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app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib
+import tensorflow as tf
+import matplotlib.pyplot as plt
+
+# Load trained model and scaler
+model = tf.keras.models.load_model("stock_model.h5")
+scaler = joblib.load("scaler.pkl")
+
+# Function to predict stock price
+def predict_stock_price(data):
+    data = scaler.transform(data.reshape(-1, 1))
+    data = np.array([data])
+    predicted_price = model.predict(data)
+    return scaler.inverse_transform(predicted_price)[0][0]
+
+# Streamlit UI
+st.title("📈 Stock Market Prediction App")
+
+uploaded_file = st.file_uploader("Upload Stock Data (CSV)", type="csv")
+
+if uploaded_file is not None:
+    df = pd.read_csv(uploaded_file, parse_dates=["Date"], index_col="Date")
+    st.write("Uploaded Data Preview:", df.tail())
+
+    if st.button("Predict Next Closing Price"):
+        last_data = df["Close"].values[-50:]  # Use last 50 days for prediction
+        predicted_price = predict_stock_price(last_data)
+        st.success(f"Predicted Closing Price: ${predicted_price:.2f}")
+
+        # Plot stock price trend
+        fig, ax = plt.subplots()
+        ax.plot(df.index, df["Close"], label="Actual Price")
+        ax.axhline(y=predicted_price, color="r", linestyle="--", label="Predicted Price")
+        ax.legend()
+        st.pyplot(fig)

requirements.txt

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+streamlit
+tensorflow
+numpy
+pandas
+scikit-learn
+matplotlib
+seaborn
+joblib

scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f2114c9c38dde724e6bdefbb4c05279fb67e1204c49c2b14ee3178872c067558
+size 1023

stock_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:21c9e70b75654343b0349d410f03030bcbe4b8b5a6ac8e957aeb1b0c5bb5c349
+size 433576