Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,154 @@
-import altair as alt
+import streamlit as st
 import numpy as np
 import pandas as pd
-import streamlit as st
+import plotly.graph_objects as go
+import pickle
+from keras.models import load_model
+
+# ============================================================
+# SETTINGS & MODEL LOAD
+# ============================================================
+st.set_page_config(page_title="Workforce AI Optimizer", layout="wide")
+
+# Sidebar genişliğini sabitleyen CSS
+st.markdown(
+    """
+    <style>
+    [data-testid="stSidebar"][aria-expanded="true"]{
+        min-width: 360px;
+        max-width: 360px;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True,
+)
+
+@st.cache_resource
+def load_assets():
+    model = load_model("lstm_traffic_model.keras")
+    sc = pickle.load(open("scaler.pkl", "rb"))
+    return model, sc
+
+try:
+    model, sc = load_assets()
+except Exception as e:
+    st.error(f"Error: Model files not found! -> {e}")
+    st.stop()
+
+# ============================================================
+# SIDEBAR / 🛠️ SETUP & GUIDE
+# ============================================================
+st.sidebar.title("🛠️ Setup & Guide / Rehber")
+
+st.sidebar.markdown("""
+**Data Format / Veri Formatı:**
+The CSV should contain historical call data. / CSV geçmiş çağrı verilerini içermelidir.
+""")
+
+# SLOT MANTIĞI - 6 Örnekli ve Noktalı Versiyon
+st.sidebar.info("""
+**Slot Logic / Slot Mantığı:**
+- **Slot 0:** 08:00-09:00 (100 cals)
+- **Slot 1:** 09:00-10:00 (150 cals)
+- **Slot 2:** 10:00-11:00 (120 cals)
+- **Slot 3:** 11:00-12:00 (180 cals)
+- **Slot 4:** 12:00-13:00 (200 cals)
+- **Slot 5:** 13:00-14:00 (160 cals)
+- **...**
+- **Slot 81:** Midnight (5 cals)
+""")
+
+st.sidebar.subheader("Sample CSV / Örnek Yapı")
+example_df = pd.DataFrame({"calls": [105, 140, 88, 120, 200, 160]})
+st.sidebar.dataframe(example_df, use_container_width=True)
+
+st.sidebar.warning("⚠️ **Column Name:** 'calls' or 'Incoming Calls'")
+
+st.sidebar.markdown("---")
+st.sidebar.subheader("💰 Cost Settings / Maliyet")
+wage = st.sidebar.number_input("Hourly Wage / Saatlik Ücret ($)", value=20)
+capacity = st.sidebar.number_input("Calls per Staff / Kapasite", value=15)
+
+# ============================================================
+# MAIN UI
+# ============================================================
+st.title("📞 Workforce Optimization AI / İş Gücü Optimizasyonu")
+st.write("Ensuring the right number of people at the right time.")
+st.markdown("---")
+
+file = st.file_uploader("Upload CSV / CSV Yükle", type=["csv"])
+
+if file is not None:
+    df = pd.read_csv(file)
+    target_col = "calls" if "calls" in df.columns else ("Incoming Calls" if "Incoming Calls" in df.columns else None)
+    
+    if target_col is None:
+        st.error("❌ Column not found!")
+        st.stop()
+    
+    raw_data = df[[target_col]].values
+
+    # PREDICTION
+    scaled_data = sc.transform(raw_data)
+    pred_scaled = model.predict(scaled_data)
+    predictions = sc.inverse_transform(pred_scaled)
+    needed_staff = np.ceil(predictions / capacity).flatten().astype(int)
+
+    # 🎯 DECISION LOGIC
+    st.header("🎯 Decision Logic / Karar Mantığı")
+    logic_col1, logic_col2 = st.columns(2)
+
+    with logic_col1:
+        st.error("### 🔥 High Intensity (Yüksek Yoğunluk)")
+        st.write("**Advice:** INCREASE STAFF to protect quality.")
+        st.write("**Öneri:** Kalite için PERSONEL ARTIRIN.")
+
+    with logic_col2:
+        st.success("### 💰 Saving Area (Tasarruf Alanı)")
+        st.write("**Advice:** REDUCE STAFF to maximize profit.")
+        st.write("**Öneri:** Kâr için PERSONELİ AZALTIN.")
+
+    # 📈 GRAPH
+    st.markdown("---")
+    st.subheader("📈 Capacity Analysis / Kapasite Analizi")
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(y=raw_data.flatten(), name="Past", line=dict(color="gray")))
+    fig.add_trace(go.Scatter(y=predictions.flatten(), name="AI Forecast", line=dict(color="#1C83E1", width=3)))
+    fig.add_trace(go.Bar(y=needed_staff * capacity, name="Capacity", opacity=0.2, marker_color="green"))
+    fig.update_layout(hovermode="x unified", template="plotly_white", height=400)
+    st.plotly_chart(fig, use_container_width=True)
+
+    # 🧠 RECOMMENDATIONS
+    st.header("🧠 AI Strategic Recommendations")
+    mean_val = np.mean(needed_staff)
+    peak_indices = np.where(needed_staff > mean_val * 1.25)[0].tolist()
+    low_indices = np.where(needed_staff < mean_val * 0.75)[0].tolist()
+
+    c1, c2 = st.columns(2)
+    with c1:
+        st.error(f"### 🚨 High Intensity")
+        if peak_indices:
+            st.write(f"**At:** {', '.join([f'Slot {i}' for i in peak_indices[:5]])}...")
+            st.write("Increase staff. / Personel artırın.")
+        else:
+            st.write("No major peaks.")
+
+    with c2:
+        st.success(f"### 📉 Saving Area")
+        if low_indices:
+            st.write(f"**At:** {', '.join([f'Slot {i}' for i in low_indices[:5]])}...")
+            st.write("Reduce staff. / Personeli azaltın.")
+        else:
+            st.write("No saving opportunity.")
+
+    # 📊 TABLE
+    with st.expander("📊 Detailed Schedule Table"):
+        res_df = pd.DataFrame({
+            "Time Slot": [f"Slot {i}" for i in range(len(predictions))],
+            "Predicted Demand": predictions.flatten().astype(int),
+            "Suggested Staff": needed_staff
+        })
+        st.dataframe(res_df, use_container_width=True)
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+else:
+    st.info("👋 Please upload your CSV file to begin. / Başlamak için CSV yükleyin.")