Update app.py

app.py

@@ -2,6 +2,9 @@ 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")
@@ -12,25 +15,9 @@ os.makedirs(data_folder, exist_ok=True)
 
 # File paths
 batting_path = "Batting_10_Teams_Final.csv"
-# os.path.join(data_folder, "batting.csv")
 bowling_path = "Bowling_10_Teams_Final.csv"
-# os.path.join(data_folder, "bowling.csv")
-
-# # Upload CSVs only if they are not already saved
-# if not (os.path.exists(batting_path) and os.path.exists(bowling_path)):
-#     uploaded_batting = st.sidebar.file_uploader("Upload Batting CSV", type=["csv"], key="batting")
-#     uploaded_bowling = st.sidebar.file_uploader("Upload Bowling CSV", type=["csv"], key="bowling")
-    
-#     if uploaded_batting and uploaded_bowling:
-#         batting_df = pd.read_csv(uploaded_batting)
-#         bowling_df = pd.read_csv(uploaded_bowling)
-        
-#         # Save the files permanently
-#         batting_df.to_csv(batting_path, index=False)
-#         bowling_df.to_csv(bowling_path, index=False)
-#         st.success("CSV files saved successfully!")
-# else:
-    # Load saved CSVs
+
+# Load saved CSVs
 batting_df = pd.read_csv(batting_path)
 bowling_df = pd.read_csv(bowling_path)
 
@@ -50,10 +37,31 @@ player_bowling = bowling_df[(bowling_df['player_name'] == player) & (bowling_df[
 
 st.title(f"{player}'s Performance Dashboard")
 
-# Display player data
-st.subheader("Player Data")
+# ✅ 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. You only provide information about players, not their stats."),
+    ("human", "{player_name}")
+])
 
-# Display only the first 16 columns
+# Creating chain
+chain = prompt | model | out_par
+
+# Query GenAI with the player's name
+if st.button("Get Player Info"):
+    response = chain.invoke({"player_name": player})
+    st.subheader(f"Information about {player}")
+    st.write(response)
+
+# ✅ Display only the first 16 columns
 if len(player_batting) > 0:
     st.write("### Batting Data")
     st.dataframe(player_batting.iloc[:min(17, len(player_batting)), :17])
@@ -66,9 +74,7 @@ if len(player_bowling) > 0:
 else:
     st.write("No bowling data available.")
 
-
-
-# Batting Visualization
+# ✅ Batting Visualization
 if not player_batting.empty:
     st.subheader("Batting Stats")
     col1, col2 = st.columns(2)
@@ -79,7 +85,7 @@ if not player_batting.empty:
         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)
 
-# Bowling Visualization
+# ✅ Bowling Visualization
 if not player_bowling.empty:
     st.subheader("Bowling Stats")
     col3, col4 = st.columns(2)