Update app.py

app.py

@@ -3,146 +3,83 @@ 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 torch
-import torch.nn as nn
-import torch.optim as optim
-from transformers import AutoTokenizer, AutoModel
-import ast
-import networkx as nx
-import matplotlib.pyplot as plt
+import git
 
 # 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,
-    "top_p": 0.8,
-    "top_k": 30,
-    "max_output_tokens": 16384,
+    "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(
     model_name="gemini-1.5-pro",
     generation_config=generation_config,
     system_instruction="""
-    You are Ath, a highly advanced code assistant with deep knowledge in AI, machine learning, and software engineering. You provide cutting-edge, optimized, and secure code solutions. Speak casually and use tech jargon when appropriate.and high-quality code only, without explanations.
+    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.
     """
 )
 chat_session = model.start_chat(history=[])
 
-# Load pre-trained BERT model for code understanding
-tokenizer = AutoTokenizer.from_pretrained("microsoft/codebert-base")
-codebert_model = AutoModel.from_pretrained("microsoft/codebert-base")
-
-class CodeImprovement(nn.Module):
-    def __init__(self, input_dim):
-        super(CodeImprovement, self).__init__()
-        self.fc1 = nn.Linear(input_dim, 512)
-        self.fc2 = nn.Linear(512, 256)
-        self.fc3 = nn.Linear(256, 128)
-        self.fc4 = nn.Linear(128, 2)  # Binary classification: needs improvement or not
-
-    def forward(self, x):
-        x = torch.relu(self.fc1(x))
-        x = torch.relu(self.fc2(x))
-        x = torch.relu(self.fc3(x))
-        return torch.sigmoid(self.fc4(x))
-
-code_improvement_model = CodeImprovement(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 in generating response: {str(e)}"
-
-def validate_and_fix_code(code):
-    lines = code.split('\n')
-    fixed_lines = []
-    for line in lines:
-        # Check for unterminated string literals
-        if line.count('"') % 2 != 0 and line.count("'") % 2 != 0:
-            line += '"'  # Add a closing quote if needed
-        fixed_lines.append(line)
-    return '\n'.join(fixed_lines)
+        return f"Error: {e}"
 
 def optimize_code(code):
-    # Validate and fix the code first
-    fixed_code = validate_and_fix_code(code)
-    
-    try:
-        tree = ast.parse(fixed_code)
-        # Placeholder for actual optimization logic
-        optimized_code = fixed_code
-    except SyntaxError as e:
-        return fixed_code, f"SyntaxError: {str(e)}"
-    
-    # Run pylint for additional suggestions
+    # 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(optimized_code)
+        file.write(code)
     result = subprocess.run(["pylint", "temp_code.py"], capture_output=True, text=True)
     os.remove("temp_code.py")
-    
-    return optimized_code, result.stdout
+    return code
 
 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):
-    # Tokenize and encode the code
-    inputs = tokenizer(code, return_tensors="pt", truncation=True, max_length=512, padding="max_length")
-    
-    # Get BERT embeddings
-    with torch.no_grad():
-        outputs = codebert_model(**inputs)
-    
-    # Use the [CLS] token embedding for classification
-    cls_embedding = outputs.last_hidden_state[:, 0, :]
-    
-    # Pass through our code improvement model
-    prediction = code_improvement_model(cls_embedding)
-    
-    return prediction.item()  # Return the probability of needing improvement
-
-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=type(node).__name__)
-            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=(12, 8))
-        pos = nx.spring_layout(graph)
-        nx.draw(graph, pos, with_labels=True, node_color='lightblue', node_size=1000, font_size=8, font_weight='bold')
-        labels = nx.get_node_attributes(graph, 'label')
-        nx.draw_networkx_labels(graph, pos, labels, font_size=6)
-        
-        return plt
-    except SyntaxError:
-        return None
+    # 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")
 
 # Streamlit UI setup
-st.set_page_config(page_title="Advanced AI Code Assistant", page_icon="🚀", layout="wide")
+st.set_page_config(page_title="Sleek AI Code Assistant", page_icon="💻", layout="wide")
 
 st.markdown("""
 <style>
@@ -154,7 +91,7 @@ st.markdown("""
         color: #1a202c;
     }
     .stApp {
-        max-width: 1200px;
+        max-width: 1000px;
         margin: 0 auto;
         padding: 2rem;
     }
@@ -232,55 +169,39 @@ st.markdown("""
 """, unsafe_allow_html=True)
 
 st.markdown('<div class="main-container">', unsafe_allow_html=True)
-st.title("🚀 Advanced AI Code Assistant")
-st.markdown('<p class="subtitle">Powered by Google Gemini & Deep Learning</p>', unsafe_allow_html=True)
+st.title("💻 Sleek AI Code Assistant")
+st.markdown('<p class="subtitle">Powered by Google Gemini</p>', unsafe_allow_html=True)
 
-prompt = st.text_area("What advanced code task can I help you with today?", height=120)
+prompt = st.text_area("What code can I help you with today?", height=120)
 
-if st.button("Generate Advanced Code"):
+if st.button("Generate Code"):
     if prompt.strip() == "":
         st.error("Please enter a valid prompt.")
     else:
-        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 "SyntaxError" in lint_results:
-                    st.warning(f"Syntax error 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.")
+        with st.spinner("Generating code..."):
+            try:
+                completed_text = generate_response(prompt)
+                if "Error" in completed_text:
+                    handle_error(completed_text)
                 else:
-                    quality_score = analyze_code_quality(optimized_code)
-                    st.success(f"Code generated and optimized successfully! Quality Score: {quality_score:.2f}")
+                    optimized_code = optimize_code(completed_text)
+                    st.success("Code generated and optimized successfully!")
                     
                     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)
-                    
-                    visualization = visualize_code_structure(optimized_code)
-                    if visualization:
-                        with st.expander("View Code Structure Visualization"):
-                            st.pyplot(visualization)
-                    else:
-                        st.warning("Unable to generate code structure visualization due to syntax errors.")
-                    
-                    with st.expander("View Lint Results"):
-                        st.text(lint_results)
-                    
-                    with st.expander("Fetch 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)
+                    
+                    # Integrate with Git
+                    repo_path = "./repo"  # Replace with your repository path
+                    integrate_with_git(repo_path, optimized_code)
+            except Exception as e:
+                handle_error(e)
 
 st.markdown("""
 <div style='text-align: center; margin-top: 2rem; color: #4a5568;'>
-    Crafted with 🚀 by Your Advanced AI Code Assistant
+    Created with ❤️ by Your Sleek AI Code Assistant
 </div>
 """, unsafe_allow_html=True)