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import streamlit as st
import google.generativeai as genai
import requests
import subprocess
import os
import pylint
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
import git
import spacy
import boto3
import unittest
import sympy as sp
from scipy.optimize import differential_evolution
import matplotlib.pyplot as plt
import seaborn as sns
from tenacity import retry, stop_after_attempt, wait_fixed
import networkx as nx
from scipy.stats import ttest_ind
import nltk
from nltk.sentiment import SentimentIntensityAnalyzer
from PIL import Image
import logging
# Initialize NLTK and spaCy
nltk.download('punkt', quiet=True)
nltk.download('vader_lexicon', quiet=True)
try:
nlp = spacy.load("en_core_web_sm")
except:
spacy.cli.download("en_core_web_sm")
nlp = spacy.load("en_core_web_sm")
# Configure Gemini
genai.configure(api_key=st.secrets["GOOGLE_API_KEY"])
generation_config = {
"temperature": 0.4,
"top_p": 0.8,
"top_k": 50,
"max_output_tokens": 2048,
}
model = genai.GenerativeModel(
model_name="gemini-1.5-pro-latest",
generation_config=generation_config,
system_instruction="You are Ath, an advanced AI coding assistant. Provide secure, efficient code with clear explanations."
)
# Streamlit UI Configuration
st.set_page_config(page_title="AI Code Assistant", page_icon="💻", layout="wide")
st.markdown("""
<style>
.main-container {padding: 2rem; border-radius: 10px; background: #f8f9fa;}
.code-block {background: #1e1e1e; color: #d4d4d4; padding: 1rem; border-radius: 5px;}
</style>
""", unsafe_allow_html=True)
# Core Functions
@retry(stop=stop_after_attempt(3), wait=wait_fixed(1))
def generate_response(prompt):
try:
response = model.generate_content(prompt)
return response.text
except Exception as e:
return f"Error: {str(e)}"
def process_user_input(text):
try:
doc = nlp(text)
return {
'tokens': [token.text for token in doc],
'entities': [(ent.text, ent.label_) for ent in doc.ents],
'sentiment': SentimentIntensityAnalyzer().polarity_scores(text)
}
except Exception as e:
st.error(f"NLP Error: {str(e)}")
return text
def optimize_code(code):
try:
with open("temp.py", "w") as f:
f.write(code)
result = subprocess.run(["pylint", "temp.py"], capture_output=True, text=True)
os.remove("temp.py")
return code, result.stdout
except Exception as e:
return code, f"Optimization Error: {str(e)}"
# Streamlit UI Components
st.title("💻 AI Code Assistant")
st.markdown("### Generate, Optimize, and Deploy Code")
task_type = st.selectbox("Select Task Type", [
"Code Generation",
"Data Analysis",
"NLP Processing",
"Math Solving"
])
prompt = st.text_area("Enter your request:", height=150)
if st.button("Generate Solution"):
if not prompt.strip():
st.error("Please enter a valid prompt")
else:
with st.spinner("Processing..."):
try:
# Process input
processed = process_user_input(prompt)
# Generate response
response = generate_response(prompt)
# Display results
with st.expander("Generated Solution", expanded=True):
if task_type == "Code Generation":
optimized, lint = optimize_code(response)
st.code(optimized, language='python')
st.write("Code Analysis:")
st.text(lint[:1000]) # Show first 1000 chars
elif task_type == "Data Analysis":
df = pd.DataFrame(np.random.randn(50, 4), columns=['A','B','C','D'])
st.write("Sample Analysis:")
st.dataframe(df.describe())
fig, ax = plt.subplots()
df.plot.kde(ax=ax)
st.pyplot(fig)
elif task_type == "NLP Processing":
st.json(processed)
elif task_type == "Math Solving":
solution = sp.solve(prompt)
st.latex(f"Solution: {solution}")
st.success("Processing complete!")
except Exception as e:
st.error(f"Error: {str(e)}")
# Sidebar Utilities
st.sidebar.header("Tools")
if st.sidebar.button("Clear Cache"):
st.cache_data.clear()
st.success("Cache cleared!")
st.sidebar.markdown("""
---
**About**
AI Code Assistant v2.0
Powered by Gemini Pro
[GitHub Repo](https://github.com/your-repo)
""") |