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import streamlit as st
import pandas as pd
import numpy as np
import os
import json
import sys
# Add project root to sys.path to allow importing from core
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
# Load environment variables
from dotenv import load_dotenv
load_dotenv()
# Ensure API key is set
if not os.getenv("GOOGLE_API_KEY"):
st.error("GOOGLE_API_KEY not found in environment variables. Please check your .env file.")
st.stop()
from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_agent
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_core.prompts import PromptTemplate
from langchain_core.output_parsers import StrOutputParser
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import base64
import io
from langchain_core.messages import HumanMessage
from core.agents.plotting import create_dcrm_plot, create_velocity_plot, create_resistance_zoom_plot, get_dcrm_prompt
from core.calculators.kpi import calculate_kpis
from core.calculators.cbhi import compute_cbhi
from core.signal.phases import analyze_dcrm_data
from core.engines.rules import analyze_dcrm_advanced
from core.agents.diagnosis import detect_fault, standardize_input
from core.utils.report_generator import generate_dcrm_json
from core.agents.recommendation import generate_recommendations
# Optional ViT Model (requires PyTorch compatibility)
try:
from core.models.vit_classifier import predict_dcrm_image, plot_resistance_for_vit
VIT_AVAILABLE = True
except Exception as e:
print(f"ViT Model not available: {e}")
VIT_AVAILABLE = False
predict_dcrm_image = None
plot_resistance_for_vit = None
# --- Configuration & CSS ---
st.set_page_config(page_title="DCRM Analyzer Pro", page_icon="โก", layout="wide")
def load_css():
st.markdown("""
<style>
/* Main Font */
html, body, [class*="css"] {
font-family: 'Segoe UI', Roboto, Helvetica, Arial, sans-serif;
}
/* Headers */
h1, h2, h3 {
color: #2c3e50;
font-weight: 600;
}
/* Metrics */
[data-testid="stMetricValue"] {
font-size: 2rem;
color: #2980b9;
}
/* Cards/Containers */
.stExpander {
border: 1px solid #e0e0e0;
border-radius: 8px;
box-shadow: 0 2px 4px rgba(0,0,0,0.05);
}
/* Sidebar */
section[data-testid="stSidebar"] {
background-color: #f8f9fa;
}
/* Status Indicators */
.status-pass { color: #27ae60; font-weight: bold; }
.status-warning { color: #f39c12; font-weight: bold; }
.status-fail { color: #c0392b; font-weight: bold; }
</style>
""", unsafe_allow_html=True)
def main():
load_css()
with st.sidebar:
st.title("โก DCRM Analyzer")
st.markdown("---")
mode = st.radio("Select Module", ["General Chat", "DCRM Analysis"], index=1)
# Segmentation method selector removed - using robust signal processing by default
st.markdown("---")
st.caption("Powered by Gemini 1.5 Flash")
st.header(f"{mode}")
uploaded_file = st.file_uploader("Upload DCRM Data (CSV)", type="csv")
if uploaded_file is not None:
try:
df = pd.read_csv(uploaded_file)
llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
if mode == "General Chat":
st.info("๐ก **Tip:** You can ask for graphs! Try: 'Plot the resistance in Zone 2'")
# Zone Enrichment Option
if st.checkbox("โจ Enrich Data with Zones"):
with st.spinner("Detecting Zones..."):
df, zones = enrich_data_with_zones(df, llm)
st.success("Data enriched with 'Zone' column!")
with st.expander("View Data"):
st.dataframe(df.head())
# Agent with Plotting Capabilities
agent = create_pandas_dataframe_agent(
llm,
df,
verbose=True,
allow_dangerous_code=True,
handle_parsing_errors=True,
prefix="""
You are a data analysis agent.
If the user asks for a plot or graph:
1. Use `plotly.graph_objects` (import as go) or `plotly.express` (import as px).
2. Create the figure object `fig`.
3. Display it using `st.plotly_chart(fig)`.
4. Do NOT use matplotlib.
5. Ensure you import streamlit as st inside the code execution.
"""
)
st.divider()
user_question = st.chat_input("Ask a question or request a plot...")
if user_question:
st.chat_message("user").write(user_question)
with st.spinner("Thinking..."):
try:
# We use the agent directly for everything now, as it handles both plotting and QA
response = agent.run(user_question)
with st.chat_message("assistant"):
st.write(response)
except Exception as e:
st.error(f"An error occurred: {str(e)}")
elif mode == "DCRM Analysis":
if st.button("๐ Run Advanced Analysis", type="primary", width='stretch'):
with st.spinner("Performing Kinematic Segmentation & Diagnostics..."):
try:
# 1. Deterministic KPI Calculation
kpi_results = calculate_kpis(df)
kpis = kpi_results['kpis']
# cbhi_score will be calculated later after AI and Phase analysis
# 1.5. Phase Segmentation (using AI Agent)
with st.spinner("Running AI-Based Phase Segmentation..."):
# Use analyze_dcrm_data for robust segmentation and status
phase_analysis_result = analyze_dcrm_data(df, llm)
# Extract zones for plotting
phase_to_zone_map = {
1: "zone_1_pre_contact",
2: "zone_2_arcing_engagement",
3: "zone_3_main_conduction",
4: "zone_4_parting",
5: "zone_5_final_open"
}
zones = {}
phase_timings = []
if 'phaseWiseAnalysis' in phase_analysis_result:
st.success("โ AI Segmentation complete!")
for phase in phase_analysis_result['phaseWiseAnalysis']:
p_num = phase.get('phaseNumber')
zone_key = phase_to_zone_map.get(p_num)
start_time = phase.get('startTime', 0)
end_time = phase.get('endTime', 0)
duration = end_time - start_time
if zone_key:
zones[zone_key] = {
'start_ms': start_time,
'end_ms': end_time
}
phase_timings.append({
"Phase": phase.get('name'),
"Start (ms)": start_time,
"End (ms)": end_time,
"Duration (ms)": duration
})
else:
st.warning("โ ๏ธ AI Segmentation returned no zones.")
# 2. Rule Engine & AI Agent Analysis
# Prepare KPIs for Raj modules (needs specific format)
# kpis dict from calculate_kpis_and_score is flat: {'closing_time': 45.15, ...}
# Raj modules expect: {'kpis': [{'name': 'Closing Time', 'value': ...}, ...]} or dict {'Closing Time (ms)': ...}
# Let's construct the dict format expected by raj_rule_engine (it handles dicts loosely but prefers specific keys)
# Mapping current KPIs to Raj expected keys
raj_kpis = {
"Closing Time (ms)": kpis.get('closing_time'),
"Opening Time (ms)": kpis.get('opening_time'),
"Contact Speed (m/s)": kpis.get('contact_speed'),
"DLRO Value (ยตฮฉ)": kpis.get('dlro'),
"Peak Resistance (ยตฮฉ)": kpis.get('peak_resistance'),
"Peak Close Coil Current (A)": kpis.get('peak_close_coil'),
"Peak Trip Coil 1 Current (A)": kpis.get('peak_trip_coil_1'),
"Peak Trip Coil 2 Current (A)": kpis.get('peak_trip_coil_2'),
"SF6 Pressure (bar)": kpis.get('sf6_pressure'),
"Ambient Temperature (ยฐC)": kpis.get('ambient_temp'),
"Main Wipe (mm)": kpis.get('main_wipe'),
"Arc Wipe (mm)": kpis.get('arc_wipe'),
"Contact Travel Distance (mm)": kpis.get('contact_travel')
}
# Also construct the list format for raj_ai_agent if needed, but detect_fault takes sample_kpis dict
# detect_fault expects: {'kpis': [{'name': '...', 'value': ...}]}
raj_ai_kpis = {
"kpis": [
{"name": "Closing Time", "unit": "ms", "value": kpis.get('closing_time')},
{"name": "Opening Time", "unit": "ms", "value": kpis.get('opening_time')},
{"name": "DLRO Value", "unit": "ยตฮฉ", "value": kpis.get('dlro')},
{"name": "Peak Resistance", "unit": "ยตฮฉ", "value": kpis.get('peak_resistance')},
{"name": "Contact Speed", "unit": "m/s", "value": kpis.get('contact_speed')},
{"name": "Peak Close Coil Current", "unit": "A", "value": kpis.get('peak_close_coil')},
{"name": "Peak Trip Coil 1 Current", "unit": "A", "value": kpis.get('peak_trip_coil_1')},
{"name": "Peak Trip Coil 2 Current", "unit": "A", "value": kpis.get('peak_trip_coil_2')},
{"name": "SF6 Pressure", "unit": "bar", "value": kpis.get('sf6_pressure')},
{"name": "Ambient Temperature", "unit": "ยฐC", "value": kpis.get('ambient_temp')}
]
}
# Prepare row values for Rule Engine
# We need 401 points. If df has > 401, take first 401. If < 401, pad?
# Assuming standard 400ms data at 1ms sample rate.
# If Time_ms is present, we can try to resample or just take the Resistance column values.
# raj_rule_engine.standardize_input handles this.
# Create a temp df for standardize_input
temp_df = df[['Resistance']].copy()
# Ensure we have enough data or handle it
if len(temp_df) < 401:
# Pad with last value
last_val = temp_df.iloc[-1, 0]
padding = pd.DataFrame({'Resistance': [last_val] * (401 - len(temp_df))})
temp_df = pd.concat([temp_df, padding], ignore_index=True)
std_df = standardize_input(temp_df)
row_values = std_df.iloc[0].values.tolist()
# --- Run Rule Engine ---
rule_engine_result = analyze_dcrm_advanced(row_values, raj_kpis)
# --- Run AI Agent ---
# detect_fault takes (df, sample_kpis)
# We can pass the original df, it calls standardize_input internally
ai_agent_result = detect_fault(df, raj_ai_kpis)
# --- Run ViT Model (Visual Inspection) ---
vit_result = None
# --- Run ViT Model (Visual Inspection) ---
vit_result = None
vit_plot_path = "temp_vit_plot.png"
# Always generate plot (doesn't require torch)
plot_generated = False
try:
if plot_resistance_for_vit(df, vit_plot_path):
plot_generated = True
except Exception as e:
print(f"ViT Plot generation failed: {e}")
# Try prediction if plot exists and VIT is available
if plot_generated and VIT_AVAILABLE:
try:
vit_class, vit_conf, vit_details = predict_dcrm_image(vit_plot_path)
if vit_class: # Ensure we got a result
vit_result = {
"class": vit_class,
"confidence": vit_conf,
"details": vit_details
}
except Exception as e:
print(f"ViT Prediction failed: {e}")
# ============================================
# SECTION 1: CBHI SCORE & KPIs
# ============================================
# Calculate CBHI using new logic
# Prepare phase_data for CBHI
cbhi_phase_data = {}
if 'phaseWiseAnalysis' in phase_analysis_result:
for phase in phase_analysis_result['phaseWiseAnalysis']:
p_name = f"Phase {phase.get('phaseNumber')}"
cbhi_phase_data[p_name] = {
"status": phase.get('status', 'Unknown'),
"confidence": phase.get('confidence', 0)
}
# Compute Score
cbhi_score = compute_cbhi(raj_ai_kpis['kpis'], ai_agent_result, cbhi_phase_data)
# --- Run Recommendation Agent ---
with st.spinner("Generating Maintenance Recommendations..."):
recommendations = generate_recommendations(
kpis=kpis,
cbhi_score=cbhi_score,
rule_faults=rule_engine_result.get("Fault_Detection", []),
ai_faults=ai_agent_result.get("Fault_Detection", []),
llm=llm
)
st.markdown("## ๐ Composite Breaker Health Index (CBHI)")
# CBHI Score Display
cbhi_col1, cbhi_col2, cbhi_col3 = st.columns([1, 2, 1])
with cbhi_col1:
st.markdown("") # Spacer
with cbhi_col2:
# Large centered score
if cbhi_score >= 90:
score_color = "#27ae60" # Green
status_text = "โ
Excellent Condition"
status_color = "success"
gradient = "linear-gradient(135deg, #11998e 0%, #38ef7d 100%)"
elif cbhi_score >= 75:
score_color = "#f39c12" # Orange
status_text = "โ ๏ธ Good - Minor Review Needed"
status_color = "warning"
gradient = "linear-gradient(135deg, #f093fb 0%, #f5576c 100%)"
else:
score_color = "#e74c3c" # Red
status_text = "๐จ Critical Attention Required"
status_color = "error"
gradient = "linear-gradient(135deg, #fa709a 0%, #fee140 100%)"
st.markdown(f"""
<div style='text-align: center; padding: 30px; background: {gradient}; border-radius: 20px; box-shadow: 0 8px 16px rgba(0,0,0,0.2);'>
<h1 style='color: white; font-size: 5rem; margin: 0; text-shadow: 2px 2px 4px rgba(0,0,0,0.3);'>{cbhi_score}</h1>
<p style='color: white; font-size: 1.4rem; margin: 10px 0; font-weight: 500;'>out of 100</p>
</div>
""", unsafe_allow_html=True)
st.markdown("<br>", unsafe_allow_html=True)
if status_color == "success":
st.success(status_text)
elif status_color == "warning":
st.warning(status_text)
else:
st.error(status_text)
with cbhi_col3:
st.markdown("") # Spacer
st.markdown("---")
# KPIs Section
st.markdown("### ๐ Key Performance Indicators")
st.caption("Measured parameters from DCRM test data")
# Timing & Motion
with st.container():
st.markdown("**โฑ๏ธ Timing & Motion**")
k1, k2, k3, k4 = st.columns(4)
k1.metric("Closing Time", f"{kpis['closing_time']} ms", help="Time taken for contacts to close")
k2.metric("Opening Time", f"{kpis['opening_time']} ms", help="Time taken for contacts to open")
k3.metric("Contact Speed", f"{kpis['contact_speed']} m/s", help="Average contact movement speed")
k4.metric("Contact Travel", f"{kpis['contact_travel']} mm", help="Total mechanical travel distance")
st.markdown("")
# Contact Health
with st.container():
st.markdown("**๐ Contact Health**")
k5, k6, k7, k8 = st.columns(4)
k5.metric("DLRO", f"{kpis['dlro']} ยตฮฉ", help="Dynamic Low Resistance - contact quality indicator")
k6.metric("Peak Resistance", f"{kpis['peak_resistance']} ยตฮฉ", help="Maximum resistance during operation")
k7.metric("Main Wipe", f"{kpis['main_wipe']} mm", help="Main contact wipe distance")
k8.metric("Arc Wipe", f"{kpis['arc_wipe']} mm", help="Arcing contact wipe distance")
st.markdown("")
# Electrical & Environment
with st.container():
st.markdown("**โก Electrical & Environment**")
k9, k10, k11, k12, k13 = st.columns(5)
k9.metric("Close Coil", f"{kpis['peak_close_coil']} A", help="Peak closing coil current")
k10.metric("Trip Coil 1", f"{kpis['peak_trip_coil_1']} A", help="Peak trip coil 1 current")
k11.metric("Trip Coil 2", f"{kpis['peak_trip_coil_2']} A", help="Peak trip coil 2 current")
k12.metric("Temperature", f"{kpis['ambient_temp']} ยฐC", help="Ambient temperature during test")
k13.metric("SF6 Pressure", f"{kpis['sf6_pressure']} bar", help="SF6 gas pressure")
st.markdown("---")
# ============================================
# SECTION 2: SEGMENTED GRAPH WITH ZONES
# ============================================
st.markdown("## ๐ DCRM Waveforms with Phase Segmentation")
st.caption("Programmatic phase detection using resistance, current, and travel thresholds")
# Generate the full JSON report first to get phase boundaries
with st.spinner("Analyzing phases and generating report..."):
full_report = generate_dcrm_json(
df=df,
kpis=kpis,
cbhi_score=cbhi_score,
rule_result=rule_engine_result,
ai_result=ai_agent_result,
llm=llm,
vit_result=vit_result,
phase_analysis_result=phase_analysis_result,
recommendations=recommendations
)
# Get phase boundaries and create zone timing table
phase_to_zone_map = {
"Pre-Contact Travel": "zone_1_pre_contact",
"Arcing Contact Engagement & Arc Initiation": "zone_2_arcing_engagement",
"Main Contact Conduction": "zone_3_main_conduction",
"Main Contact Parting & Arc Elongation": "zone_4_parting",
"Final Open State": "zone_5_final_open"
}
zones = {}
phase_timings = []
if 'phaseWiseAnalysis' in full_report:
for phase in full_report['phaseWiseAnalysis']:
phase_name = phase.get('name', '')
zone_key = phase_to_zone_map.get(phase_name)
start_time = phase.get('startTime', 0)
end_time = phase.get('endTime', 0)
duration = end_time - start_time
if zone_key:
zones[zone_key] = {
'start_ms': start_time,
'end_ms': end_time
}
phase_timings.append({
"Phase": phase_name,
"Start (ms)": start_time,
"End (ms)": end_time,
"Duration (ms)": duration
})
# Display graph
fig_main = create_dcrm_plot(df, zones)
st.plotly_chart(fig_main, width='stretch')
# Display phase timing table
if phase_timings:
st.markdown("#### โฑ๏ธ Phase Timing Breakdown")
timing_df = pd.DataFrame(phase_timings)
st.dataframe(timing_df, width='stretch', hide_index=True)
st.markdown("---")
# ============================================
# SECTION 3: PHASE-WISE DETAILED ANALYSIS
# ============================================
st.markdown("## ๐ Phase-Wise Detailed Analysis")
st.caption("AI-enhanced analysis of each operational phase with diagnostic verdicts")
if 'phaseWiseAnalysis' in full_report:
for phase in full_report['phaseWiseAnalysis']:
phase_num = phase.get('phaseNumber', 0)
phase_name = phase.get('name', 'Unknown Phase')
phase_title = phase.get('phaseTitle', phase_name)
phase_desc = phase.get('description', '')
confidence = phase.get('confidence', 0)
# Color code by phase
phase_colors = {
1: "#ff9800", # Orange
2: "#ff26bd", # Pink
3: "#4caf50", # Green
4: "#2196f3", # Blue
5: "#a629ff" # Purple
}
phase_color = phase_colors.get(phase_num, "#cccccc")
with st.expander(f"**Phase {phase_num}: {phase_name}** | Confidence: {confidence}%", expanded=False):
# Phase header
st.markdown(f"""
<div style='padding: 15px; background-color: {phase_color}22; border-left: 5px solid {phase_color}; border-radius: 5px; margin-bottom: 15px;'>
<h4 style='margin: 0; color: {phase_color};'>{phase_title}</h4>
<p style='margin: 5px 0 0 0; color: #666;'>{phase_desc}</p>
</div>
""", unsafe_allow_html=True)
# Event Synopsis
event_synopsis = phase.get('eventSynopsis', '')
if event_synopsis:
st.markdown("**๐ Event Synopsis**")
st.info(event_synopsis)
# Key Characteristics
characteristics = phase.get('details', {}).get('characteristics', [])
if characteristics:
st.markdown("**๐ Key Characteristics**")
for char in characteristics:
st.markdown(f"- {char}")
# Waveform Analysis
waveform = phase.get('waveformAnalysis', {})
if waveform:
st.markdown("**๐ Waveform Analysis**")
wave_col1, wave_col2, wave_col3 = st.columns(3)
with wave_col1:
st.markdown("**Resistance**")
st.caption(waveform.get('resistance', 'N/A'))
with wave_col2:
st.markdown("**Current**")
st.caption(waveform.get('current', 'N/A'))
with wave_col3:
st.markdown("**Travel**")
st.caption(waveform.get('travel', 'N/A'))
# Diagnostic Verdict
verdict = phase.get('diagnosticVerdict', '')
if verdict:
st.markdown("**๐ฉบ Diagnostic Verdict & Justification**")
st.success(verdict)
st.markdown("---")
# ============================================
# SECTION 4: AI AGENT ANALYSIS
# ============================================
st.markdown("## ๐ค AI Agent Analysis (Generative)")
st.caption("Deep learning-based fault detection using physics-informed signatures")
ai_faults = ai_agent_result.get("Fault_Detection", [])
if not ai_faults:
st.success("โ
**No AI-detected faults found.** System appears healthy based on generative AI analysis.")
else:
for idx, fault in enumerate(ai_faults):
name = fault.get("defect_name", "Unknown")
conf = fault.get("Confidence", "0%")
sev = fault.get("Severity", "Low")
desc = fault.get("description", "")
# Check if this is "No Secondary Defect Detected"
if "no secondary defect" in name.lower() or "no defect" in name.lower():
st.success(f"โ
**{name}**")
else:
# Color code by severity
if sev == "Critical":
icon = "๐ด"
border_color = "#e74c3c"
elif sev == "High":
icon = "๐ "
border_color = "#f39c12"
else:
icon = "๐ก"
border_color = "#f1c40f"
with st.expander(f"{icon} **{name}** | Confidence: {conf} | Severity: {sev}", expanded=(sev in ["Critical", "High"])):
st.markdown(f"""
<div style='padding: 10px; background-color: {border_color}11; border-left: 4px solid {border_color}; border-radius: 5px;'>
<p style='margin: 0;'><strong>AI Reasoning:</strong> {desc}</p>
</div>
""", unsafe_allow_html=True)
st.markdown("---")
# ============================================
# SECTION 5: RULE ENGINE ANALYSIS
# ============================================
st.markdown("## โ๏ธ Rule-Based Analysis (Deterministic)")
st.caption("Threshold-based fault detection using industry standards and expert knowledge")
re_faults = rule_engine_result.get("Fault_Detection", [])
if not re_faults:
st.success("โ
**No rule-based faults detected.** All parameters within acceptable ranges per industry standards.")
else:
for idx, fault in enumerate(re_faults):
name = fault.get("defect_name", "Unknown")
conf = fault.get("Confidence", "0%")
sev = fault.get("Severity", "Low")
desc = fault.get("description", "")
# Color code by severity
if sev == "Critical":
icon = "๐ด"
border_color = "#e74c3c"
elif sev == "High":
icon = "๐ "
border_color = "#f39c12"
else:
icon = "๐ก"
border_color = "#f1c40f"
with st.expander(f"{icon} **{name}** | Confidence: {conf} | Severity: {sev}", expanded=(sev in ["Critical", "High"])):
st.markdown(f"""
<div style='padding: 10px; background-color: {border_color}11; border-left: 4px solid {border_color}; border-radius: 5px;'>
<p style='margin: 0;'><strong>Rule Engine Reasoning:</strong> {desc}</p>
</div>
""", unsafe_allow_html=True)
st.markdown("---")
# ============================================
# SECTION 6: ViT MODEL ANALYSIS
# ============================================
st.markdown("## ๐ธ Visual Pattern Recognition (ViT Model)")
st.caption("Computer vision-based classification using Vision Transformer neural network")
vit_col1, vit_col2 = st.columns([1, 2])
with vit_col1:
if plot_generated:
st.image(vit_plot_path, caption="Resistance Curve Input", width='stretch')
else:
st.warning("โ ๏ธ Could not generate visualization.")
with vit_col2:
if vit_result:
pred_class = vit_result['class']
pred_conf = vit_result['confidence'] * 100
st.markdown("### ๐ฏ Prediction Results (ViT + Gemini Ensemble)")
metric_col1, metric_col2 = st.columns(2)
with metric_col1:
st.metric("Predicted Condition", pred_class)
with metric_col2:
st.metric("Ensemble Confidence", f"{pred_conf:.1f}%")
st.markdown("---")
# Show Breakdown if available
details = vit_result.get("details", {})
if details and details.get("gemini_probs"):
with st.expander("๐ Detailed Ensemble Breakdown", expanded=True):
st.caption("Comparison of ViT Model and Gemini Expert Analysis")
# Prepare data for chart
vit_probs = details.get("vit_probs", {})
gemini_probs = details.get("gemini_probs", {})
ensemble_scores = details.get("ensemble_scores", {})
chart_data = []
for cls, score in ensemble_scores.items():
chart_data.append({
"Class": cls,
"ViT": vit_probs.get(cls, 0),
"Gemini": gemini_probs.get(cls, 0),
"Ensemble": score
})
if chart_data:
st.bar_chart(
pd.DataFrame(chart_data).set_index("Class")[["ViT", "Gemini", "Ensemble"]]
)
if pred_class == "Healthy":
st.success("โ
**Visual pattern matches healthy signature.**")
else:
st.warning(f"โ ๏ธ **Visual pattern suggests: {pred_class}**")
elif not VIT_AVAILABLE:
st.info("โน๏ธ **ViT Model Unavailable**\n\nThe Vision Transformer model requires PyTorch.")
else:
st.warning("โ ๏ธ **Prediction failed.** Model may not be loaded correctly or input image is invalid.")
st.markdown("---")
# ============================================
# SECTION 7: ADDITIONAL DIAGNOSTICS
# ============================================
st.markdown("## ๐ฌ Additional Diagnostic Plots")
st.caption("Supplementary visualizations for detailed analysis")
diag_col1, diag_col2 = st.columns(2)
with diag_col1:
st.markdown("### Contact Velocity Profile")
st.caption("Derivative of travel - indicates mechanical performance")
fig_vel = create_velocity_plot(df)
st.plotly_chart(fig_vel, width='stretch')
with diag_col2:
st.markdown("### Resistance Detail (Log Scale)")
st.caption("Logarithmic view reveals subtle resistance variations")
fig_res = create_resistance_zoom_plot(df)
st.plotly_chart(fig_res, width='stretch')
st.markdown("---")
st.markdown("---")
# ============================================
# SECTION 8: RECOMMENDATIONS & PREDICTIONS
# ============================================
st.markdown("## ๐ ๏ธ Recommendations & Future Fault Predictions")
st.caption("AI-generated maintenance actions and predictive failure analysis")
# Maintenance Actions
if recommendations.get("maintenanceActions"):
st.markdown("### ๐ง Recommended Maintenance Actions")
for group in recommendations["maintenanceActions"]:
priority = group.get("priority", "Priority")
color = group.get("color", "#333")
bg_color = group.get("bgColor", "#eee")
st.markdown(f"""
<div style='background-color: {bg_color}; padding: 10px; border-radius: 5px; border-left: 5px solid {color}; margin-bottom: 10px;'>
<h4 style='color: {color}; margin: 0;'>{priority}</h4>
</div>
""", unsafe_allow_html=True)
for action in group.get("actions", []):
with st.expander(f"**{action.get('title')}**"):
st.markdown(f"**Justification:** {action.get('justification')}")
st.markdown(f"**Timeline:** {action.get('timeline')}")
if action.get("whatToLookFor"):
st.markdown("**What to Look For:**")
for item in action["whatToLookFor"]:
st.markdown(f"- {item}")
st.markdown("---")
# Future Faults
if recommendations.get("futureFaultsPdf"):
st.markdown("### ๐ฎ Future Fault Predictions")
cols = st.columns(len(recommendations["futureFaultsPdf"]))
for idx, fault in enumerate(recommendations["futureFaultsPdf"]):
with cols[idx % 4]: # Wrap around if many
prob = fault.get("probability", 0)
color = fault.get("color", "#333")
st.markdown(f"""
<div style='text-align: center; border: 1px solid #ddd; border-radius: 10px; padding: 15px; height: 100%;'>
<h3 style='color: {color};'>{prob}%</h3>
<p style='font-weight: bold;'>{fault.get('fault')}</p>
<p style='font-size: 0.9em; color: #666;'>{fault.get('timeline')}</p>
<hr>
<p style='font-size: 0.8em; text-align: left;'>{fault.get('evidence')}</p>
</div>
""", unsafe_allow_html=True)
st.markdown("---")
# RUL Estimation
ai_verdict = full_report.get("aiVerdict", {})
rul_est = ai_verdict.get("rulEstimate")
uncertainty = ai_verdict.get("uncertainty")
if rul_est:
st.markdown("### โณ Remaining Useful Life (RUL) Estimation")
st.caption("Estimated remaining operations based on current degradation")
rul_col1, rul_col2 = st.columns(2)
with rul_col1:
st.metric("RUL Estimate", rul_est, delta=uncertainty, delta_color="off")
with rul_col2:
st.info(f"**Uncertainty:** {uncertainty}\n\nThis estimate considers contact wear, timing deviations, and coil health.")
st.markdown("---")
# AI Strategic Advice
ai_advice = ai_verdict.get("aiAdvice", [])
if ai_advice:
st.markdown("## ๐ง AI Strategic Advisory")
st.caption("Expert recommendations based on engineering analysis")
for advice in ai_advice:
color = advice.get("color", "#333")
title = advice.get("title", "Recommendation")
desc = advice.get("description", "")
impact = advice.get("expectedImpact", "")
priority = advice.get("priority", "Medium")
with st.expander(f"**{priority}**: {title}", expanded=(priority=="Critical")):
st.markdown(f"""
<div style='border-left: 5px solid {color}; padding-left: 15px;'>
<p><strong>Rationale:</strong> {desc}</p>
<p><strong>Expected Impact:</strong> {impact}</p>
</div>
""", unsafe_allow_html=True)
effects = advice.get("effectAnalysis", {})
if effects:
e_col1, e_col2 = st.columns(2)
with e_col1:
if effects.get("shortTerm"):
st.markdown("**Short Term Benefits:**")
for item in effects["shortTerm"]:
st.markdown(f"- {item}")
with e_col2:
if effects.get("longTerm"):
st.markdown("**Long Term Benefits:**")
for item in effects["longTerm"]:
st.markdown(f"- {item}")
st.markdown("---")
# ============================================
# SECTION 9: DOWNLOAD REPORT
# ============================================
st.markdown("## ๐ฅ Export Analysis Report")
st.caption("Download complete JSON report with all metrics, phase data, and AI insights")
col_download1, col_download2, col_download3 = st.columns([1, 2, 1])
with col_download1:
st.markdown("") # Spacer
with col_download2:
st.download_button(
label="๐ Download Full Report (JSON)",
data=json.dumps(full_report, indent=2),
file_name=f"dcrm_analysis_report_{pd.Timestamp.now().strftime('%Y%m%d_%H%M%S')}.json",
mime="application/json",
width='stretch'
)
st.caption("โ
Includes: KPIs, CBHI score, phase analysis, AI verdicts, rule engine results, and ViT predictions")
with col_download3:
st.markdown("") # Spacer
except Exception as e:
st.error(f"Analysis failed: {str(e)}")
st.expander("Debug Info").write(content if 'content' in locals() else "No content")
else:
st.info("Click 'Run Advanced Analysis' to start.")
with st.expander("Preview Raw Data"):
st.dataframe(df.head())
except Exception as e:
st.error(f"Error reading CSV file: {str(e)}")
if __name__ == "__main__":
main()
|