Update app.py

app.py

@@ -20,6 +20,14 @@ import matplotlib.pyplot as plt
 import seaborn as sns
 from IPython.display import display
 from tenacity import retry, stop_after_attempt, wait_fixed
+from transformers import pipeline
+import tensorflow as tf
+import torch
+import json
+import logging
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
 
 # Configure the Gemini API
 genai.configure(api_key=st.secrets["GOOGLE_API_KEY"])
@@ -49,6 +57,7 @@ def generate_response(user_input):
         response = chat_session.send_message(user_input)
         return response.text
     except Exception as e:
+        logging.error(f"Error generating response: {e}")
         return f"Error: {e}"
 
 def optimize_code(code):
@@ -81,6 +90,7 @@ def train_ml_model(code_data):
 
 def handle_error(error):
     """Handle errors and log them."""
+    logging.error(f"An error occurred: {error}")
     st.error(f"An error occurred: {error}")
 
 def initialize_git_repo(repo_path):
@@ -142,6 +152,7 @@ def execute_code_in_docker(code):
         logs = container.logs().decode('utf-8')
         return logs, result['StatusCode']
     except Exception as e:
+        logging.error(f"Error executing code in Docker: {e}")
         return f"Error: {e}", 1
 
 def solve_equation(equation):
@@ -175,6 +186,38 @@ def display_dataframe(data):
     df = pd.DataFrame(data)
     display(df)
 
+def generate_text(prompt):
+    """Generate text using a pre-trained transformer model."""
+    generator = pipeline('text-generation', model='gpt2')
+    result = generator(prompt, max_length=50, num_return_sequences=1)
+    return result[0]['generated_text']
+
+def classify_text(text):
+    """Classify text using a pre-trained transformer model."""
+    classifier = pipeline('sentiment-analysis')
+    result = classifier(text)
+    return result
+
+def predict_with_tensorflow(model_path, data):
+    """Make predictions using a TensorFlow model."""
+    model = tf.keras.models.load_model(model_path)
+    predictions = model.predict(data)
+    return predictions
+
+def predict_with_pytorch(model_path, data):
+    """Make predictions using a PyTorch model."""
+    model = torch.load(model_path)
+    model.eval()
+    with torch.no_grad():
+        predictions = model(data)
+    return predictions
+
+def load_json_config(config_path):
+    """Load a JSON configuration file."""
+    with open(config_path, 'r') as file:
+        config = json.load(file)
+    return config
+
 # Streamlit UI setup
 st.set_page_config(page_title="Ultra AI Code Assistant", page_icon="🚀", layout="wide")