Update app.py

app.py

@@ -1,11 +1,11 @@
-# app.py - FactoryGPT 5.0: Role-Differentiated Technical Assistant
-
 import streamlit as st
 import pandas as pd
 import numpy as np
+import matplotlib.pyplot as plt
 from sentence_transformers import SentenceTransformer
 from transformers import pipeline
 from sklearn.ensemble import IsolationForest
+from io import BytesIO
 
 # App Config
 st.set_page_config(
@@ -23,7 +23,8 @@ st.markdown("""
         color: #f0f0f0;
     }
     .stTextInput>div>div>input,
-    .stSelectbox>div>div>div>div {
+    .stSelectbox>div>div>div>div,
+    .stFileUploader>div>div {
         background-color: #1a1c23;
         color: #fff;
     }
@@ -57,7 +58,7 @@ if uploaded_file:
     st.markdown("### 📊 Sensor Data Snapshot")
     st.dataframe(df.head(), use_container_width=True)
 
-    # Vector Chunking for RAG
+    # Run Vector Chunking
     def convert_to_chunks(df):
         return [f"[Entry {i}] " + ", ".join([f"{col}: {row[col]:.2f}" for col in numeric_cols]) for i, row in df.iterrows()]
 
@@ -67,15 +68,82 @@ if uploaded_file:
         st.session_state.chunks = chunks
         st.session_state.embeddings = embeddings
 
-    # Predictive Anomaly Detection
-    st.markdown("### 🔎 Machine Health Assessment")
+    # Detect Anomalies
     iso = IsolationForest(contamination=0.02)
     labels = iso.fit_predict(df[numeric_cols])
     df['status'] = ['❌ Fault Detected' if x == -1 else '✅ Healthy' for x in labels]
     df['maintenance_flag'] = ['🔧 Inspect Required' if x == -1 else '🟢 Stable' for x in labels]
+
+    st.markdown("### 🔎 Machine Health Assessment")
     st.dataframe(df[['status', 'maintenance_flag'] + numeric_cols].head(), use_container_width=True)
 
-    # Role-based Response Logic
+    # Health Overview Chart
+    st.markdown("### 📈 Machine Health Summary")
+    status_counts = df['status'].value_counts()
+    fig1, ax1 = plt.subplots()
+    ax1.bar(status_counts.index, status_counts.values, color=["red", "green"])
+    ax1.set_title("Machine Health Status")
+    ax1.set_ylabel("Occurrences")
+    ax1.set_facecolor("#0f1117")
+    ax1.tick_params(colors='white')
+    ax1.title.set_color('white')
+    ax1.yaxis.label.set_color('white')
+    for spine in ax1.spines.values():
+        spine.set_edgecolor('white')
+    st.pyplot(fig1)
+
+    # Parameter Trend Chart
+    st.markdown("### 📉 Sensor Parameter Trend")
+    time_col = st.selectbox("🕒 Select Time Column (optional)", ["None"] + list(df.columns))
+    param = st.selectbox("📌 Choose a Parameter to Visualize", numeric_cols)
+
+    if time_col != "None":
+        df = df.sort_values(by=time_col)
+
+    fig2, ax2 = plt.subplots()
+    x_vals = df[time_col] if time_col != "None" else df.index
+    ax2.plot(x_vals, df[param], label=param, color="skyblue")
+    anomaly_x = x_vals[df['status'] == '❌ Fault Detected']
+    anomaly_y = df[param][df['status'] == '❌ Fault Detected']
+    ax2.scatter(anomaly_x, anomaly_y, color='red', label='Fault Detected', zorder=5)
+    ax2.set_title(f"{param} Over Time")
+    ax2.set_xlabel("Time" if time_col != "None" else "Index")
+    ax2.set_ylabel(param)
+    ax2.legend()
+    ax2.set_facecolor("#0f1117")
+    ax2.tick_params(colors='white')
+    ax2.title.set_color('white')
+    ax2.xaxis.label.set_color('white')
+    ax2.yaxis.label.set_color('white')
+    for spine in ax2.spines.values():
+        spine.set_edgecolor('white')
+    st.pyplot(fig2)
+
+    # Parameter List
+    st.markdown("### 🧪 Parameters Monitored")
+    st.markdown(", ".join(numeric_cols))
+
+    # Export Anomalies
+    st.markdown("### 📤 Export Flagged Anomalies")
+    anomalies_df = df[df['status'] == '❌ Fault Detected']
+    buffer = BytesIO()
+    anomalies_df.to_csv(buffer, index=False)
+    st.download_button(
+        label="⬇️ Download Fault Records",
+        data=buffer.getvalue(),
+        file_name="fault_records.csv",
+        mime="text/csv"
+    )
+
+    # Fault Correlation if Metadata Exists
+    st.markdown("### 🏷️ Fault Distribution by Machine/Component")
+    metadata_cols = [col for col in df.columns if 'machine' in col.lower() or 'component' in col.lower()]
+    for col in metadata_cols:
+        st.markdown(f"**{col.capitalize()} – Fault Frequency**")
+        fault_counts = df[df['status'] == '❌ Fault Detected'][col].value_counts()
+        st.bar_chart(fault_counts)
+
+    # Role-based Technical Assistant
     st.markdown("### 💬 Technical Assistant by Role")
     roles = {
         "Operator": {
@@ -114,7 +182,7 @@ if uploaded_file:
         top_idxs = np.argsort(sims)[-5:][::-1]
         context = "\n".join([st.session_state.chunks[i] for i in top_idxs])
 
-        # Build Prompt
+        # Prompt Assembly
         system_instruction = (
             f"ROLE: {role}\n"
             f"RESPONSIBILITIES: {roles[role]['description']}\n"