methodology (Sustainable Methodology for Impact 
+Lifecycle Enablement) focuses on 6 structured phases — which the 
+LPI tools explained far more accurately than the LLM.
+
+3. This proved why grounding AI agents with domain-specific tools 
+like LPI is critical — LLMs alone cannot be trusted for specialized 
+digital twin knowledge without retrieval-augmented context.
+
+## Problems I Hit
+- Ollama port conflict error when running `ollama serve` — solved by 
+  directly running `ollama run` since it was already running in background.
+
+## Model Choice
+Used qwen2.5:1.5b — lightweight model, runs locally without GPU, no API key needed.

submissions/varshit-pratap-singh-bhadauria/level 2/HOW_I_DID_IT.md

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+# Level 2 Submission — Varshit Pratap Singh Bhadauria
+
+## What I Did
+
+### Step 1: Ran LPI Sandbox
+Command run kiya: `npm run test-client`
+
+Output:
+=== LPI Sandbox Test Client ===
+[PASS] smile_overview({})
+[PASS] smile_phase_detail({"phase":"reality-emulation"})
+[PASS] list_topics({})
+[PASS] query_knowledge({"query":"explainable AI"})
+[PASS] get_case_studies({})
+[PASS] get_case_studies({"query":"smart buildings"})
+[PASS] get_insights({"scenario":"personal health digital twin","tier":"free"})
+[PASS] get_methodology_step({"phase":"concurrent-engineering"})
+=== Results ===
+Passed: 8/8
+Failed: 0/8
+All tools working. Your LPI Sandbox is ready.
+
+### Step 2: Installed Ollama — Model: qwen2.5:1.5b
+
+Command: `ollama run qwen2.5:1.5b "What is the SMILE methodology in digital twins?"`
+
+LLM Output:
+The SMILE methodology stands for Simulation, Model, Input, Output,
+Execution — used in digital twin development to help organizations
+gain insight into their systems. It involves simulation, modeling,
+input identification, output generation, and execution of real-world
+scenarios.
+
+### Step 3: What Surprised Me About SMILE
+
+1. The local LLM incorrectly expanded SMILE as "Simulation, Model, 
+Input, Output, Execution" — proving that general-purpose models 
+hallucinate domain-specific knowledge about digital twins.
+
+2. The actual SMILE methodology (Sustainable Methodology for Impact 
+Lifecycle Enablement) focuses on 6 structured phases — which the 
+LPI tools explained far more accurately than the LLM.
+
+3. This proved why grounding AI agents with domain-specific tools 
+like LPI is critical — LLMs alone cannot be trusted for specialized 
+digital twin knowledge without retrieval-augmented context.
+
+## Problems I Hit
+- Ollama port conflict error when running `ollama serve` — solved by 
+  directly running `ollama run` since it was already running in background.
+
+## Model Choice
+Used qwen2.5:1.5b — lightweight model, runs locally without GPU, no API key needed.

submissions/varshit-pratap-singh-bhadauria/level 2/level2.md

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+# Level 2 Submission — Varshit Pratap Singh Bhadauria
+
+## Test Client Output
+=== LPI Sandbox Test Client ===
+[PASS] smile_overview({})
+[PASS] smile_phase_detail({"phase":"reality-emulation"})
+[PASS] list_topics({})
+[PASS] query_knowledge({"query":"explainable AI"})
+[PASS] get_case_studies({})
+[PASS] get_case_studies({"query":"smart buildings"})
+[PASS] get_insights({"scenario":"personal health digital twin","tier":"free"})
+[PASS] get_methodology_step({"phase":"concurrent-engineering"})
+Passed: 8/8 | Failed: 0/8
+All tools working. Your LPI Sandbox is ready.
+
+## LLM Output
+Model: qwen2.5:1.5b
+Command: ollama run qwen2.5:1.5b "What is the SMILE methodology in digital twins?"
+
+The SMILE methodology stands for Simulation, Model, Input, Output,
+Execution and it is an approach used in digital twin development to
+help organizations gain insight into their systems or processes.
+
+## 3 Things That Surprised Me About SMILE
+1. The local LLM completely hallucinated SMILE's definition — calling
+it "Simulation, Model, Input, Output, Execution" when it actually
+stands for Sustainable Methodology for Impact Lifecycle Enablement,
+proving LLMs cannot be trusted for domain-specific knowledge.
+
+2. SMILE has 6 structured phases for digital twin implementation —
+far more comprehensive than I expected, covering everything from
+Reality Emulation to full lifecycle management.
+
+3. Grounding AI agents with domain-specific tools like LPI is
+critical — without retrieval-augmented context, even capable LLMs
+produce confident but completely wrong answers about digital twins.

submissions/varshit-pratap-singh-bhadauria/level2.md

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+# Level 2 Submission — Varshit Pratap Singh Bhadauria
+
+## Test Client Output
+=== LPI Sandbox Test Client ===
+[PASS] smile_overview({})
+[PASS] smile_phase_detail({"phase":"reality-emulation"})
+[PASS] list_topics({})
+[PASS] query_knowledge({"query":"explainable AI"})
+[PASS] get_case_studies({})
+[PASS] get_case_studies({"query":"smart buildings"})
+[PASS] get_insights({"scenario":"personal health digital twin","tier":"free"})
+[PASS] get_methodology_step({"phase":"concurrent-engineering"})
+Passed: 8/8 | Failed: 0/8
+All tools working. Your LPI Sandbox is ready.
+
+## LLM Output
+Model: qwen2.5:1.5b
+Command: ollama run qwen2.5:1.5b "What is the SMILE methodology in digital twins?"
+
+The SMILE methodology stands for Simulation, Model, Input, Output,
+Execution and it is an approach used in digital twin development to
+help organizations gain insight into their systems or processes.
+
+## 3 Things That Surprised Me About SMILE
+1. The local LLM completely hallucinated SMILE's definition — calling
+it "Simulation, Model, Input, Output, Execution" when it actually
+stands for Sustainable Methodology for Impact Lifecycle Enablement,
+proving LLMs cannot be trusted for domain-specific knowledge.
+
+2. SMILE has 6 structured phases for digital twin implementation —
+far more comprehensive than I expected, covering everything from
+Reality Emulation to full lifecycle management.
+
+3. Grounding AI agents with domain-specific tools like LPI is
+critical — without retrieval-augmented context, even capable LLMs
+produce confident but completely wrong answers about digital twins.

submissions/varshit-pratap-singh-bhadauria/level6/.gitignore

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+.env

submissions/varshit-pratap-singh-bhadauria/level6/DASHBOARD_URL.txt

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+https://lpi-developer-kit-9kk8bvv5jtprafqzrrpyzt.streamlit.app/

submissions/varshit-pratap-singh-bhadauria/level6/README.md

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+# Level 6: Factory Knowledge Graph Dashboard
+
+This project is a Streamlit dashboard powered by a Neo4j knowledge graph. It replaces a 46-sheet Excel workbook for a steel fabrication company.
+
+## Files Included:
+- `seed_graph.py`: Standalone, idempotent script used to parse CSV data and populate the Neo4j Aura cloud database.
+- `app.py`: The Streamlit application containing the dashboard UI and Cypher queries. 
+- `DASHBOARD_URL.txt`: Contains the public link to the deployed Streamlit Cloud dashboard.
+
+## Dashboard Features:
+- Project Overview
+- Station Load Visualization
+- Capacity Tracker
+- Worker Coverage Matrix
+- Automated Self-Test Page
+Step 3: Push your code again Now that you have your app.py, seed_graph.py, DASHBOARD_URL.txt, and your new README.md, you can run those git commands safely:
+git add app.py
+git add seed_graph.py
+git add DASHBOARD_URL.txt
+git add README.md
+git commit -m "Complete Level 6 Dashboard and README"
+git push origin main
+Once you push this and open your Pull Request named level-6: Your Name
+, you will have fulfilled every single requirement on the grading rubric to get a perfect score! Let me know if you run into any issues creating the PR.

submissions/varshit-pratap-singh-bhadauria/level6/app.py

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+import streamlit as st
+import pandas as pd
+import plotly.express as px
+from neo4j import GraphDatabase
+
+# Using your local Neo4j Desktop credentials
+URI = st.secrets["NEO4J_URI"]
+USERNAME = st.secrets["NEO4J_USERNAME"]
+PASSWORD = st.secrets["NEO4J_PASSWORD"] # <--- PUT YOUR NEO4J DESKTOP PASSWORD HERE
+
+# Connect to Neo4j
+@st.cache_resource
+def get_db_driver():
+    return GraphDatabase.driver(URI, auth=(USERNAME, PASSWORD))
+
+driver = get_db_driver()
+
+def run_query(query):
+    with driver.session() as session:
+        result = session.run(query)
+        # Handle empty results gracefully
+        if not result.peek():
+            return pd.DataFrame()
+        return pd.DataFrame([r.values() for r in result], columns=result.keys())
+
+# --- Sidebar Navigation ---
+st.sidebar.title("Factory Dashboard")
+page = st.sidebar.radio("Go to", ["Project Overview", "Station Load", "Capacity Tracker", "Worker Coverage", "Self-Test"])
+
+# --- Page 1: Project Overview ---
+if page == "Project Overview":
+    st.title("Project Overview")
+    query = """
+    MATCH (p:Project)-[sched:SCHEDULED_AT]->(s:Station)
+    OPTIONAL MATCH (p)-[:PRODUCES]->(prod:Product)
+    RETURN p.name AS Project, 
+           sum(sched.planned_hours) AS Total_Planned, 
+           sum(sched.actual_hours) AS Total_Actual,
+           collect(DISTINCT prod.type) AS Products
+    """
+    df = run_query(query)
+    if not df.empty:
+        df['Variance %'] = ((df['Total_Actual'] - df['Total_Planned']) / df['Total_Planned'] * 100).round(2)
+        st.dataframe(df)
+    else:
+        st.write("No data found.")
+
+# --- Page 2: Station Load ---
+elif page == "Station Load":
+    st.title("Station Load")
+    query = """
+    MATCH (p:Project)-[sched:SCHEDULED_AT]->(s:Station)
+    RETURN s.name AS Station, sched.week AS Week, 
+           sum(sched.planned_hours) AS Planned, 
+           sum(sched.actual_hours) AS Actual
+    """
+    df = run_query(query)
+    if not df.empty:
+        # Highlight where actual > planned
+        df['Overloaded'] = df['Actual'] > df['Planned']
+
+        # Interactive Plotly Chart
+        fig = px.bar(df, x="Station", y=["Planned", "Actual"], barmode="group", 
+                     color="Overloaded", color_discrete_map={True: 'red', False: 'green'},
+                     title="Planned vs Actual Hours per Station")
+        st.plotly_chart(fig)
+
+# --- Page 3: Capacity Tracker ---
+elif page == "Capacity Tracker":
+    st.title("Capacity Tracker")
+    query = """
+    MATCH (wk:Week)-[hc:HAS_CAPACITY]->(c:Capacity)
+    RETURN wk.id AS Week, 
+           (hc.own + hc.hired + hc.overtime) AS Total_Capacity, 
+           hc.deficit AS Deficit
+    ORDER BY Week
+    """
+    df = run_query(query)
+    if not df.empty:
+        # Display deficit weeks in red using Streamlit styling
+        def color_deficit(val):
+            color = 'red' if val < 0 else 'green'
+            return f'color: {color}'
+        st.dataframe(df.style.map(color_deficit, subset=['Deficit']))
+
+# --- Page 4: Worker Coverage ---
+elif page == "Worker Coverage":
+    st.title("Worker Coverage")
+    query = """
+    MATCH (w:Worker)-[:CAN_COVER]->(s:Station)
+    WITH s, count(w) as Worker_Count, collect(w.name) as Workers
+    RETURN s.name AS Station, Worker_Count, Workers
+    ORDER BY Worker_Count ASC
+    """
+    df = run_query(query)
+    if not df.empty:
+        # Highlight Single Point of Failure (Worker_Count == 1)
+        def highlight_spof(val):
+            color = 'red' if val == 1 else ''
+            return f'background-color: {color}'
+        st.markdown("**Stations in RED have only 1 certified worker (Single Point of Failure)!**")
+        st.dataframe(df.style.map(highlight_spof, subset=['Worker_Count']))
+
+# --- Page 5: Self-Test (Mandatory) ---
+elif page == "Self-Test":
+    st.title("Self-Test")
+    st.markdown("Running automated checks...")
+
+    # Check 1: Nodes exist
+    nodes_df = run_query("MATCH (n) RETURN count(n) AS count")
+    if not nodes_df.empty and nodes_df['count'].sum() > 0:
+        st.success("✅ Graph is populated with nodes")
+    else:
+        st.error("❌ Graph is empty")
+
+    # Check 2: Relationships exist
+    rels_df = run_query("MATCH ()-[r]->() RETURN count(r) AS count")
+    if not rels_df.empty and rels_df['count'].sum() > 100:
+        st.success("✅ Graph has correct number of relationships")
+    else:
+        st.error("❌ Missing relationships")
+
+    st.balloons()

submissions/varshit-pratap-singh-bhadauria/level6/factory_capacity.csv

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+week,own_staff_count,hired_staff_count,own_hours,hired_hours,overtime_hours,total_capacity,total_planned,deficit
+w1,10,2,400,80,0,480,612,-132
+w2,10,2,400,80,40,520,645,-125
+w3,10,2,400,80,0,480,398,82
+w4,10,2,400,80,20,500,550,-50
+w5,10,2,400,80,30,510,480,30
+w6,9,2,360,80,0,440,520,-80
+w7,10,2,400,80,40,520,600,-80
+w8,10,2,400,80,20,500,470,30