Update app.py

app.py

@@ -1,4 +1,4 @@
-# app.py - FactoryRAG+: Fancy Lite Version with Animation & Chatbot Only
+# app.py - FactoryRAG+: AI Assistant with Real-Time Functional Status by Role
 
 import streamlit as st
 import pandas as pd
@@ -7,41 +7,54 @@ from sentence_transformers import SentenceTransformer
 from transformers import pipeline
 from sklearn.ensemble import IsolationForest
 
-# Page config with emoji + layout
+# Page config
 st.set_page_config(page_title="FactoryRAG+ Assistant", page_icon="🧠", layout="wide")
 
-# Animated header
+# Custom dark theme styling
 st.markdown("""
-    <h1 style='text-align: center; color: #3498db; font-size: 48px;'>
-        🏭 FactoryRAG+ <span style="font-size: 28px;">| AI Assistant for Smart Sensors</span>
-    </h1>
-    <hr style='border-top: 2px solid #bbb;' />
+    <style>
+    html, body, [class*="css"]  {
+        font-family: 'Segoe UI', sans-serif;
+        background-color: #0f1117;
+        color: #f0f0f0;
+    }
+    .stTextInput>div>div>input,
+    .stSelectbox>div>div>div>div {
+        background-color: #1a1c23;
+        color: #fff;
+    }
+    .stDataFrame .blank {
+        background-color: #0f1117 !important;
+    }
+    </style>
+""", unsafe_allow_html=True)
+
+# Header
+st.markdown("""
+    <div style='text-align: center;'>
+        <h1 style='color: #58a6ff;'>🏭 FactoryRAG+ Assistant</h1>
+        <p style='color: #bbb;'>AI-Powered Digital Twin | Real-Time Sensor Health</p>
+        <hr style='border-top: 2px solid #888;'>
+    </div>
 """, unsafe_allow_html=True)
 
 # Load models
 EMBED_MODEL = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
 GEN_MODEL = pipeline('text2text-generation', model='google/flan-t5-base')
 
-# Sidebar upload
-st.sidebar.markdown("### 📂 Upload Sensor Log")
-uploaded_file = st.sidebar.file_uploader("Upload a CSV sensor file", type=["csv"])
+# File upload
+uploaded_file = st.sidebar.file_uploader("📂 Upload your sensor CSV", type=["csv"])
 
 if uploaded_file:
     df = pd.read_csv(uploaded_file)
     numeric_cols = df.select_dtypes(include=np.number).columns.tolist()
-    st.success("✅ Data uploaded successfully!")
+    st.success("✅ Sensor log loaded!")
 
-    # Animated section
-    st.markdown("### 🔍 Sensor Log Preview")
-    st.dataframe(df.head())
+    st.markdown("### 🧾 Sensor Log Preview")
+    st.dataframe(df.head(), use_container_width=True)
 
-    # Convert to text chunks
     def convert_to_chunks(df):
-        chunks = []
-        for idx, row in df.iterrows():
-            log_text = f"[Log {idx}] " + ", ".join([f"{col}: {row[col]:.2f}" for col in numeric_cols])
-            chunks.append(log_text)
-        return chunks
+        return [f"[Log {i}] " + ", ".join([f"{col}: {row[col]:.2f}" for col in numeric_cols]) for i, row in df.iterrows()]
 
     if 'chunks' not in st.session_state or 'embeddings' not in st.session_state:
         chunks = convert_to_chunks(df)
@@ -49,26 +62,28 @@ if uploaded_file:
         st.session_state.chunks = chunks
         st.session_state.embeddings = embeddings
 
-    # --- Anomaly Detection ---
-    st.markdown("### 🚨 Real-Time Anomaly Scanner")
+    # Condition status based on Isolation Forest
+    st.markdown("### ⚙️ Equipment Condition Status")
     iso = IsolationForest(contamination=0.02)
     labels = iso.fit_predict(df[numeric_cols])
-    df['anomaly'] = ['❌ Anomaly' if x == -1 else '✅ Normal' for x in labels]
-    st.dataframe(df[df['anomaly'].str.contains("❌")].head())
+    df['status'] = ['❌ No Function' if x == -1 else '✅ Functional' for x in labels]
+    df['maintenance'] = ['🔧 Needs Maintenance' if x == -1 else '🟢 Stable' for x in labels]
+    st.dataframe(df[['status', 'maintenance'] + numeric_cols].head(), use_container_width=True)
 
-    # --- Chatbot Assistant ---
-    st.markdown("### 💬 Ask FactoryGPT")
+    # Role-based chatbot
+    st.markdown("### 💬 Real-Time Role-Based Chat Assistant")
     roles = {
-        "Operator": "You are a machine operator. Provide practical insights and safety warnings.",
-        "Maintenance": "You are a maintenance technician. Suggest inspections and likely causes of sensor anomalies.",
-        "Engineer": "You are a control systems engineer. Offer analytical interpretations and system-level advice."
+        "Operator": "You are a machine operator. Check if equipment is running properly. If not, flag it immediately.",
+        "Maintenance": "You are a maintenance technician. Assess faulty logs and provide service insights.",
+        "Engineer": "You are a systems engineer. Offer data-backed advice and failure diagnostics."
     }
-    role = st.selectbox("👷 Select your role", list(roles.keys()))
+
+    role = st.selectbox("👷 Choose your role", list(roles.keys()))
 
     if 'chat_history' not in st.session_state:
         st.session_state.chat_history = []
 
-    user_input = st.text_input("🗨️ Ask about the sensor log...", key="chat_input")
+    user_input = st.text_input("🗨️ Ask FactoryGPT about machine status or maintenance needs")
 
     if user_input:
         query_vec = EMBED_MODEL.encode([user_input])[0]
@@ -76,7 +91,7 @@ if uploaded_file:
         top_idxs = np.argsort(sims)[-3:][::-1]
         context = "\n".join([st.session_state.chunks[i] for i in top_idxs])
         system_prompt = roles[role]
-        full_prompt = f"{system_prompt}\n\nSensor Context:\n{context}\n\nUser Question: {user_input}"
+        full_prompt = f"{system_prompt}\n\nSensor Log Context:\n{context}\n\nUser Question: {user_input}"
         reply = GEN_MODEL(full_prompt, max_length=256)[0]['generated_text']
 
         st.session_state.chat_history.append((f"👤 You ({role})", user_input))
@@ -86,4 +101,4 @@ if uploaded_file:
         st.markdown(f"<div style='margin-bottom: 10px;'><b>{speaker}:</b> {msg}</div>", unsafe_allow_html=True)
 
 else:
-    st.info("👈 Please upload a sensor CSV file to begin.")
+    st.info("👈 Upload a CSV file with sensor logs to begin.")