Update app.py

app.py

@@ -1,4 +1,4 @@
-# app.py - FactoryGPT 5.0: Predictive Maintenance + Role Chat (No 3D Map)
+# app.py - FactoryGPT 5.0: Predictive Maintenance with Technical Expert GPT
 
 import streamlit as st
 import pandas as pd
@@ -9,7 +9,7 @@ from sklearn.ensemble import IsolationForest
 
 # Page setup
 st.set_page_config(
-    page_title="FactoryGPT 5.0 – Predict, Perfect, and Connect",
+    page_title="FactoryGPT 5.0 – Predictive Maintenance Expert",
     page_icon="🧠",
     layout="wide"
 )
@@ -33,20 +33,20 @@ st.markdown("""
     </style>
 """, unsafe_allow_html=True)
 
-# Title
+# Header
 st.markdown("""
     <div style='text-align: center;'>
-        <h1 style='color: #58a6ff;'>🏭 FactoryGPT 5.0 – Predict, Perfect, and Connect</h1>
-        <p style='color: #bbb;'>AI-Powered Predictive Maintenance | Human-in-the-Loop Decision Support</p>
+        <h1 style='color: #58a6ff;'>🏭 FactoryGPT 5.0 – Predictive Maintenance Assistant</h1>
+        <p style='color: #bbb;'>Sensor-Driven Diagnostics | Role-Based Expert Assistant | Industry 5.0 Ready</p>
         <hr style='border-top: 2px solid #888;'>
     </div>
 """, unsafe_allow_html=True)
 
-# Load models
+# Load NLP models
 EMBED_MODEL = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
 GEN_MODEL = pipeline('text2text-generation', model='google/flan-t5-base')
 
-# File upload
+# Upload data
 uploaded_file = st.sidebar.file_uploader("📂 Upload your sensor CSV", type=["csv"])
 
 if uploaded_file:
@@ -54,12 +54,12 @@ if uploaded_file:
     numeric_cols = df.select_dtypes(include=np.number).columns.tolist()
     st.success("✅ Sensor log loaded!")
 
-    st.markdown("### 🧾 Sensor Log Preview")
+    st.markdown("### 📊 Sensor Data Preview")
     st.dataframe(df.head(), use_container_width=True)
 
-    # RAG Embeddings
+    # Prepare vector chunks
     def convert_to_chunks(df):
-        return [f"[Log {i}] " + ", ".join([f"{col}: {row[col]:.2f}" for col in numeric_cols]) for i, row in df.iterrows()]
+        return [f"[Entry {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)
@@ -67,43 +67,61 @@ if uploaded_file:
         st.session_state.chunks = chunks
         st.session_state.embeddings = embeddings
 
-    # Equipment condition via Isolation Forest
-    st.markdown("### ⚙️ Equipment Condition Status")
+    # Predict machine status
+    st.markdown("### 🔍 Equipment Status Prediction")
     iso = IsolationForest(contamination=0.02)
     labels = iso.fit_predict(df[numeric_cols])
-    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)
+    df['status'] = ['❌ Faulty' if x == -1 else '✅ OK' for x in labels]
+    df['maintenance_flag'] = ['🔧 Action Required' if x == -1 else '🟢 Normal' for x in labels]
+    st.dataframe(df[['status', 'maintenance_flag'] + numeric_cols].head(), use_container_width=True)
 
-    # Role-based Assistant
-    st.markdown("### 💬 Role-Based Chat Assistant")
+    # Role-based assistant
+    st.markdown("### 💬 Role-Based Technical Assistant")
     roles = {
-        "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."
+        "Operator": (
+            "You are a senior machine operator. Based on sensor logs, identify abnormal conditions in real-time operation. "
+            "Explain deviations clearly and suggest next steps to the floor team."
+        ),
+        "Maintenance": (
+            "You are a certified maintenance technician. Analyze anomalous sensor readings, reference maintenance schedules, "
+            "and recommend immediate actions or predictive interventions with justification."
+        ),
+        "Engineer": (
+            "You are a reliability and control systems engineer. Provide a detailed root cause analysis from the sensor logs, "
+            "explain what failure modes may be emerging (e.g., torque drift, PID lag), and propose technical mitigations."
+        )
     }
 
-    role = st.selectbox("👷 Choose your role", list(roles.keys()))
+    role = st.selectbox("👤 Select your role for technical insights", list(roles.keys()))
 
     if 'chat_history' not in st.session_state:
         st.session_state.chat_history = []
 
-    user_input = st.text_input("🗨️ Ask FactoryGPT about machine status or maintenance needs")
+    user_input = st.text_input("🧠 Ask FactoryGPT a technical question about the machine behavior")
 
     if user_input:
+        # Retrieve relevant context from logs
         query_vec = EMBED_MODEL.encode([user_input])[0]
         sims = np.dot(st.session_state.embeddings, query_vec)
-        top_idxs = np.argsort(sims)[-3:][::-1]
+        top_idxs = np.argsort(sims)[-5:][::-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 Log Context:\n{context}\n\nUser Question: {user_input}"
-        reply = GEN_MODEL(full_prompt, max_length=256)[0]['generated_text']
 
+        # Prompt Engineering
+        system_prompt = (
+            f"ROLE CONTEXT: {roles[role]}\n\n"
+            f"DATA CONTEXT:\n{context}\n\n"
+            f"INSTRUCTION:\nPlease respond with a technical explanation that includes sensor-based reasoning, terminology, and if possible, an action plan.\n"
+        )
+        full_prompt = f"{system_prompt}\nUser Query: {user_input}"
+        reply = GEN_MODEL(full_prompt, max_length=350)[0]['generated_text']
+
+        # Record conversation
         st.session_state.chat_history.append((f"👤 You ({role})", user_input))
         st.session_state.chat_history.append(("🤖 FactoryGPT", reply))
 
+    # Show chat
     for speaker, msg in st.session_state.chat_history[-10:]:
-        st.markdown(f"<div style='margin-bottom: 10px;'><b>{speaker}:</b> {msg}</div>", unsafe_allow_html=True)
+        st.markdown(f"<div style='margin-bottom: 10px;'><b>{speaker}:</b><br>{msg}</div>", unsafe_allow_html=True)
 
 else:
-    st.info("👈 Upload a CSV file with sensor logs to begin.")
+    st.info("👈 Please upload a CSV file containing numeric sensor logs to begin analysis.")