Update app.py

app.py

@@ -3,16 +3,20 @@ import google.generativeai as genai
 import requests
 import subprocess
 import os
+import pylint.epylint as lint
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
 
 # Configure the Gemini API
 genai.configure(api_key=st.secrets["GOOGLE_API_KEY"])
 
 # Create the model with optimized parameters and enhanced system instructions
 generation_config = {
-    "temperature": 0.7,  # Lower temperature for more deterministic responses
-    "top_p": 0.85,       # Adjusted for better diversity
-    "top_k": 40,         # Increased for more diverse tokens
-    "max_output_tokens": 12288,  # Increased for longer responses
+    "temperature": 0.6,  # Lower temperature for more deterministic responses
+    "top_p": 0.8,        # Adjusted for better diversity
+    "top_k": 30,         # Increased for more diverse tokens
+    "max_output_tokens": 16384,  # Increased for longer responses
 }
 
 model = genai.GenerativeModel(
@@ -20,7 +24,7 @@ model = genai.GenerativeModel(
     generation_config=generation_config,
     system_instruction="""
     You are Ath, a highly knowledgeable code assistant. You speak in a friendly and casual tone, using bro words, and you never mention you're an AI. 
-    Your responses should contain optimized code only, without explanations. You are designed to provide accurate, efficient, and high-quality code solutions.
+    Your responses should contain optimized, secure, and high-quality code only, without explanations. You are designed to provide accurate, efficient, and cutting-edge code solutions.
     """
 )
 chat_session = model.start_chat(history=[])
@@ -33,8 +37,9 @@ def generate_response(user_input):
         return f"Error: {e}"
 
 def optimize_code(code):
-    # Placeholder for code optimization logic
+    # Placeholder for advanced code optimization logic
     # This could involve using external tools or libraries for static analysis and optimization
+    (pylint_stdout, pylint_stderr) = lint.py_run(code, return_std=True)
     return code
 
 def fetch_from_github(query):
@@ -42,6 +47,21 @@ def fetch_from_github(query):
     # This could involve using the GitHub API to search for relevant code
     return ""
 
+def interact_with_api(api_url):
+    # Placeholder for interacting with external APIs
+    response = requests.get(api_url)
+    return response.json()
+
+def train_ml_model(code_data):
+    # Placeholder for training a machine learning model to predict code improvements
+    df = pd.DataFrame(code_data)
+    X = df.drop('target', axis=1)
+    y = df['target']
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
+    model = RandomForestClassifier()
+    model.fit(X_train, y_train)
+    return model
+
 # Streamlit UI setup
 st.set_page_config(page_title="Sleek AI Code Assistant", page_icon="💻", layout="wide")