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README.md

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----
-title: Stock Prediction V2
-emoji: 🚀
-colorFrom: red
-colorTo: red
-sdk: docker
-app_port: 8501
-tags:
-- streamlit
-pinned: false
-short_description: Streamlit template space
----
-
-# Welcome to Streamlit!
-
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
-
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+---
+title: Stock Prediction App
+emoji: 🌖
+colorFrom: purple
+colorTo: gray
+sdk: streamlit
+sdk_version: 1.32.2
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import seaborn as sns
+import streamlit as st
+from datetime import datetime, timedelta
+from src import stock_data, model_train, train_test_split, model_predict
+
+st.image('images.jpg', caption='Image credit : Kelly Sikkema')
+# Function to display result, history, and data information
+def display_results(user_input,result, data_inf, train):
+    st.subheader(f'Data for :blue[{user_input.upper()}] the past 10 days.')
+    st.write(data_inf)
+    st.subheader(f'Annual data line Chart :blue[{user_input.upper()}]')
+    plot = sns.lineplot(train)
+    st.pyplot(plot.get_figure())
+    future_date = datetime.now() + timedelta(days=1)
+    formatted_date = future_date.strftime('%Y-%m-%d')
+    st.markdown(f'''
+        ## Stock Prediction Analysis for <span style="font-size:24px;">{user_input.upper()}</span>
+        
+        <p style="font-size:24px;">
+        {formatted_date} {user_input.upper()} predicted price is : <b>{round(result,2)}</b>
+        </p>
+    ''', unsafe_allow_html=True)
+    
+    
+    
+    
+# Main function to run the app
+def main():
+    st.title("Stock Prediction App")
+    user_input = st.text_input("Enter your stock ticker here:")
+    
+    if st.button("Predict"):
+        with st.spinner('Loading...'):
+            train = stock_data(user_input)
+            if len(train)>0: 
+                x_train, y_train, scaler = train_test_split(train)
+                history, model = model_train(x_train, y_train)
+                result, data_inf = model_predict(train, scaler, model) 
+                display_results(user_input,result[0][0], data_inf,train)
+            else:
+                st.write('Invalid stock ticker. Please verify the ticker symbol on the following website: [Yahoo Finance.](https://finance.yahoo.com/)')
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

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-altair
-pandas
-streamlit
+scikit-learn==1.3.2
+numpy<2
+pandas
+yfinance
+streamlit
+tensorflow
+seaborn
+matplotlib