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app(2).py

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+# -*- coding: utf-8 -*-
+"""stockpriceprediction_RNN.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1WNG8vH1hyyxmR3_BEtT9-c0Golei-f4d
+"""
+
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import SimpleRNN, Dense
+import gradio as gr
+
+# 1. Generate dummy data
+def generate_dummy_data():
+    x = np.linspace(0, 100, 500)
+    y = np.sin(x / 5) + np.random.normal(scale=0.1, size=len(x))
+    return y
+
+data = generate_dummy_data()
+
+# 2. Prepare dataset (with time_steps = 5)
+def create_dataset(data, time_steps=5):  # 👈 changed from 10 to 5
+    X, y = [], []
+    for i in range(len(data) - time_steps):
+        X.append(data[i:i + time_steps])
+        y.append(data[i + time_steps])
+    return np.array(X), np.array(y)
+
+X, y = create_dataset(data)
+X = X.reshape((X.shape[0], X.shape[1], 1))
+
+# 3. Build model for input shape (5, 1)
+model = Sequential([
+    SimpleRNN(50, activation='relu', input_shape=(5, 1)),  # 👈 changed from (10, 1) to (5, 1)
+    Dense(1)
+])
+model.compile(optimizer='adam', loss='mse')
+model.fit(X, y, epochs=10, verbose=0)
+
+import pickle
+
+with open('stock.pkl', 'wb') as file:
+    pickle.dump(model,file)
+
+with open('stock.pkl', 'rb') as f:
+    loaded_model = pickle.load(f)
+
+# 4. Predict function
+def predict_next_price(seq):
+    try:
+        seq = [float(i.strip()) for i in seq.split(',')]
+        if len(seq) != 5:  # 👈 only 5 numbers expected now
+            return "Please enter exactly 5 numbers."
+
+        input_seq = np.array(seq).reshape((1, 5, 1))  # 👈 reshape accordingly
+        pred = model.predict(input_seq)
+        return f"📈 Predicted next price: {pred[0][0]:.4f}"
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# 5. Gradio UI
+iface = gr.Interface(
+    fn=predict_next_price,
+    inputs=gr.Textbox(lines=2, placeholder="Enter 5 stock prices, comma-separated"),
+    outputs="text",
+    title="📊 Stock Price Predictor (RNN)",
+    description="Enter 5 stock prices to predict the next one."
+)
+
+iface.launch()

requirements(2).txt

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+gradio
+pandas
+scikit-learn
+numpy
+matplotlib
+tensorflow

stock.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1cad596d25aaafe0ca1919dcd14ea30cd1cc00e8d80193c3f34720999832de91
+size 56048