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Browse files- app.py +26 -0
- model.h5 +3 -0
- requirements.txt +10 -0
app.py
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import streamlit as st
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from keras.models import load_model
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import numpy as np
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# Modeli yükle
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model = load_model('model.h5')
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# Uygulama başlığı
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st.title("Model Tahmin Uygulaması")
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# Kullanıcıdan veri girişi al
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team1_input = st.number_input("Team 1 ID:", min_value=0)
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team2_input = st.number_input("Team 2 ID:", min_value=0)
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if st.button("Tahmin Yap"):
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if team1_input is not None and team2_input is not None:
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# Giriş verilerini hazırlayın
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input_data = [np.array([[team1_input]]), np.array([[team2_input]])] # İki ayrı girdi
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# Tahmin yap
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prediction = model.predict(input_data)
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# Sonucu göster
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st.write("Tahmin edilen değer:", prediction[0][0])
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else:
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st.warning("Lütfen her iki takımın ID'sini de girin.")
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model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:d010df3f2e78727263edab630ab853ba8768e9e13fb39dd21a42a74b1d1277ac
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size 479600
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requirements.txt
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streamlit
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tensorflow
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opencv-python
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scikit-learn
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torch
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torchvision
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matplotlib
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transformers
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sentencepiece
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plotly
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