Update app.py

app.py

@@ -1,169 +1,170 @@
-import streamlit as st
-import pandas as pd
-import numpy as np
-import datetime
-import os
-import matplotlib.pyplot as plt
-from tensorflow.keras.models import load_model
-import joblib
-
-# ===============================
-# Sidebar Navigation
-# ===============================
-st.set_page_config(page_title="🏏 IPL Unified Predictor App", layout="wide")
-selected_tab = st.sidebar.selectbox("🏆 Select Module", [
-    "Dashboard",
-    "Commentary",
-    "Scenario Simulator",
-    "Match Simulator",
-    "Live Match Predictor"
-])
-
-# ===============================
-# 1. Dashboard Intro
-# ===============================
-if selected_tab == "Dashboard":
-    st.title("📊 IPL Unified Prediction & Simulation Dashboard")
-    st.markdown("""
-    Welcome to the complete IPL analytics suite. This unified app supports:
-    - 🎯 Score Prediction using GRU
-    - 🧠 GPT-3.5 Commentary
-    - 🎲 Match Scenario Simulator
-    - 📈 Real-time Momentum & Points Table
-    - 🧪 Live Match Score Prediction
-    
-    Use the sidebar to navigate through modules.
-    """)
-    st.image("match_momentum_dashboard.gif", use_column_width=True)
-
-# ===============================
-# 2. Commentary Dashboard
-# ===============================
-elif selected_tab == "Commentary":
-    st.title("🧠 Score Prediction & GPT Commentary")
-    try:
-        df = pd.read_csv("gru_match_simulation_commentary.csv")
-        st.dataframe(df)
-        st.markdown("""
-        **Commentary Summary:**
-        > The team started steadily, losing early wickets in the powerplay. However, they built partnerships during middle overs and accelerated towards the end with a flurry of boundaries.
-        """)
-        st.image("gru_match_simulation_plot.gif", caption="Score + Commentary Summary")
-    except:
-        st.warning("Upload commentary file or check dataset path.")
-
-# ===============================
-# 3. Scenario Simulator
-# ===============================
-elif selected_tab == "Scenario Simulator":
-    st.title("🎯 Match Scenario Score Predictor")
-    st.markdown("Enter cumulative runs over 20 overs to predict the final score.")
-
-    current_over = st.slider("Select Overs Completed", 1, 20, 10)
-    runs_input = []
-
-    for i in range(current_over):
-        run = st.number_input(f"Runs after Over {i+1}", min_value=0, step=1, key=f"r_{i}")
-        runs_input.append(run)
-
-    if st.button("Predict Score"):
-        if len(runs_input) == current_over:
-            padded = runs_input + [0]*(20 - len(runs_input))
-            arr = np.array(padded).reshape(-1, 1)
-            model = load_model("gru_score_predictor.keras", compile=False)
-            scaler_input = joblib.load("scaler_input.save")
-            scaler_output = joblib.load("scaler_output.save")
-
-            scaled = scaler_input.transform(arr).reshape(1, 20, 1)
-            pred = model.predict(scaled)
-            predicted_score = scaler_output.inverse_transform(pred)[0][0]
-
-            st.success(f"🏏 Predicted Final Score: {predicted_score:.2f} runs")
-
-# ===============================
-# 4. Match Simulator + Points Table
-# ===============================
-elif selected_tab == "Match Simulator":
-    st.title("🏟️ IPL Match Simulator + Points Table")
-    teams = ["CSK", "MI", "RCB", "GT", "RR", "LSG", "KKR", "SRH"]
-    match_results = []
-    points = {team: 0 for team in teams}
-
-    for i in range(10):
-        t1, t2 = np.random.choice(teams, 2, replace=False)
-        winner = np.random.choice([t1, t2])
-        points[winner] += 2
-        match_results.append((t1, t2, winner))
-
-    df_matches = pd.DataFrame(match_results, columns=["Team A", "Team B", "Winner"])
-    df_points = pd.DataFrame(sorted(points.items(), key=lambda x: x[1], reverse=True), columns=["Team", "Points"])
-
-    st.subheader("📝 Simulated Match Results")
-    st.dataframe(df_matches)
-
-    st.subheader("📊 Points Table")
-    st.dataframe(df_points)
-
-# ===============================
-# 5. Live Match Predictor (Runs + Wickets)
-# ===============================
-elif selected_tab == "Live Match Predictor":
-    st.title("📡 IPL Live Match Predictor")
-
-    col1, col2, col3 = st.columns(3)
-    with col1:
-        team_a = st.selectbox("Team A", ["MI", "RCB", "CSK", "GT"])
-    with col2:
-        team_b = st.selectbox("Team B", ["MI", "RCB", "CSK", "GT"])
-    with col3:
-        venue = st.selectbox("Venue", ["Wankhede", "Chinnaswamy", "Chepauk"])
-
-    date = st.date_input("Match Date", datetime.date.today())
-    status = st.radio("Match Status", ["In Progress", "Completed", "Scheduled"])
-
-    if st.button("Generate & Predict"):
-        runs = np.random.randint(0, 21, 20)
-        wickets = np.random.binomial(1, 0.3, 20)
-
-        df = pd.DataFrame({
-            "Over": list(range(1, 21)),
-            "Runs_This_Over": runs,
-            "Cumulative_Runs": np.cumsum(runs),
-            "Wickets_This_Over": wickets,
-            "Cumulative_Wickets": np.cumsum(wickets)
-        })
-
-        st.success(f"Match: {team_a} vs {team_b} at {venue} on {date}")
-        st.dataframe(df)
-
-        # Prediction logic
-        model = load_model("gru_score_predictor_rw.keras", compile=False)
-        scaler_input = joblib.load("scaler_rw_input.save")
-        scaler_output = joblib.load("scaler_rw_output.save")
-
-        padded_input = np.pad(df[["Runs_This_Over", "Wickets_This_Over"]].values,
-                              ((0, 20 - df.shape[0]), (0, 0)), mode='constant')
-        scaled_input = scaler_input.transform(padded_input).reshape(1, 20, 2)
-        pred_scaled = model.predict(scaled_input)
-        predicted_score = scaler_output.inverse_transform(pred_scaled)[0][0]
-
-        st.success(f"🎯 Predicted Final Score: {predicted_score:.2f} runs")
-
-        fig, ax1 = plt.subplots()
-        ax1.plot(df["Over"], df["Cumulative_Runs"], marker='o', color='green', label='Runs')
-        ax1.set_xlabel("Over")
-        ax1.set_ylabel("Runs", color='green')
-        ax2 = ax1.twinx()
-        ax2.bar(df["Over"], df["Wickets_This_Over"], alpha=0.4, color='red', label='Wickets')
-        ax2.set_ylabel("Wickets", color='red')
-        plt.axhline(y=predicted_score, color='blue', linestyle='--', label='Predicted Score')
-        fig.legend(loc='upper left')
-        st.pyplot(fig)
-
-# ===============================
-# Footer
-# ===============================
-st.markdown("""
----
-👨‍💻 Built by Dinesh Kumar | Powered by Streamlit, NumPy, Matplotlib, TensorFlow
-""")
+import streamlit as st
+import pandas as pd
+import numpy as np
+import datetime
+import os
+import matplotlib.pyplot as plt
+from tensorflow.keras.models import load_model
+import joblib
+
+# ===============================
+# Sidebar Navigation
+# ===============================
+st.set_page_config(page_title="🏏 IPL Unified Predictor App", layout="wide")
+selected_tab = st.sidebar.selectbox("🏆 Select Module", [
+    "Dashboard",
+    "Commentary",
+    "Scenario Simulator",
+    "Match Simulator",
+    "Live Match Predictor"
+])
+
+# ===============================
+# 1. Dashboard Intro
+# ===============================
+if selected_tab == "Dashboard":
+    st.title("📊 IPL Unified Prediction & Simulation Dashboard")
+    st.markdown("""
+    Welcome to the complete IPL analytics suite. This unified app supports:
+    - 🎯 Score Prediction using GRU
+    - 🧠 GPT-3.5 Commentary
+    - 🎲 Match Scenario Simulator
+    - 📈 Real-time Momentum & Points Table
+    - 🧪 Live Match Score Prediction
+    
+    Use the sidebar to navigate through modules.
+    """)
+    st.image("match_momentum_dashboard.gif", use_column_width=True)
+
+# ===============================
+# 2. Commentary Dashboard
+# ===============================
+elif selected_tab == "Commentary":
+    st.title("🧠 Score Prediction & GPT Commentary")
+    try:
+        df = pd.read_csv("gru_match_simulation_commentary.csv")
+        st.dataframe(df)
+        st.markdown("""
+        **Commentary Summary:**
+        > The team started steadily, losing early wickets in the powerplay. However, they built partnerships during middle overs and accelerated towards the end with a flurry of boundaries.
+        """)
+        st.image("gru_match_simulation_plot.gif", caption="Score + Commentary Summary")
+    except:
+        st.warning("Upload commentary file or check dataset path.")
+
+# ===============================
+# 3. Scenario Simulator
+# ===============================
+elif selected_tab == "Scenario Simulator":
+    st.title("🎯 Match Scenario Score Predictor")
+    st.markdown("Enter cumulative runs over 20 overs to predict the final score.")
+
+    current_over = st.slider("Select Overs Completed", 1, 20, 10)
+    runs_input = []
+
+    for i in range(current_over):
+        run = st.number_input(f"Runs after Over {i+1}", min_value=0, step=1, key=f"r_{i}")
+        runs_input.append(run)
+
+    if st.button("Predict Score"):
+        if len(runs_input) == current_over:
+            padded = runs_input + [0]*(20 - len(runs_input))
+            arr = np.array(padded).reshape(-1, 1)
+            model = load_model("gru_score_predictor.keras", compile=False)
+            scaler_input = joblib.load("scaler_input.save")
+            scaler_output = joblib.load("scaler_output.save")
+
+            scaled = scaler_input.transform(arr).reshape(1, 20, 1)
+            pred = model.predict(scaled)
+            predicted_score = scaler_output.inverse_transform(pred)[0][0]
+
+            st.success(f"🏏 Predicted Final Score: {predicted_score:.2f} runs")
+
+# ===============================
+# 4. Match Simulator + Points Table
+# ===============================
+elif selected_tab == "Match Simulator":
+    st.title("🏟️ IPL Match Simulator + Points Table")
+    teams = ["CSK", "MI", "RCB", "GT", "RR", "LSG", "KKR", "SRH"]
+    match_results = []
+    points = {team: 0 for team in teams}
+
+    for i in range(10):
+        t1, t2 = np.random.choice(teams, 2, replace=False)
+        winner = np.random.choice([t1, t2])
+        points[winner] += 2
+        match_results.append((t1, t2, winner))
+
+    df_matches = pd.DataFrame(match_results, columns=["Team A", "Team B", "Winner"])
+    df_points = pd.DataFrame(sorted(points.items(), key=lambda x: x[1], reverse=True), columns=["Team", "Points"])
+
+    st.subheader("📝 Simulated Match Results")
+    st.dataframe(df_matches)
+
+    st.subheader("📊 Points Table")
+    st.dataframe(df_points)
+
+# ===============================
+# 5. Live Match Predictor (Runs + Wickets)
+# ===============================
+elif selected_tab == "Live Match Predictor":
+    st.title("📡 IPL Live Match Predictor")
+
+    col1, col2, col3 = st.columns(3)
+    with col1:
+        team_a = st.selectbox("Team A", ["MI", "RCB", "CSK", "GT"])
+    with col2:
+        team_b = st.selectbox("Team B", ["MI", "RCB", "CSK", "GT"])
+    with col3:
+        venue = st.selectbox("Venue", ["Wankhede", "Chinnaswamy", "Chepauk"])
+
+    date = st.date_input("Match Date", datetime.date.today())
+    status = st.radio("Match Status", ["In Progress", "Completed", "Scheduled"])
+
+    if st.button("Generate & Predict"):
+        runs = np.random.randint(0, 21, 20)
+        wickets = np.random.binomial(1, 0.3, 20)
+
+        df = pd.DataFrame({
+            "Over": list(range(1, 21)),
+            "Runs_This_Over": runs,
+            "Cumulative_Runs": np.cumsum(runs),
+            "Wickets_This_Over": wickets,
+            "Cumulative_Wickets": np.cumsum(wickets)
+        })
+
+        st.success(f"Match: {team_a} vs {team_b} at {venue} on {date}")
+        st.dataframe(df)
+
+        # Prediction logic
+        model = load_model("match_live_predictor/gru_score_predictor_rw.keras", compile=False)
+        scaler_input = joblib.load("match_live_predictor/scaler_input_rw.save")
+        scaler_output = joblib.load("match_live_predictor/scaler_output_rw.save")
+
+
+        padded_input = np.pad(df[["Runs_This_Over", "Wickets_This_Over"]].values,
+                              ((0, 20 - df.shape[0]), (0, 0)), mode='constant')
+        scaled_input = scaler_input.transform(padded_input).reshape(1, 20, 2)
+        pred_scaled = model.predict(scaled_input)
+        predicted_score = scaler_output.inverse_transform(pred_scaled)[0][0]
+
+        st.success(f"🎯 Predicted Final Score: {predicted_score:.2f} runs")
+
+        fig, ax1 = plt.subplots()
+        ax1.plot(df["Over"], df["Cumulative_Runs"], marker='o', color='green', label='Runs')
+        ax1.set_xlabel("Over")
+        ax1.set_ylabel("Runs", color='green')
+        ax2 = ax1.twinx()
+        ax2.bar(df["Over"], df["Wickets_This_Over"], alpha=0.4, color='red', label='Wickets')
+        ax2.set_ylabel("Wickets", color='red')
+        plt.axhline(y=predicted_score, color='blue', linestyle='--', label='Predicted Score')
+        fig.legend(loc='upper left')
+        st.pyplot(fig)
+
+# ===============================
+# Footer
+# ===============================
+st.markdown("""
+---
+👨‍💻 Built by Dinesh Kumar | Powered by Streamlit, NumPy, Matplotlib, TensorFlow
+""")