Update app.py

app.py

@@ -1,3 +1,134 @@
+# import streamlit as st
+# import pandas as pd
+# import plotly.express as px
+# import os
+# from langchain_google_genai import GoogleGenerativeAI
+# from langchain_core.prompts import ChatPromptTemplate
+# from langchain_core.output_parsers import StrOutputParser
+
+# # Streamlit App
+# st.set_page_config(page_title="Cricket Player Stats Dashboard", layout="wide")
+
+# # Create a folder to save CSVs if not exists
+# data_folder = "data"
+# os.makedirs(data_folder, exist_ok=True)
+
+# # File paths
+# batting_path = "Batting_10_Teams_Final.csv"
+# bowling_path = "Bowling_10_Teams_Final.csv"
+
+# # Load saved CSVs
+# batting_df = pd.read_csv(batting_path)
+# bowling_df = pd.read_csv(bowling_path)
+
+# # Extract unique teams and players
+# teams = sorted(set(batting_df['Country'].unique()).union(bowling_df['Country'].unique()))
+# team = st.sidebar.selectbox("Select Team", teams)
+
+# # Filter players by team
+# batting_players = batting_df[batting_df['Country'] == team]['player_name'].unique()
+# bowling_players = bowling_df[bowling_df['Country'] == team]['player_name'].unique()
+# players = sorted(set(batting_players).union(bowling_players))
+# player = st.sidebar.selectbox("Select Player", players)
+
+# # Filter data for the selected player
+# player_batting = batting_df[(batting_df['player_name'] == player) & (batting_df['Country'] == team)]
+# player_bowling = bowling_df[(bowling_df['player_name'] == player) & (bowling_df['Country'] == team)]
+
+# st.title(f"{player}")
+
+# # ✅ Integrating LLMs using Gen-AI
+
+# # Loading API key and creating model
+# api_key = st.secrets.get('genai_key')
+# model = GoogleGenerativeAI(model="gemini-1.5-pro", google_api_key=api_key)
+
+# # Creating output parser to generate output
+# out_par = StrOutputParser()
+
+# # Creating prompt template
+# prompt = ChatPromptTemplate.from_messages([
+#     ("system", '''You are an AI cricket player information provider. Display the player's complete bio data in a 
+#                   detailed table format with rows and columns, including personal information. Below the table, 
+#                   include debut details for all formats. Additionally, provide a brief description of the player 
+#                   underneath. Only include player information, not their performance statistics.'''),
+#     ("human", "{player_name}")
+# ])
+
+# # Creating chain
+# chain = prompt | model | out_par
+
+# # Query GenAI with the player's name
+# response = chain.invoke({"player_name": player})
+# st.write(response)
+
+# # Buttons for Batting and Bowling Cards with Toggle Feature
+# col_card1, col_card2 = st.columns(2)
+# if "batting_card" not in st.session_state:
+#     st.session_state["batting_card"] = False
+# if "bowling_card" not in st.session_state:
+#     st.session_state["bowling_card"] = False
+
+# with col_card1:
+#     if st.button("Batting Card"):
+#         st.session_state["batting_card"] = not st.session_state["batting_card"]
+
+# if st.session_state["batting_card"]:
+#     if len(player_batting) > 0:
+#         st.write("### Batting Data")
+#         st.dataframe(player_batting.iloc[:, :16])  # Display first 16 columns
+#     else:
+#         st.write("No batting data available.")
+
+# with col_card2:
+#     if st.button("Bowling Card"):
+#         st.session_state["bowling_card"] = not st.session_state["bowling_card"]
+
+# if st.session_state["bowling_card"]:
+#     if len(player_bowling) > 0:
+#         st.write("### Bowling Data")
+#         st.dataframe(player_bowling.iloc[:, :15])  # Display first 15 columns
+#     else:
+#         st.write("No bowling data available.")
+
+
+    
+    
+# # Batting Visualization
+# if not player_batting.empty:
+#     st.subheader("Batting Stats")
+#     col1, col2 = st.columns(2)
+#     with col1:
+#         fig_bat_runs = px.bar(player_batting, x='Format', y='Runs', color='Format', title='Runs by Format')
+#         st.plotly_chart(fig_bat_runs, use_container_width=True)
+#     with col2:
+#         fig_bat_sr = px.line(player_batting, x='Format', y='SR', title='Strike Rate by Format')
+#         st.plotly_chart(fig_bat_sr, use_container_width=True)
+
+#      # Batting Pie Chart
+#     fig_bat_pie = px.pie(player_batting, values='Runs', names='Format', 
+#                          title='Runs Distribution Across Formats', 
+#                          color_discrete_map={"ODI": "red", "T20": "green", "Test": "blue"})
+#     st.plotly_chart(fig_bat_pie, use_container_width=True)
+
+# # Bowling Visualization
+# if not player_bowling.empty:
+#     st.subheader("Bowling Stats")
+#     col3, col4 = st.columns(2)
+#     with col3:
+#         fig_bowl_wickets = px.bar(player_bowling, x='Format', y='Wickets', color='Format', title='Wickets by Format')
+#         st.plotly_chart(fig_bowl_wickets, use_container_width=True)
+#     with col4:
+#         fig_bowl_eco = px.line(player_bowling, x='Format', y='Eco', title='Economy Rate by Format')
+#         st.plotly_chart(fig_bowl_eco, use_container_width=True)
+
+#       # Bowling Pie Chart
+#     fig_bowl_pie = px.pie(player_bowling, values='Wickets', names='Format', 
+#                           title='Wickets Distribution Across Formats', 
+#                           color_discrete_map={"ODI": "yellow", "T20": "purple", "Test": "brown"})
+#     st.plotly_chart(fig_bowl_pie, use_container_width=True)
+    
+
 import streamlit as st
 import pandas as pd
 import plotly.express as px
@@ -6,125 +137,97 @@ from langchain_google_genai import GoogleGenerativeAI
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_core.output_parsers import StrOutputParser
 
-# Streamlit App
-st.set_page_config(page_title="Cricket Player Stats Dashboard", layout="wide")
-
-# Create a folder to save CSVs if not exists
-data_folder = "data"
-os.makedirs(data_folder, exist_ok=True)
-
-# File paths
-batting_path = "Batting_10_Teams_Final.csv"
-bowling_path = "Bowling_10_Teams_Final.csv"
-
-# Load saved CSVs
-batting_df = pd.read_csv(batting_path)
-bowling_df = pd.read_csv(bowling_path)
-
-# Extract unique teams and players
-teams = sorted(set(batting_df['Country'].unique()).union(bowling_df['Country'].unique()))
-team = st.sidebar.selectbox("Select Team", teams)
-
-# Filter players by team
-batting_players = batting_df[batting_df['Country'] == team]['player_name'].unique()
-bowling_players = bowling_df[bowling_df['Country'] == team]['player_name'].unique()
-players = sorted(set(batting_players).union(bowling_players))
-player = st.sidebar.selectbox("Select Player", players)
-
-# Filter data for the selected player
-player_batting = batting_df[(batting_df['player_name'] == player) & (batting_df['Country'] == team)]
-player_bowling = bowling_df[(bowling_df['player_name'] == player) & (bowling_df['Country'] == team)]
-
-st.title(f"{player}")
-
-# ✅ Integrating LLMs using Gen-AI
-
-# Loading API key and creating model
-api_key = st.secrets.get('genai_key')
-model = GoogleGenerativeAI(model="gemini-1.5-pro", google_api_key=api_key)
-
-# Creating output parser to generate output
-out_par = StrOutputParser()
-
-# Creating prompt template
-prompt = ChatPromptTemplate.from_messages([
-    ("system", '''You are an AI cricket player information provider. Display the player's complete bio data in a 
-                  detailed table format with rows and columns, including personal information. Below the table, 
-                  include debut details for all formats. Additionally, provide a brief description of the player 
-                  underneath. Only include player information, not their performance statistics.'''),
-    ("human", "{player_name}")
-])
-
-# Creating chain
-chain = prompt | model | out_par
-
-# Query GenAI with the player's name
-response = chain.invoke({"player_name": player})
-st.write(response)
-
-# Buttons for Batting and Bowling Cards with Toggle Feature
-col_card1, col_card2 = st.columns(2)
-if "batting_card" not in st.session_state:
-    st.session_state["batting_card"] = False
-if "bowling_card" not in st.session_state:
-    st.session_state["bowling_card"] = False
-
-with col_card1:
-    if st.button("Batting Card"):
-        st.session_state["batting_card"] = not st.session_state["batting_card"]
-
-if st.session_state["batting_card"]:
-    if len(player_batting) > 0:
-        st.write("### Batting Data")
-        st.dataframe(player_batting.iloc[:, :16])  # Display first 16 columns
-    else:
-        st.write("No batting data available.")
-
-with col_card2:
-    if st.button("Bowling Card"):
-        st.session_state["bowling_card"] = not st.session_state["bowling_card"]
-
-if st.session_state["bowling_card"]:
-    if len(player_bowling) > 0:
-        st.write("### Bowling Data")
-        st.dataframe(player_bowling.iloc[:, :15])  # Display first 15 columns
-    else:
-        st.write("No bowling data available.")
-
-
-    
-    
-# Batting Visualization
-if not player_batting.empty:
-    st.subheader("Batting Stats")
-    col1, col2 = st.columns(2)
-    with col1:
-        fig_bat_runs = px.bar(player_batting, x='Format', y='Runs', color='Format', title='Runs by Format')
-        st.plotly_chart(fig_bat_runs, use_container_width=True)
-    with col2:
-        fig_bat_sr = px.line(player_batting, x='Format', y='SR', title='Strike Rate by Format')
-        st.plotly_chart(fig_bat_sr, use_container_width=True)
-
-     # Batting Pie Chart
-    fig_bat_pie = px.pie(player_batting, values='Runs', names='Format', 
-                         title='Runs Distribution Across Formats', 
-                         color_discrete_map={"ODI": "red", "T20": "green", "Test": "blue"})
-    st.plotly_chart(fig_bat_pie, use_container_width=True)
-
-# Bowling Visualization
-if not player_bowling.empty:
-    st.subheader("Bowling Stats")
-    col3, col4 = st.columns(2)
-    with col3:
-        fig_bowl_wickets = px.bar(player_bowling, x='Format', y='Wickets', color='Format', title='Wickets by Format')
-        st.plotly_chart(fig_bowl_wickets, use_container_width=True)
-    with col4:
-        fig_bowl_eco = px.line(player_bowling, x='Format', y='Eco', title='Economy Rate by Format')
-        st.plotly_chart(fig_bowl_eco, use_container_width=True)
-
-      # Bowling Pie Chart
-    fig_bowl_pie = px.pie(player_bowling, values='Wickets', names='Format', 
-                          title='Wickets Distribution Across Formats', 
-                          color_discrete_map={"ODI": "yellow", "T20": "purple", "Test": "brown"})
-    st.plotly_chart(fig_bowl_pie, use_container_width=True)
-    
+# Set page config
+st.set_page_config(page_title="🏏 Ultimate Cricket Analytics", layout="wide")
+
+# Load CSV files
+batting_df = pd.read_csv("Batting_10_Teams_Final.csv")
+bowling_df = pd.read_csv("Bowling_10_Teams_Final.csv")
+odi_df = pd.read_excel("odi.xls")
+t20_df = pd.read_excel("t20.xls")
+test_df = pd.read_excel("test.xls")
+
+# Tabs for navigation
+tab1, tab2, tab3 = st.tabs(["🏏 Player Dashboard", "📊 Team Comparison", "📚 GenAI Team Bio"])
+
+# ---------------- TAB 1: Player Dashboard ----------------
+with tab1:
+    st.title("🏏 Player Dashboard")
+    teams = sorted(set(batting_df['Country'].unique()).union(bowling_df['Country'].unique()))
+    team = st.sidebar.selectbox("Select Team", teams)
+
+    batting_players = batting_df[batting_df['Country'] == team]['player_name'].unique()
+    bowling_players = bowling_df[bowling_df['Country'] == team]['player_name'].unique()
+    players = sorted(set(batting_players).union(bowling_players))
+    player = st.sidebar.selectbox("Select Player", players)
+
+    player_batting = batting_df[(batting_df['player_name'] == player) & (batting_df['Country'] == team)]
+    player_bowling = bowling_df[(bowling_df['player_name'] == player) & (bowling_df['Country'] == team)]
+
+    st.header(player)
+
+    # GenAI Player Info
+    api_key = st.secrets.get('genai_key')
+    model = GoogleGenerativeAI(model="gemini-1.5-pro", google_api_key=api_key)
+    out_par = StrOutputParser()
+    prompt = ChatPromptTemplate.from_messages([
+        ("system", '''You are an AI cricket player information provider. Display the player's complete bio data in a 
+                      detailed table format with rows and columns, including personal information. Below the table, 
+                      include debut details for all formats. Additionally, provide a brief description of the player 
+                      underneath. Only include player information, not their performance statistics.'''),
+        ("human", "{player_name}")
+    ])
+    chain = prompt | model | out_par
+    st.write(chain.invoke({"player_name": player}))
+
+    # Toggle Cards
+    if st.button("Show Batting Card"):
+        st.dataframe(player_batting.iloc[:, :16])
+    if st.button("Show Bowling Card"):
+        st.dataframe(player_bowling.iloc[:, :15])
+
+    # Visuals
+    if not player_batting.empty:
+        st.subheader("Batting Visualizations")
+        col1, col2 = st.columns(2)
+        with col1:
+            st.plotly_chart(px.bar(player_batting, x='Format', y='Runs', color='Format'))
+        with col2:
+            st.plotly_chart(px.line(player_batting, x='Format', y='SR'))
+        st.plotly_chart(px.pie(player_batting, values='Runs', names='Format'))
+
+    if not player_bowling.empty:
+        st.subheader("Bowling Visualizations")
+        col3, col4 = st.columns(2)
+        with col3:
+            st.plotly_chart(px.bar(player_bowling, x='Format', y='Wickets', color='Format'))
+        with col4:
+            st.plotly_chart(px.line(player_bowling, x='Format', y='Eco'))
+        st.plotly_chart(px.pie(player_bowling, values='Wickets', names='Format'))
+
+# ---------------- TAB 2: Team Comparison ----------------
+with tab2:
+    st.title("📊 Team Comparison")
+    format_option = st.selectbox("Choose Format", ["ODI", "T20", "Test"])
+    data = odi_df if format_option == "ODI" else t20_df if format_option == "T20" else test_df
+
+    selected_teams = st.multiselect("Select Teams to Compare (up to 3)", data['Team'].unique(), max_selections=3)
+    if selected_teams:
+        comparison_df = data[data['Team'].isin(selected_teams)]
+        st.dataframe(comparison_df.reset_index(drop=True))
+
+        # Example visuals
+        st.plotly_chart(px.bar(comparison_df, x='Team', y='Matches', color='Team', title="Matches Played"))
+        st.plotly_chart(px.bar(comparison_df, x='Team', y='Win%', color='Team', title="Win Percentage"))
+
+# ---------------- TAB 3: Team GenAI Bio ----------------
+with tab3:
+    st.title("📚 Team GenAI Bio")
+    team_input = st.text_input("Enter Team Name (e.g., India, Australia)")
+    if team_input:
+        team_prompt = ChatPromptTemplate.from_messages([
+            ("system", "You are an AI cricket historian. Provide a brief yet detailed overview of the team."),
+            ("human", "{team_name}")
+        ])
+        team_chain = team_prompt | model | out_par
+        st.write(team_chain.invoke({"team_name": team_input}))