Update app.py

app.py

@@ -1,52 +1,46 @@
-# app.py - FactoryGPT: FactoryGPT Humanization for the Factories of the Future in Industry 5.0 – Predict, Perfect, and Connect
+# app.py - FactoryRAG+: Fancy Lite Version with Animation & Chatbot Only
 
 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
-import base64
-from io import BytesIO
-from fpdf import FPDF
 
-st.set_page_config(page_title="FactoryRAG+ - Smart Sensor Twin", layout="wide")
-st.title("🏭 FactoryRAG+: Smart Dashboard with AI Monitoring, PDF Reporting & Digital Twin")
+# Page config with emoji + layout
+st.set_page_config(page_title="FactoryRAG+ Assistant", page_icon="🧠", layout="wide")
+
+# Animated header
+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;' />
+""", 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')
 
-# Upload data
-uploaded_file = st.sidebar.file_uploader("📂 Upload your condition monitoring CSV", type=["csv"])
+# Sidebar upload
+st.sidebar.markdown("### 📂 Upload Sensor Log")
+uploaded_file = st.sidebar.file_uploader("Upload a CSV sensor file", type=["csv"])
 
 if uploaded_file:
     df = pd.read_csv(uploaded_file)
     numeric_cols = df.select_dtypes(include=np.number).columns.tolist()
-    st.success("✅ Data loaded successfully!")
+    st.success("✅ Data uploaded successfully!")
 
-    st.subheader("📊 Sensor Snapshot")
+    # Animated section
+    st.markdown("### 🔍 Sensor Log Preview")
     st.dataframe(df.head())
 
-    # --- Multi-signal Dashboard (Collapsible) ---
-    with st.expander("📈 Expand to View Sensor Dashboard", expanded=False):
-        selected_cols = st.multiselect("Select signals to visualize", numeric_cols, default=numeric_cols[:3])
-        fig, ax = plt.subplots(len(selected_cols), 1, figsize=(8, 2 * len(selected_cols)))
-        if len(selected_cols) == 1:
-            ax = [ax]
-        for i, col in enumerate(selected_cols):
-            ax[i].plot(df[col], label=col)
-            ax[i].set_ylabel(col)
-            ax[i].legend()
-        st.pyplot(fig)
-
-    # --- Convert Logs to Chunks ---
+    # Convert to text chunks
     def convert_to_chunks(df):
         chunks = []
         for idx, row in df.iterrows():
-            sentence = f"[Log {idx}] " + ", ".join([f"{col}: {row[col]:.2f}" for col in numeric_cols])
-            chunks.append(sentence)
+            log_text = f"[Log {idx}] " + ", ".join([f"{col}: {row[col]:.2f}" for col in numeric_cols])
+            chunks.append(log_text)
         return chunks
 
     if 'chunks' not in st.session_state or 'embeddings' not in st.session_state:
@@ -56,46 +50,25 @@ if uploaded_file:
         st.session_state.embeddings = embeddings
 
     # --- Anomaly Detection ---
-    st.subheader("🚨 Anomaly Detection (Isolation Forest)")
+    st.markdown("### 🚨 Real-Time Anomaly Scanner")
     iso = IsolationForest(contamination=0.02)
-    anomaly_labels = iso.fit_predict(df[numeric_cols])
-    df['anomaly'] = ['❌' if x == -1 else '' for x in anomaly_labels]
-    st.dataframe(df[df['anomaly'] == '❌'].head(5))
-
-    # --- Digital Twin Summary ---
-    st.subheader("🧪 Digital Twin Summary")
-    twin_report = ""
-    for col in selected_cols:
-        max_v = df[col].max()
-        min_v = df[col].min()
-        mean_v = df[col].mean()
-        twin_report += f"{col}\n→ Max: {max_v:.2f}, Min: {min_v:.2f}, Avg: {mean_v:.2f}\n\n"
-    st.code(twin_report)
-
-    # --- PDF Export ---
-    st.subheader("📤 Export Digital Twin Report as PDF")
-    pdf = FPDF()
-    pdf.add_page()
-    pdf.set_font("Arial", size=12)
-    pdf.multi_cell(0, 10, f"FactoryRAG+ Digital Twin Report\n\nSelected Signals: {', '.join(selected_cols)}\n\n" + twin_report)
-    pdf_bytes = pdf.output(dest='S').encode('latin1')
-    b64 = base64.b64encode(pdf_bytes).decode()
-    href = f'<a href="data:application/octet-stream;base64,{b64}" download="digital_twin_report.pdf">📄 Download PDF Report</a>'
-    st.markdown(href, unsafe_allow_html=True)
-
-    # --- Role-based Factory Assistant Chatbot ---
-    st.subheader("💬 Factory Assistant Chat")
+    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())
+
+    # --- Chatbot Assistant ---
+    st.markdown("### 💬 Ask FactoryGPT")
     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."
     }
-    role = st.selectbox("👤 Choose your role: Operator, Maintenance, or Engineer", list(roles.keys()))
+    role = st.selectbox("👷 Select your role", list(roles.keys()))
 
     if 'chat_history' not in st.session_state:
         st.session_state.chat_history = []
 
-    user_input = st.text_input("Ask FactoryGPT anything (based on uploaded sensor logs):", key="chat_input")
+    user_input = st.text_input("🗨️ Ask about the sensor log...", key="chat_input")
 
     if user_input:
         query_vec = EMBED_MODEL.encode([user_input])[0]
@@ -106,11 +79,11 @@ if uploaded_file:
         full_prompt = f"{system_prompt}\n\nSensor 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(("You", user_input))
-        st.session_state.chat_history.append((f"{role} - FactoryGPT", reply))
+        st.session_state.chat_history.append((f"👤 You ({role})", user_input))
+        st.session_state.chat_history.append(("🤖 FactoryGPT", reply))
 
     for speaker, msg in st.session_state.chat_history[-10:]:
-        st.markdown(f"**{speaker}:** {msg}")
+        st.markdown(f"<div style='margin-bottom: 10px;'><b>{speaker}:</b> {msg}</div>", unsafe_allow_html=True)
 
 else:
-    st.info("👈 Upload a sensor log CSV file to explore digital twin analysis, chatbot Q&A, waveform charts, anomaly detection, and PDF export.")
+    st.info("👈 Please upload a sensor CSV file to begin.")