Create app.py

app.py

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+import yfinance as yf
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import gradio as gr
+from sklearn.linear_model import LinearRegression
+from transformers import pipeline
+
+# Load Hugging Face summarization pipeline
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+
+
+# Step 1: Fetch historical stock data
+def get_stock_data(ticker):
+    df = yf.download(ticker, start="2020-01-01", end="2024-12-31")
+    return df[['Close']]
+
+
+# Step 2: Prepare training data
+def prepare_data(df):
+    df['Target'] = df['Close'].shift(-1)
+    df = df.dropna()
+    X = df[['Close']]
+    y = df['Target']
+    return X, y
+
+
+# Step 3: Train a simple regression model
+def train_model(X, y):
+    model = LinearRegression()
+    model.fit(X, y)
+    return model
+
+
+# Step 4: Predict future prices (next 7 days)
+def predict_future(model, last_price, days=7):
+    predictions = []
+    current = last_price
+    for _ in range(days):
+        next_price = model.predict([[current]])[0]
+        predictions.append(next_price)
+        current = next_price
+    return predictions
+
+
+# Step 5: Generate AI explanation using Hugging Face summarizer
+def generate_explanation(df_tail):
+    text_data = df_tail.to_string()
+    summary = summarizer(text_data, max_length=100, min_length=30, do_sample=False)
+    return summary[0]['summary_text']
+
+
+# Step 6: Combine all into a Gradio interface function
+def stock_predictor(ticker):
+    try:
+        df = get_stock_data(ticker)
+        X, y = prepare_data(df)
+        model = train_model(X, y)
+        last_price = X.iloc[-1][0]
+        predictions = predict_future(model, last_price)
+
+        # Convert predictions to a plot
+        plt.figure()
+        plt.plot(range(1, 8), predictions, marker='o')
+        plt.title(f"{ticker} Stock Price Predictions (Next 7 Days)")
+        plt.xlabel("Days")
+        plt.ylabel("Predicted Price")
+        plt.grid(True)
+        plot_path = "prediction_plot.png"
+        plt.savefig(plot_path)
+        plt.close()
+
+        # Generate explanation using last 5 days of data
+        explanation = generate_explanation(df.tail())
+
+        return plot_path, predictions, explanation
+
+    except Exception as e:
+        return None, [], f"Error: {str(e)}"
+
+
+# Step 7: Gradio Interface
+interface = gr.Interface(
+    fn=stock_predictor,
+    inputs=gr.Textbox(label="Enter Stock Ticker (e.g., AAPL)"),
+    outputs=[
+        gr.Image(label="📈 Predicted Price Plot"),
+        gr.Textbox(label="📊 Predicted Prices (Next 7 Days)"),
+        gr.Textbox(label="🧠 AI Explanation of Recent Trends")
+    ],
+    title="Stock Price Predictor + AI Insight Generator",
+    description="Enter a stock ticker (like AAPL or GOOG) to see predicted prices and a natural language explanation of recent trends using Hugging Face models."
+)
+
+interface.launch()