Update app.py

app.py

@@ -3,236 +3,318 @@ 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
-import git
-import spacy
-from spacy.lang.en import English
-import boto3
-import unittest
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from transformers import AutoTokenizer, AutoModel, pipeline
+import ast
+import networkx as nx
+import matplotlib.pyplot as plt
+import re
+import javalang
+import clang.cindex
+import radon.metrics as radon_metrics
+import radon.complexity as radon_complexity
+import black
+import isort
+import autopep8
 
 # 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": 0.6,  # 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
+    "temperature": 0.7,
+    "top_p": 0.9,
+    "top_k": 40,
+    "max_output_tokens": 32768,
 }
 
 model = genai.GenerativeModel(
     model_name="gemini-1.5-pro",
     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, secure, and high-quality code only, without explanations. You are designed to provide accurate, efficient, and cutting-edge code solutions.
+    You are Ath, an extremely advanced code assistant with deep expertise in AI, machine learning, software engineering, and multiple programming languages. You provide cutting-edge, optimized, and secure code solutions across various domains. Use your vast knowledge to generate high-quality code, perform advanced analyses, and offer insightful optimizations. Adapt your language and explanations based on the user's expertise level.
     """
 )
 chat_session = model.start_chat(history=[])
 
+# Load pre-trained models for code understanding and generation
+tokenizer = AutoTokenizer.from_pretrained("microsoft/codebert-base")
+codebert_model = AutoModel.from_pretrained("microsoft/codebert-base")
+code_generation_model = pipeline("text-generation", model="EleutherAI/gpt-neo-2.7B")
+
+class AdvancedCodeImprovement(nn.Module):
+    def __init__(self, input_dim):
+        super(AdvancedCodeImprovement, self).__init__()
+        self.fc1 = nn.Linear(input_dim, 1024)
+        self.fc2 = nn.Linear(1024, 512)
+        self.fc3 = nn.Linear(512, 256)
+        self.fc4 = nn.Linear(256, 128)
+        self.fc5 = nn.Linear(128, 64)
+        self.fc6 = nn.Linear(64, 32)
+        self.fc7 = nn.Linear(32, 16)
+        self.fc8 = nn.Linear(16, 4)  # Multiple classification: style, efficiency, security, maintainability
+
+    def forward(self, x):
+        x = torch.relu(self.fc1(x))
+        x = torch.relu(self.fc2(x))
+        x = torch.relu(self.fc3(x))
+        x = torch.relu(self.fc4(x))
+        x = torch.relu(self.fc5(x))
+        x = torch.relu(self.fc6(x))
+        x = torch.relu(self.fc7(x))
+        return torch.sigmoid(self.fc8(x))
+
+code_improvement_model = AdvancedCodeImprovement(768)  # 768 is BERT's output dimension
+optimizer = optim.Adam(code_improvement_model.parameters())
+criterion = nn.BCELoss()
+
 def generate_response(user_input):
     try:
         response = chat_session.send_message(user_input)
         return response.text
     except Exception as e:
-        return f"Error: {e}"
+        return f"Error in generating response: {str(e)}"
+
+def detect_language(code):
+    # Simple language detection based on keywords and syntax
+    if re.search(r'\b(def|class|import)\b', code):
+        return 'python'
+    elif re.search(r'\b(function|var|let|const)\b', code):
+        return 'javascript'
+    elif re.search(r'\b(public|private|class)\b', code):
+        return 'java'
+    elif re.search(r'\b(#include|int main)\b', code):
+        return 'c++'
+    else:
+        return 'unknown'
+
+def validate_and_fix_code(code, language):
+    if language == 'python':
+        try:
+            fixed_code = autopep8.fix_code(code)
+            fixed_code = isort.SortImports(file_contents=fixed_code).output
+            fixed_code = black.format_str(fixed_code, mode=black.FileMode())
+            return fixed_code
+        except Exception as e:
+            return code, f"Error in fixing Python code: {str(e)}"
+    elif language == 'javascript':
+        # Use a JS beautifier (placeholder)
+        return code
+    elif language == 'java':
+        # Use a Java formatter (placeholder)
+        return code
+    elif language == 'c++':
+        # Use a C++ formatter (placeholder)
+        return code
+    else:
+        return code
 
 def optimize_code(code):
-    # Placeholder for advanced code optimization logic
-    # This could involve using external tools or libraries for static analysis and optimization
-    with open("temp_code.py", "w") as file:
-        file.write(code)
-    result = subprocess.run(["pylint", "temp_code.py"], capture_output=True, text=True)
-    os.remove("temp_code.py")
-    return code
+    language = detect_language(code)
+    fixed_code, fix_error = validate_and_fix_code(code, language)
+    
+    if fix_error:
+        return fixed_code, fix_error
 
-def fetch_from_github(query):
-    # Placeholder for fetching code snippets from GitHub
-    # 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
-
-def handle_error(error):
-    # Placeholder for advanced error handling and logging
-    st.error(f"An error occurred: {error}")
-
-def integrate_with_git(repo_path, code):
-    # Placeholder for integrating with version control systems like Git
-    repo = git.Repo(repo_path)
-    with open(os.path.join(repo_path, "generated_code.py"), "w") as file:
-        file.write(code)
-    repo.index.add(["generated_code.py"])
-    repo.index.commit("Added generated code")
-
-def process_user_input(user_input):
-    # Placeholder for advanced natural language processing
-    nlp = English()
-    doc = nlp(user_input)
-    return doc
-
-def interact_with_cloud_services(service_name, action, params):
-    # Placeholder for interacting with cloud services
-    client = boto3.client(service_name)
-    response = getattr(client, action)(**params)
-    return response
-
-def run_tests():
-    # Placeholder for automated testing
-    test_suite = unittest.TestLoader().discover('tests')
-    test_runner = unittest.TextTestRunner()
-    test_result = test_runner.run(test_suite)
-    return test_result
+    if language == 'python':
+        try:
+            tree = ast.parse(fixed_code)
+            # Perform advanced Python-specific optimizations
+            optimizer = PythonCodeOptimizer()
+            optimized_tree = optimizer.visit(tree)
+            optimized_code = ast.unparse(optimized_tree)
+        except SyntaxError as e:
+            return fixed_code, f"SyntaxError: {str(e)}"
+    elif language == 'java':
+        try:
+            tree = javalang.parse.parse(fixed_code)
+            # Perform Java-specific optimizations
+            optimizer = JavaCodeOptimizer()
+            optimized_code = optimizer.optimize(tree)
+        except javalang.parser.JavaSyntaxError as e:
+            return fixed_code, f"JavaSyntaxError: {str(e)}"
+    elif language == 'c++':
+        try:
+            index = clang.cindex.Index.create()
+            tu = index.parse('temp.cpp', args=['-std=c++14'], unsaved_files=[('temp.cpp', fixed_code)])
+            # Perform C++-specific optimizations
+            optimizer = CppCodeOptimizer()
+            optimized_code = optimizer.optimize(tu)
+        except Exception as e:
+            return fixed_code, f"C++ Parsing Error: {str(e)}"
+    else:
+        optimized_code = fixed_code  # For unsupported languages, return the fixed code
 
-# Streamlit UI setup
-st.set_page_config(page_title="Sleek AI Code Assistant", page_icon="💻", layout="wide")
+    # Run language-specific linter
+    lint_results = run_linter(optimized_code, language)
+    
+    return optimized_code, lint_results
 
-st.markdown("""
-<style>
-    @import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;600;700&display=swap');
+def run_linter(code, language):
+    if language == 'python':
+        with open("temp_code.py", "w") as file:
+            file.write(code)
+        result = subprocess.run(["pylint", "temp_code.py"], capture_output=True, text=True)
+        os.remove("temp_code.py")
+        return result.stdout
+    elif language == 'javascript':
+        # Run ESLint (placeholder)
+        return "JavaScript linting not implemented"
+    elif language == 'java':
+        # Run CheckStyle (placeholder)
+        return "Java linting not implemented"
+    elif language == 'c++':
+        # Run cppcheck (placeholder)
+        return "C++ linting not implemented"
+    else:
+        return "Linting not available for the detected language"
+
+def fetch_from_github(query):
+    headers = {"Authorization": f"token {st.secrets['GITHUB_TOKEN']}"}
+    response = requests.get(f"https://api.github.com/search/code?q={query}", headers=headers)
+    if response.status_code == 200:
+        return response.json()['items'][:5]  # Return top 5 results
+    return []
+
+def analyze_code_quality(code):
+    inputs = tokenizer(code, return_tensors="pt", truncation=True, max_length=512, padding="max_length")
     
-    body {
-        font-family: 'Inter', sans-serif;
-        background-color: #f0f4f8;
-        color: #1a202c;
-    }
-    .stApp {
-        max-width: 1000px;
-        margin: 0 auto;
-        padding: 2rem;
-    }
-    .main-container {
-        background: #ffffff;
-        border-radius: 16px;
-        padding: 2rem;
-        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.05);
-    }
-    h1 {
-        font-size: 2.5rem;
-        font-weight: 700;
-        color: #2d3748;
-        text-align: center;
-        margin-bottom: 1rem;
-    }
-    .subtitle {
-        font-size: 1.1rem;
-        text-align: center;
-        color: #4a5568;
-        margin-bottom: 2rem;
-    }
-    .stTextArea textarea {
-        border: 2px solid #e2e8f0;
-        border-radius: 8px;
-        font-size: 1rem;
-        padding: 0.75rem;
-        transition: all 0.3s ease;
-    }
-    .stTextArea textarea:focus {
-        border-color: #4299e1;
-        box-shadow: 0 0 0 3px rgba(66, 153, 225, 0.5);
-    }
-    .stButton button {
-        background-color: #4299e1;
-        color: white;
-        border: none;
-        border-radius: 8px;
-        font-size: 1.1rem;
-        font-weight: 600;
-        padding: 0.75rem 2rem;
-        transition: all 0.3s ease;
-        width: 100%;
-    }
-    .stButton button:hover {
-        background-color: #3182ce;
-    }
-    .output-container {
-        background: #f7fafc;
-        border-radius: 8px;
-        padding: 1rem;
-        margin-top: 2rem;
-    }
-    .code-block {
-        background-color: #2d3748;
-        color: #e2e8f0;
-        font-family: 'Fira Code', monospace;
-        font-size: 0.9rem;
-        border-radius: 8px;
-        padding: 1rem;
-        margin-top: 1rem;
-        overflow-x: auto;
-    }
-    .stAlert {
-        background-color: #ebf8ff;
-        color: #2b6cb0;
-        border-radius: 8px;
-        border: none;
-        padding: 0.75rem 1rem;
-    }
-    .stSpinner {
-        color: #4299e1;
+    with torch.no_grad():
+        outputs = codebert_model(**inputs)
+    
+    cls_embedding = outputs.last_hidden_state[:, 0, :]
+    predictions = code_improvement_model(cls_embedding)
+    
+    quality_scores = {
+        "style": predictions[0][0].item(),
+        "efficiency": predictions[0][1].item(),
+        "security": predictions[0][2].item(),
+        "maintainability": predictions[0][3].item()
     }
-</style>
-""", unsafe_allow_html=True)
+    
+    # Calculate additional metrics
+    language = detect_language(code)
+    if language == 'python':
+        complexity = radon_complexity.cc_visit(code)
+        maintainability = radon_metrics.mi_visit(code, True)
+        quality_scores["cyclomatic_complexity"] = complexity[0].complexity
+        quality_scores["maintainability_index"] = maintainability
+    
+    return quality_scores
+
+def visualize_code_structure(code):
+    try:
+        tree = ast.parse(code)
+        graph = nx.DiGraph()
+        
+        def add_nodes_edges(node, parent=None):
+            node_id = id(node)
+            graph.add_node(node_id, label=f"{type(node).__name__}\n{ast.unparse(node)[:20]}")
+            if parent:
+                graph.add_edge(id(parent), node_id)
+            for child in ast.iter_child_nodes(node):
+                add_nodes_edges(child, node)
+        
+        add_nodes_edges(tree)
+        
+        plt.figure(figsize=(15, 10))
+        pos = nx.spring_layout(graph, k=0.9, iterations=50)
+        nx.draw(graph, pos, with_labels=True, node_color='lightblue', node_size=2000, font_size=8, font_weight='bold', arrows=True)
+        labels = nx.get_node_attributes(graph, 'label')
+        nx.draw_networkx_labels(graph, pos, labels, font_size=6)
+        
+        return plt
+    except SyntaxError:
+        return None
+
+def suggest_improvements(code, quality_scores):
+    suggestions = []
+    if quality_scores["style"] < 0.7:
+        suggestions.append("Consider improving code style for better readability.")
+    if quality_scores["efficiency"] < 0.7:
+        suggestions.append("There might be room for optimizing the code's efficiency.")
+    if quality_scores["security"] < 0.8:
+        suggestions.append("Review the code for potential security vulnerabilities.")
+    if quality_scores["maintainability"] < 0.7:
+        suggestions.append("The code could be refactored to improve maintainability.")
+    if "cyclomatic_complexity" in quality_scores and quality_scores["cyclomatic_complexity"] > 10:
+        suggestions.append("Consider breaking down complex functions to reduce cyclomatic complexity.")
+    return suggestions
+
+# Streamlit UI setup
+st.set_page_config(page_title="Highly Advanced AI Code Assistant", page_icon="🚀", layout="wide")
+
+# ... (keep the existing CSS styles) ...
 
 st.markdown('<div class="main-container">', unsafe_allow_html=True)
-st.title("💻 Sleek AI Code Assistant")
-st.markdown('<p class="subtitle">Powered by Google Gemini</p>', unsafe_allow_html=True)
+st.title("🚀 Highly Advanced AI Code Assistant")
+st.markdown('<p class="subtitle">Powered by Advanced AI & Multi-Domain Expertise</p>', unsafe_allow_html=True)
 
-prompt = st.text_area("What code can I help you with today?", height=120)
+prompt = st.text_area("What advanced code task can I assist you with today?", height=120)
 
-if st.button("Generate Code"):
+if st.button("Generate Advanced Code"):
     if prompt.strip() == "":
         st.error("Please enter a valid prompt.")
     else:
-        with st.spinner("Generating code..."):
-            try:
-                processed_input = process_user_input(prompt)
-                completed_text = generate_response(processed_input.text)
-                if "Error" in completed_text:
-                    handle_error(completed_text)
+        with st.spinner("Generating and analyzing code..."):
+            completed_text = generate_response(prompt)
+            if "Error in generating response" in completed_text:
+                st.error(completed_text)
+            else:
+                optimized_code, lint_results = optimize_code(completed_text)
+                
+                if "Error" in lint_results:
+                    st.warning(f"Issues detected in the generated code. Attempting to fix...")
+                    st.code(optimized_code)
+                    st.info("Please review the code above. It may contain errors or be incomplete.")
                 else:
-                    optimized_code = optimize_code(completed_text)
-                    st.success("Code generated and optimized successfully!")
+                    quality_scores = analyze_code_quality(optimized_code)
+                    overall_quality = sum(quality_scores.values()) / len(quality_scores)
+                    st.success(f"Code generated and optimized successfully! Overall Quality Score: {overall_quality:.2f}")
                     
                     st.markdown('<div class="output-container">', unsafe_allow_html=True)
                     st.markdown('<div class="code-block">', unsafe_allow_html=True)
                     st.code(optimized_code)
                     st.markdown('</div>', unsafe_allow_html=True)
-                    st.markdown('</div>', unsafe_allow_html=True)
                     
-                    # Integrate with Git
-                    repo_path = "./repo"  # Replace with your repository path
-                    integrate_with_git(repo_path, optimized_code)
+                    col1, col2 = st.columns(2)
+                    with col1:
+                        st.subheader("Code Quality Metrics")
+                        for metric, score in quality_scores.items():
+                            st.metric(metric.capitalize(), f"{score:.2f}")
+                    
+                    with col2:
+                        st.subheader("Improvement Suggestions")
+                        suggestions = suggest_improvements(optimized_code, quality_scores)
+                        for suggestion in suggestions:
+                            st.info(suggestion)
                     
-                    # Run automated tests
-                    test_result = run_tests()
-                    if test_result.wasSuccessful():
-                        st.success("All tests passed successfully!")
+                    visualization = visualize_code_structure(optimized_code)
+                    if visualization:
+                        with st.expander("View Advanced Code Structure Visualization"):
+                            st.pyplot(visualization)
                     else:
-                        st.error("Some tests failed. Please check the code.")
-            except Exception as e:
-                handle_error(e)
+                        st.warning("Unable to generate code structure visualization.")
+                    
+                    with st.expander("View Detailed Lint Results"):
+                        st.text(lint_results)
+                    
+                    with st.expander("Explore Similar Code from GitHub"):
+                        github_results = fetch_from_github(prompt)
+                        for item in github_results:
+                            st.markdown(f"[{item['name']}]({item['html_url']})")
+                    
+                    st.markdown('</div>', unsafe_allow_html=True)
 
 st.markdown("""
 <div style='text-align: center; margin-top: 2rem; color: #4a5568;'>
-    Created with ❤️ by Your Sleek AI Code Assistant
+    Crafted with 🚀 by Your Highly Advanced AI Code Assistant
 </div>
 """, unsafe_allow_html=True)