Update app.py

app.py

@@ -1,65 +1,115 @@
-import cv2
-import gradio as gr
 import torch
-from diffusers import StableDiffusionPipeline
+import transformers
 import numpy as np
-from transformers.utils import move_cache
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-
-# Handle Transformers cache migration
-move_cache()
+import gradio as gr
+import openai
+from sklearn.preprocessing import MinMaxScaler
+import matplotlib.pyplot as plt
+from datetime import datetime, timedelta
 
-# Initialize the Stable Diffusion pipeline
-model_id = "CompVis/stable-diffusion-v1-4"
-pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
-pipe = pipe.to("cuda")
+# Load Hugging Face model for financial insights generation
+generator = transformers.pipeline("text-generation", model="gpt2")
 
-# Load text summarizer
-summarizer_model = "facebook/bart-large-cnn"
-tokenizer = AutoTokenizer.from_pretrained(summarizer_model)
-summarizer = AutoModelForSeq2SeqLM.from_pretrained(summarizer_model)
+# Sample function for fetching stock market data (you can use libraries like yfinance or Alpha Vantage)
+def get_stock_data(symbol, start_date, end_date):
+    # This is just a placeholder. In production, use an API to get actual stock data
+    dates = np.array([start_date + timedelta(days=i) for i in range((end_date - start_date).days)])
+    prices = np.sin(np.linspace(0, 20, len(dates))) * 100 + 1000  # Placeholder for stock prices
+    return dates, prices
 
-# Create video from images using `OpenCV`
-def text_to_video(input_text, num_frames=10, fps=2):
-    # Summarize the input text
-    inputs = tokenizer(input_text, return_tensors="pt", truncation=True)
-    summary_ids = summarizer.generate(inputs["input_ids"], max_length=30, min_length=5, length_penalty=2.0)
-    prompt = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+# Preprocessing stock data for prediction
+def preprocess_data(prices):
+    scaler = MinMaxScaler(feature_range=(0, 1))
+    prices_scaled = scaler.fit_transform(prices.reshape(-1, 1))
+    return scaler, prices_scaled
 
-    # Generate frames
-    frames = []
-    for i in range(num_frames):
-        prompt_with_frame = f"{prompt}, frame {i+1}"
-        image = pipe(prompt_with_frame).images[0]
-        frames.append(np.array(image))
+# LSTM-based deep learning model for stock price prediction
+class StockPredictor(torch.nn.Module):
+    def __init__(self, input_size=1, hidden_size=50, num_layers=1):
+        super(StockPredictor, self).__init__()
+        self.lstm = torch.nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
+        self.fc = torch.nn.Linear(hidden_size, 1)
 
-    # Save frames as a video
-    height, width, layers = frames[0].shape
-    video_path = "output.avi"
-    out = cv2.VideoWriter(video_path, cv2.VideoWriter_fourcc(*'XVID'), fps, (width, height))
+    def forward(self, x):
+        out, _ = self.lstm(x)
+        out = self.fc(out[:, -1, :])
+        return out
 
-    for frame in frames:
-        out.write(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))
-    out.release()
+# Function to train the model on historical stock data
+def train_model(prices_scaled):
+    model = StockPredictor()
+    criterion = torch.nn.MSELoss()
+    optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
+    
+    # Prepare data for training
+    X_train, y_train = [], []
+    for i in range(len(prices_scaled) - 1):
+        X_train.append(prices_scaled[i:i+10])  # Using the last 10 days to predict next day's price
+        y_train.append(prices_scaled[i+10])
     
-    return video_path
+    X_train = torch.tensor(np.array(X_train), dtype=torch.float32)
+    y_train = torch.tensor(np.array(y_train), dtype=torch.float32)
+    
+    # Train the model
+    for epoch in range(100):
+        model.train()
+        optimizer.zero_grad()
+        outputs = model(X_train)
+        loss = criterion(outputs, y_train)
+        loss.backward()
+        optimizer.step()
+
+    return model
+
+# Predict future stock prices
+def predict_stock(model, scaler, input_data):
+    model.eval()
+    with torch.no_grad():
+        predictions = model(torch.tensor(input_data, dtype=torch.float32))
+    return scaler.inverse_transform(predictions.detach().numpy())
 
-# Gradio interface
-def generate_video(text, frames, fps):
-    video_file = text_to_video(text, num_frames=frames, fps=fps)
-    return video_file
+# Generate financial insights using Hugging Face's GPT model
+def generate_financial_insight(stock_symbol):
+    prompt = f"Generate a detailed financial report for the stock symbol {stock_symbol} based on recent market trends."
+    response = generator(prompt, max_length=150, num_return_sequences=1)
+    return response[0]['generated_text']
 
-interface = gr.Interface(
-    fn=generate_video,
-    inputs=[
-        gr.Textbox(label="Enter your text prompt"),
-        gr.Slider(5, 30, value=10, step=1, label="Number of Frames"),
-        gr.Slider(1, 10, value=2, step=1, label="Frames per Second (FPS)"),
-    ],
-    outputs=gr.Video(label="Generated Video"),
-    title="Text-to-Video Generator",
-    description="Enter a text prompt to generate a short video."
-)
+# Gradio interface function
+def financial_advisor(symbol):
+    start_date = datetime.now() - timedelta(days=365)  # Last year data
+    end_date = datetime.now()
+    
+    # Get stock data (for simplicity, using dummy data here)
+    dates, prices = get_stock_data(symbol, start_date, end_date)
+    
+    # Preprocess data for prediction
+    scaler, prices_scaled = preprocess_data(prices)
+    
+    # Train the model on historical data
+    model = train_model(prices_scaled)
+    
+    # Predict future prices (for simplicity, predicting the next 30 days)
+    future_data = prices_scaled[-10:].reshape(1, 10, 1)
+    predictions = predict_stock(model, scaler, future_data)
+    
+    # Generate financial insights
+    insight = generate_financial_insight(symbol)
+    
+    # Plot predicted prices
+    plt.plot(dates, prices, label="Historical Prices")
+    predicted_dates = [dates[-1] + timedelta(days=i) for i in range(1, 31)]
+    plt.plot(predicted_dates, predictions, label="Predicted Prices", linestyle="--")
+    plt.xlabel('Date')
+    plt.ylabel('Price')
+    plt.legend()
+    plt.xticks(rotation=45)
+    
+    # Return results to display in Gradio
+    return plt.gcf(), f"Financial Insight for {symbol}: {insight}"
 
-if __name__ == "__main__":
-    interface.launch()
+# Launch Gradio interface
+gr.Interface(
+    fn=financial_advisor, 
+    inputs=[gr.Textbox(label="Stock Symbol", placeholder="Enter Stock Symbol (e.g., AAPL)")], 
+    outputs=[gr.Plot(), gr.Textbox()]
+).launch()