app.py

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(
    page_title="FactoryGPT 5.0 – Maintenance Dashboard",
    page_icon="🧠",
    layout="wide"
)

# Load NLP + Anomaly Models
EMBED_MODEL = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
GEN_MODEL = pipeline('text2text-generation', model='google/flan-t5-base')

# Optional: Units per parameter
unit_map = {
    "temperature": "°C",
    "vibration": "mm/s",
    "pressure": "bar",
    "current": "A",
    "voltage": "V",
    "speed": "RPM"
}

# Style
st.markdown("""
    <style>
    html, body, [class*="css"] {
        font-family: 'Segoe UI', sans-serif;
        background-color: #0f1117;
        color: #f0f0f0;
    }
    .card {
        background-color: #1a1c23;
        padding: 1rem;
        border-radius: 10px;
        margin-bottom: 1rem;
        box-shadow: 0 0 8px rgba(88,166,255,0.2);
    }
    </style>
""", unsafe_allow_html=True)

# Header
st.markdown("""
    <div style='text-align: center;'>
        <h1 style='color: #58a6ff;'>🏭 FactoryGPT 5.0 – Technical Maintenance Dashboard</h1>
        <p style='color: #bbb;'>Anomaly Monitoring • Parameter Trends • Role-Based Intelligence</p>
        <hr style='border-top: 2px solid #888;'>
    </div>
""", unsafe_allow_html=True)

# File Upload
uploaded_file = st.sidebar.file_uploader("📂 Upload sensor log (CSV)", type=["csv"])

if uploaded_file:
    df = pd.read_csv(uploaded_file)
    numeric_cols = df.select_dtypes(include=np.number).columns.tolist()

    # Anomaly Detection
    iso = IsolationForest(contamination=0.02, random_state=42)
    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]

    # NLP Embeddings
    if 'chunks' not in st.session_state or 'embeddings' not in st.session_state:
        chunks = [
            f"[Entry {i}] " + ", ".join([f"{col}: {row[col]:.2f}" for col in numeric_cols])
            for i, row in df.iterrows()
        ]
        embeddings = EMBED_MODEL.encode(chunks)
        st.session_state.chunks = chunks
        st.session_state.embeddings = embeddings

    # Health Summary
    col1, col2 = st.columns(2)
    with col1:
        st.markdown("### 🧠 Machine Health Summary")
        status_counts = df['status'].value_counts()
        fig1, ax1 = plt.subplots(figsize=(5, 4))
        bars = ax1.bar(status_counts.index, status_counts.values, color=["red", "green"])
        ax1.set_title("Detected Machine Health Conditions", fontsize=14)
        ax1.set_ylabel("Record Count")
        ax1.set_xlabel("Status")
        ax1.set_facecolor("#0f1117")
        ax1.tick_params(colors='white')
        ax1.title.set_color('white')
        ax1.xaxis.label.set_color('white')
        ax1.yaxis.label.set_color('white')
        for spine in ax1.spines.values():
            spine.set_edgecolor('white')
        for bar in bars:
            height = bar.get_height()
            ax1.annotate(f'{height:,}', xy=(bar.get_x() + bar.get_width()/2, height),
                         xytext=(0, 6), textcoords="offset points", ha='center', color='white')
        st.pyplot(fig1)

    with col2:
        st.markdown("### 🧪 Parameters Monitored")
        st.markdown(f"""
            <div class="card">
                {' • '.join(numeric_cols)}
            </div>
        """, unsafe_allow_html=True)

    # Trend Plot
    st.markdown("### 📉 Sensor Trend (with Fault Overlay)")
    col3, col4 = st.columns(2)
    time_col = col3.selectbox("🕒 Time Column", ["None"] + list(df.columns))
    trend_param = col4.selectbox("📌 Select Parameter", numeric_cols)

    if time_col != "None":
        df = df.sort_values(by=time_col)

    x_vals = df[time_col] if time_col != "None" else df.index
    y_vals = df[trend_param]
    y_label = f"{trend_param} ({unit_map.get(trend_param.lower(), '')})"

    fig2, ax2 = plt.subplots(figsize=(8, 4.5))
    ax2.plot(x_vals, y_vals, label=trend_param, color="skyblue", linewidth=1.5)
    ax2.scatter(
        x_vals[df['status'] == '❌ Fault Detected'],
        y_vals[df['status'] == '❌ Fault Detected'],
        color='red', label='Fault Detected', zorder=5
    )
    ax2.set_title(f"{trend_param} Sensor Trend")
    ax2.set_xlabel("Time" if time_col != "None" else "Index")
    ax2.set_ylabel(y_label)
    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)

    img_buffer = BytesIO()
    fig2.savefig(img_buffer, format='png', bbox_inches="tight")
    st.download_button("📷 Download Trend Chart (PNG)", img_buffer.getvalue(), file_name="sensor_trend.png")

    # Fault Rate Over Time
    if time_col != "None":
        st.markdown("### 📈 Fault Rate Over Time")
        df[time_col] = pd.to_datetime(df[time_col], errors='coerce')
        df['fault_flag'] = df['status'].apply(lambda x: 1 if x == '❌ Fault Detected' else 0)
        freq = 'H' if (df[time_col].max() - df[time_col].min()).days <= 3 else 'D'
        grouped = df.set_index(time_col)['fault_flag'].resample(freq).mean() * 100

        fig3, ax3 = plt.subplots(figsize=(8, 4))
        ax3.plot(grouped.index, grouped, color='orange', linewidth=2)
        ax3.set_title("Fault Rate Over Time")
        ax3.set_ylabel("Fault Rate (%)")
        ax3.set_xlabel("Time")
        ax3.set_facecolor("#0f1117")
        ax3.tick_params(colors='white')
        ax3.title.set_color('white')
        ax3.yaxis.label.set_color('white')
        ax3.xaxis.label.set_color('white')
        for spine in ax3.spines.values():
            spine.set_edgecolor('white')
        st.pyplot(fig3)

    # Export Faults
    st.markdown("### 📤 Export Anomalies")
    fault_df = df[df['status'] == '❌ Fault Detected']
    buf = BytesIO()
    fault_df.to_csv(buf, index=False)
    st.download_button("⬇️ Download Fault Log", data=buf.getvalue(), file_name="faults.csv", mime="text/csv")

    # Metadata correlation
    st.markdown("### 🏷️ Fault Distribution by Machine/Component")
    meta_cols = [c for c in df.columns if 'machine' in c.lower() or 'component' in c.lower()]
    for col in meta_cols:
        st.markdown(f"**{col} – Fault Frequency**")
        st.bar_chart(df[df['status'] == '❌ Fault Detected'][col].value_counts())

    # Role Assistant
    st.markdown("### 💬 Technical Assistant by Role")
    roles = {
        "Operator": {
            "description": "Focus on equipment behavior. Spot abnormal patterns and guide simple actions.",
            "style": "Explain simply. Emphasize safety and when to alert maintenance."
        },
        "Maintenance": {
            "description": "Diagnose machine issues. Recommend parts to inspect or replace.",
            "style": "Use technical language. Mention symptoms and sensor causes."
        },
        "Engineer": {
            "description": "Analyze system behavior. Identify root causes or instability.",
            "style": "Use RCA format. Discuss fault thresholds, control issues, and next steps."
        }
    }

    role = st.selectbox("👤 Select your role", roles.keys())
    question = st.text_input("🧠 Ask a question")

    if question:
        qvec = EMBED_MODEL.encode([question])[0]
        sims = np.dot(st.session_state.embeddings, qvec)
        idxs = np.argsort(sims)[-5:][::-1]
        context = "\n".join([st.session_state.chunks[i] for i in idxs])

        prompt = (
            f"ROLE: {role}\n"
            f"RESPONSIBILITIES: {roles[role]['description']}\n"
            f"COMMUNICATION STYLE: {roles[role]['style']}\n\n"
            f"DATA CONTEXT:\n{context}\n\n"
            f"QUESTION:\n{question}\n\n"
            f"ANSWER:\n"
        )
        result = GEN_MODEL(prompt, max_length=400)[0]['generated_text']
        st.markdown(f"**🤖 Response:**\n\n{result}")

else:
    st.info("👈 Upload a sensor CSV file to generate your technical dashboard.")