Duplicate from DataWorldPro/GG

.gitattributes

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README.md

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+---
+title: IPL_SCORE_PREDICTOR
+emoji: 📉
+colorFrom: green
+colorTo: yellow
+sdk: streamlit
+sdk_version: 1.25.0
+app_file: main.py
+pinned: false
+duplicated_from: DataWorldPro/GG
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

main.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Mon Feb  6 17:42:59 2023
+
+@author: HARSH
+"""
+
+import streamlit as st
+import pandas as pd
+import pickle
+with open("model_pickle",'rb')as f:
+    pipe=pickle.load(f)
+st.title("IPL Score Predictor")
+team=['Royal Challengers Bangalore', 'Sunrisers Hyderabad',
+       'Delhi Capitals', 'Chennai Super Kings', 'Kolkata Knight Riders',
+       'Punjab Kings', 'Rajasthan Royals', 'Mumbai Indians']
+col1, col2 = st.columns(2)
+with col1:
+   BattingTeam=st.selectbox('Batting Team',sorted(team))
+with col2:
+   BowlingTeam= st.selectbox("Bowling Team", sorted(team))
+cities=['Ahmedabad', 'Mumbai', 'Navi Mumbai', 'Pune', 'Dubai', 'Sharjah',
+       'Abu Dhabi', 'Delhi', 'Chennai', 'Hyderabad', 'Visakhapatnam',
+       'Chandigarh', 'Bengaluru', 'Kolkata', 'Jaipur', 'Indore',
+       'Bangalore', 'Raipur', 'Ranchi', 'Cuttack', 'Dharamsala', 'Nagpur',
+       'Johannesburg', 'Centurion', 'Durban', 'Bloemfontein',
+       'Port Elizabeth', 'Kimberley', 'East London', 'Cape Town']
+City = st.selectbox('Select city',sorted(cities))
+
+col3,col4,col5 = st.columns(3)
+
+with col3:
+    Current_Score = st.number_input('Current Score',value=0)
+with col4:
+    overs = st.number_input('Overs done(works for over>5)',min_value=5,max_value=20)
+with col5:
+    wickets = st.number_input('Wickets out',min_value=0,max_value=10)
+
+last_five = st.number_input('Runs scored in last 5 overs',value=0)
+
+if st.button('Predict Score'):
+    Balls_left = 120 - (overs*6)
+    Wicket_left = 10 -wickets
+    crr = Current_Score/overs
+
+    input_df = pd.DataFrame(
+     {'BattingTeam': [BattingTeam], 'BowlingTeam': [BowlingTeam],'City':City, 'Current_Score': [Current_Score],'Balls_left': [Balls_left], 'Wicket_left': [Wicket_left], 'crr': [crr], 'last_five': [last_five]})
+    result = pipe.predict(input_df)
+    st.header("Predicted Score - " + str(int(result[0])))

model_pickle

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5649cab88f39d52e62098f2af2b3e43ce3ae2d81e9d55c99f0f3519546b71dd8
+size 55272142

pre-requirements.txt

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+
+streamlit
+requests
+scikit-learn==1.0.2 
+xgboost