Update app.py

app.py

@@ -1,297 +1,555 @@
-import requests
-from bs4 import BeautifulSoup
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-import seaborn as sns
-from datetime import datetime
-from nltk.corpus import stopwords
-from nltk.stem import WordNetLemmatizer
-from nltk.tokenize import word_tokenize
-from gensim.models import LdaModel
-from gensim.corpora import Dictionary
-from textblob import TextBlob
-from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
-import networkx as nx
-from sklearn.model_selection import train_test_split
-from sklearn.linear_model import LogisticRegression
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.metrics import accuracy_score, classification_report, roc_auc_score
-from sklearn.preprocessing import StandardScaler
-from sklearn.pipeline import Pipeline
-from sklearn.feature_extraction.text import TfidfVectorizer
-from scipy import linalg
-import plotly.graph_objects as go
-from collections import Counter
-import warnings
-warnings.filterwarnings("ignore")
-
-# Set up logging
-import logging
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-
-# Function to fetch HTML content from GitHub issue pages
-def fetch_issue_data(username, repository, start_page, end_page):
-    issues_data = []
-    for page in range(start_page, end_page + 1):
-        url = f"https://github.com/{username}/{repository}/issues?page={page}"
-        response = requests.get(url)
-        soup = BeautifulSoup(response.content, 'html.parser')
-        issue_elements = soup.find_all('div', class_='flex-shrink-0')
-        for issue_element in issue_elements:
-            issue_link = issue_element.find('a', class_='Link--primary')['href']
-            issue_url = f"https://github.com{issue_link}"
-            issue_data = fetch_issue_details(issue_url)
-            issues_data.append(issue_data)
-    return issues_data
-
-# Function to fetch details of a specific issue
-def fetch_issue_details(issue_url):
-    response = requests.get(issue_url)
-    soup = BeautifulSoup(response.content, 'html.parser')
-    issue_title = soup.find('h1', class_='gh-header-title').text.strip()
-    issue_body = soup.find('div', class_='markdown-body').text.strip()
-    issue_created_at = soup.find('relative-time')['datetime']
-    issue_closed_at = soup.find('relative-time', class_='no-wrap')
-    if issue_closed_at:
-        issue_closed_at = issue_closed_at['datetime']
-    else:
-        issue_closed_at = None
-    issue_author = soup.find('a', class_='author').text.strip()
-    issue_assignee = soup.find('a', class_='Link--muted')
-    if issue_assignee:
-        issue_assignee = issue_assignee.text.strip()
-    else:
-        issue_assignee = None
-    return {
-        'title': issue_title,
-        'body': issue_body,
-        'created_at': issue_created_at,
-        'closed_at': issue_closed_at,
-        'author': issue_author,
-        'assignee': issue_assignee
+import subprocess
+import streamlit as st
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer, AutoModel, RagRetriever, AutoModelForSeq2SeqLM
+import black
+from pylint import lint
+from io import StringIO
+import sys
+import torch
+from huggingface_hub import hf_hub_url, cached_download, HfApi
+
+# Set your Hugging Face API key here
+hf_token = "YOUR_HUGGING_FACE_API_KEY"  # Replace with your actual token
+
+HUGGING_FACE_REPO_URL = "https://huggingface.co/spaces/acecalisto3/DevToolKit"
+PROJECT_ROOT = "projects"
+AGENT_DIRECTORY = "agents"
+
+# Global state to manage communication between Tool Box and Workspace Chat App
+if 'chat_history' not in st.session_state:
+    st.session_state.chat_history = []
+if 'terminal_history' not in st.session_state:
+    st.session_state.terminal_history = []
+if 'workspace_projects' not in st.session_state:
+    st.session_state.workspace_projects = {}
+if 'available_agents' not in st.session_state:
+    st.session_state.available_agents = []
+if 'current_state' not in st.session_state:
+    st.session_state.current_state = {
+        'toolbox': {},
+        'workspace_chat': {}
     }
 
-# Function to clean and structure the data
-def clean_and_structure_data(issues_data):
-    df = pd.DataFrame(issues_data)
-    
-    # Check if 'created_at' column exists
-    if 'created_at' in df.columns:
-        df['created_at'] = pd.to_datetime(df['created_at'])
-    else:
-        logging.error("The 'created_at' column is missing from the dataframe.")
-        df['created_at'] = pd.NaT  # or use pd.to_datetime('now') for current time
-    
-    # Check if 'closed_at' column exists
-    if 'closed_at' in df.columns:
-        df['closed_at'] = pd.to_datetime(df['closed_at'])
+# List of top downloaded free code-generative models from Hugging Face Hub
+AVAILABLE_CODE_GENERATIVE_MODELS = [
+    "bigcode/starcoder",  # Popular and powerful
+    "Salesforce/codegen-350M-mono",  # Smaller, good for quick tasks
+    "microsoft/CodeGPT-small",  # Smaller, good for quick tasks
+    "google/flan-t5-xl",  # Powerful, good for complex tasks
+    "facebook/bart-large-cnn",  # Good for text-to-code tasks
+]
+
+# Load pre-trained RAG retriever
+rag_retriever = RagRetriever.from_pretrained("facebook/rag-token-base")  # Use a Hugging Face RAG model
+
+# Load pre-trained chat model
+chat_model = AutoModelForSeq2SeqLM.from_pretrained("microsoft/DialoGPT-medium")  # Use a Hugging Face chat model
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
+
+def process_input(user_input):
+    # Input pipeline: Tokenize and preprocess user input
+    input_ids = tokenizer(user_input, return_tensors="pt").input_ids
+    attention_mask = tokenizer(user_input, return_tensors="pt").attention_mask
+
+    # RAG model: Generate response
+    with torch.no_grad():
+        output = rag_retriever(input_ids, attention_mask=attention_mask)
+        response = output.generator_outputs[0].sequences[0]
+
+    # Chat model: Refine response
+    chat_input = tokenizer(response, return_tensors="pt")
+    chat_input["input_ids"] = chat_input["input_ids"].unsqueeze(0)
+    chat_input["attention_mask"] = chat_input["attention_mask"].unsqueeze(0)
+    with torch.no_grad():
+        chat_output = chat_model(**chat_input)
+        refined_response = chat_output.sequences[0]
+
+    # Output pipeline: Return final response
+    return refined_response
+
+class AIAgent:
+    def __init__(self, name, description, skills):
+        self.name = name
+        self.description = description
+        self.skills = skills
+
+    def create_agent_prompt(self):
+        skills_str = '\n'.join([f"* {skill}" for skill in self.skills])
+        agent_prompt = f"""
+As an elite expert developer, my name is {self.name}. I possess a comprehensive understanding of the following areas:
+{skills_str}
+I am confident that I can leverage my expertise to assist you in developing and deploying cutting-edge web applications. Please feel free to ask any questions or present any challenges you may encounter.
+"""
+        return agent_prompt
+
+    def autonomous_build(self, chat_history, workspace_projects, project_name, selected_model):
+        """
+        Autonomous build logic that continues based on the state of chat history and workspace projects.
+        """
+        summary = "Chat History:\n" + "\n".join([f"User: {u}\nAgent: {a}" for u, a in chat_history])
+        summary += "\n\nWorkspace Projects:\n" + "\n".join([f"{p}: {details}" for p, details in workspace_projects.items()])
+
+        # Analyze chat history and workspace projects to suggest actions
+        # Example:
+        # - Check if the user has requested to create a new file
+        # - Check if the user has requested to install a package
+        # - Check if the user has requested to run a command
+        # - Check if the user has requested to generate code
+        # - Check if the user has requested to translate code
+        # - Check if the user has requested to summarize text
+        # - Check if the user has requested to analyze sentiment
+
+        # Generate a response based on the analysis
+        next_step = "Based on the current state, the next logical step is to implement the main application logic."
+
+        # Ensure project folder exists
+        project_path = os.path.join(PROJECT_ROOT, project_name)
+        if not os.path.exists(project_path):
+            os.makedirs(project_path)
+
+        # Create requirements.txt if it doesn't exist
+        requirements_file = os.path.join(project_path, "requirements.txt")
+        if not os.path.exists(requirements_file):
+            with open(requirements_file, "w") as f:
+                f.write("# Add your project's dependencies here\n")
+
+        # Create app.py if it doesn't exist
+        app_file = os.path.join(project_path, "app.py")
+        if not os.path.exists(app_file):
+            with open(app_file, "w") as f:
+                f.write("# Your project's main application logic goes here\n")
+
+        # Generate GUI code for app.py if requested
+        if "create a gui" in summary.lower():
+            gui_code = generate_code("Create a simple GUI for this application", selected_model)
+            with open(app_file, "a") as f:
+                f.write(gui_code)
+
+        # Run the default build process
+        build_command = "pip install -r requirements.txt && python app.py"
+        try:
+            result = subprocess.run(build_command, shell=True, capture_output=True, text=True, cwd=project_path)
+            st.write(f"Build Output:\n{result.stdout}")
+            if result.stderr:
+                st.error(f"Build Errors:\n{result.stderr}")
+        except Exception as e:
+            st.error(f"Build Error: {e}")
+
+        return summary, next_step
+
+def save_agent_to_file(agent):
+    """Saves the agent's prompt to a file."""
+    if not os.path.exists(AGENT_DIRECTORY):
+        os.makedirs(AGENT_DIRECTORY)
+    file_path = os.path.join(AGENT_DIRECTORY, f"{agent.name}.txt")
+    with open(file_path, "w") as file:
+        file.write(agent.create_agent_prompt())
+    st.session_state.available_agents.append(agent.name)
+
+def load_agent_prompt(agent_name):
+    """Loads an agent prompt from a file."""
+    file_path = os.path.join(AGENT_DIRECTORY, f"{agent_name}.txt")
+    if os.path.exists(file_path):
+        with open(file_path, "r") as file:
+            agent_prompt = file.read()
+        return agent_prompt
     else:
-        df['closed_at'] = pd.NaT
+        return None
+
+def create_agent_from_text(name, text):
+    skills = text.split('\n')
+    agent = AIAgent(name, "AI agent created from text input.", skills)
+    save_agent_to_file(agent)
+    return agent.create_agent_prompt()
+
+def chat_interface_with_agent(input_text, agent_name):
+    agent_prompt = load_agent_prompt(agent_name)
+    if agent_prompt is None:
+        return f"Agent {agent_name} not found."
+
+    model_name ="MaziyarPanahi/Codestral-22B-v0.1-GGUF"
+    try:
+        from transformers import AutoModel, AutoTokenizer  # Import AutoModel here
+        model = AutoModel.from_pretrained("MaziyarPanahi/Codestral-22B-v0.1-GGUF")
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
+    except EnvironmentError as e:
+        return f"Error loading model: {e}"
+
+    combined_input = f"{agent_prompt}\n\nUser: {input_text}\nAgent:"
     
-    df['resolution_time'] = (df['closed_at'] - df['created_at']).dt.days
-    df['resolution_time'] = df['resolution_time'].fillna(-1)
-    df['is_closed'] = (df['closed_at'].notna()).astype(int)
-    return df
-
-# Function for exploratory data analysis (EDA)
-def perform_eda(df):
-    # Descriptive statistics
-    print("Descriptive Statistics:")
-    print(df.describe())
-
-    # Visualizations
-    plt.figure(figsize=(10, 6))
-    sns.histplot(df['resolution_time'], kde=True)
-    plt.title('Distribution of Issue Resolution Time')
-    plt.xlabel('Resolution Time (Days)')
-    plt.ylabel('Frequency')
-    plt.show()
-
-    # Trend analysis
-    df['created_at_month'] = df['created_at'].dt.month
-    plt.figure(figsize=(10, 6))
-    sns.lineplot(x='created_at_month', y='resolution_time', data=df)
-    plt.title('Trend of Issue Resolution Time Over Months')
-    plt.xlabel('Month')
-    plt.ylabel('Resolution Time (Days)')
-    plt.show()
-
-    # Top Authors and Assignees
-    top_authors = df['author'].value_counts().nlargest(10)
-    top_assignees = df['assignee'].value_counts().nlargest(10)
-    print("\nTop 10 Authors:")
-    print(top_authors)
-    print("\nTop 10 Assignees:")
-    print(top_assignees)
-
-# Function for text analysis using NLP
-def analyze_text_content(df):
-    # Text preprocessing
-    stop_words = set(stopwords.words('english'))
-    lemmatizer = WordNetLemmatizer()
-    df['processed_body'] = df['body'].apply(lambda text: ' '.join([lemmatizer.lemmatize(word) for word in word_tokenize(text) if word.lower() not in stop_words]))
-
-    # Topic modeling
-    dictionary = Dictionary([word_tokenize(text) for text in df['processed_body']])
-    corpus = [dictionary.doc2bow(word_tokenize(text)) for text in df['processed_body']]
-    lda_model = LdaModel(corpus, num_topics=5, id2word=dictionary)
-    print("Top 5 Topics:")
-    for topic in lda_model.print_topics(num_words=5):
-        print(topic)
-
-    # Sentiment analysis
-    analyzer = SentimentIntensityAnalyzer()
-    df['sentiment'] = df['body'].apply(lambda text: analyzer.polarity_scores(text)['compound'])
-    print("Sentiment Analysis:")
-    print(df['sentiment'].describe())
-
-    # Word Cloud for Common Words
-    from wordcloud import WordCloud
-    all_words = ' '.join([text for text in df['processed_body']])
-    wordcloud = WordCloud(width=800, height=400, background_color='white').generate(all_words)
-    plt.figure(figsize=(10, 6), facecolor=None)
-    plt.imshow(wordcloud)
-    plt.axis("off")
-    plt.tight_layout(pad=0)
-    plt.show()
-
-# Function to create a network graph of issues, authors, and assignees
-def create_network_graph(df):
-    graph = nx.Graph()
-    for index, row in df.iterrows():
-        graph.add_node(row['title'], type='issue')
-        graph.add_node(row['author'], type='author')
-        if row['assignee']:
-            graph.add_node(row['assignee'], type='assignee')
-        graph.add_edge(row['title'], row['author'])
-        if row['assignee']:
-            graph.add_edge(row['title'], row['assignee'])
-
-    # Interactive Network Graph with Plotly
-    pos = nx.spring_layout(graph, k=0.5)
-    edge_x = []
-    edge_y = []
-    for edge in graph.edges():
-        x0, y0 = pos[edge[0]]
-        x1, y1 = pos[edge[1]]
-        edge_x.append([x0, x1, None])
-        edge_y.append([y0, y1, None])
-
-    edge_trace = go.Scatter(
-        x=edge_x,
-        y=edge_y,
-        line=dict(width=0.5, color='#888'),
-        hoverinfo='none',
-        mode='lines'
+    input_ids = tokenizer.encode(combined_input, return_tensors="pt")
+    max_input_length = 900
+    if input_ids.shape[1] > max_input_length:
+        input_ids = input_ids[:, :max_input_length]
+
+    outputs = model.generate(
+        input_ids, max_new_tokens=50, num_return_sequences=1, do_sample=True,
+        pad_token_id=tokenizer.eos_token_id  # Set pad_token_id to eos_token_id
     )
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+# Terminal interface
+def terminal_interface(command, project_name=None):
+    if project_name:
+        project_path = os.path.join(PROJECT_ROOT, project_name)
+        if not os.path.exists(project_path):
+            return f"Project {project_name} does not exist."
+        result = subprocess.run(command, shell=True, capture_output=True, text=True, cwd=project_path)
+    else:
+        result = subprocess.run(command, shell=True, capture_output=True, text=True)
+    return result.stdout
+
+# Code editor interface
+def code_editor_interface(code):
+    try:
+        formatted_code = black.format_str(code, mode=black.FileMode())
+    except black.NothingChanged:
+        formatted_code = code
+
+    result = StringIO()
+    sys.stdout = result
+    sys.stderr = result
+
+    (pylint_stdout, pylint_stderr) = lint.py_run(code, return_std=True)
+    sys.stdout = sys.__stdout__
+    sys.stderr = sys.__stderr__
+
+    lint_message = pylint_stdout.getvalue() + pylint_stderr.getvalue()
+
+    return formatted_code, lint_message
+
+# Text summarization tool
+def summarize_text(text):
+    summarizer = pipeline("summarization")
+    summary = summarizer(text, max_length=130, min_length=30, do_sample=False)
+    return summary[0]['summary_text']
+
+# Sentiment analysis tool
+def sentiment_analysis(text):
+    analyzer = pipeline("sentiment-analysis")
+    result = analyzer(text)
+    return result[0]['label']
+
+# Text translation tool (code translation)
+def translate_code(code, source_language, target_language):
+    # Use a Hugging Face translation model instead of OpenAI
+    translator = pipeline("translation", model="bartowski/Codestral-22B-v0.1-GGUF")  # Example: English to Spanish
+    translated_code = translator(code, target_lang=target_language)[0]['translation_text']
+    return translated_code
+
+def generate_code(code_idea, model_name):
+    """Generates code using the selected model."""
+    try:
+        generator = pipeline('text-generation', model=model_name)
+        generated_code = generator(code_idea, max_length=1000, num_return_sequences=1)[0]['generated_text']
+        return generated_code
+    except Exception as e:
+        return f"Error generating code: {e}"
+
+def chat_interface(input_text):
+    """Handles general chat interactions with the user."""
+    # Use a Hugging Face chatbot model or your own logic
+    chatbot = pipeline("text-generation", model="microsoft/DialoGPT-medium")
+    response = chatbot(input_text, max_length=50, num_return_sequences=1)[0]['generated_text']
+    return response
+
+# Workspace interface
+def workspace_interface(project_name):
+    project_path = os.path.join(PROJECT_ROOT, project_name)
+    if not os.path.exists(project_path):
+        os.makedirs(project_path)
+        st.session_state.workspace_projects[project_name] = {'files': []}
+        return f"Project '{project_name}' created successfully."
+    else:
+        return f"Project '{project_name}' already exists."
 
-    node_x = []
-    node_y = []
-    for node in graph.nodes():
-        x, y = pos[node]
-        node_x.append(x)
-        node_y.append(y)
-
-    node_trace = go.Scatter(
-        x=node_x,
-        y=node_y,
-        mode='markers',
-        marker=dict(
-            color=[],
-            size=10,
-            line=dict(width=2, color='black')
-        ),
-        text=[],
-        hoverinfo='text'
-    )
+# Add code to workspace
+def add_code_to_workspace(project_name, code, file_name):
+    project_path = os.path.join(PROJECT_ROOT, project_name)
+    if not os.path.exists(project_path):
+        return f"Project '{project_name}' does not exist."
+    
+    file_path = os.path.join(project_path, file_name)
+    with open(file_path, "w") as file:
+        file.write(code)
+    st.session_state.workspace_projects[project_name]['files'].append(file_name)
+    return f"Code added to '{file_name}' in project '{project_name}'."
+
+# Streamlit App
+st.title("AI Agent Creator")
+
+# Sidebar navigation
+st.sidebar.title("Navigation")
+app_mode = st.sidebar.selectbox("Choose the app mode", ["AI Agent Creator", "Tool Box", "Workspace Chat App"])
+
+# Get Hugging Face token from secrets.toml
+hf_token = st.secrets["huggingface"]["hf_token"]
+
+if app_mode == "AI Agent Creator":
+    # AI Agent Creator
+    st.header("Create an AI Agent from Text")
+
+    st.subheader("From Text")
+    agent_name = st.text_input("Enter agent name:")
+    text_input = st.text_area("Enter skills (one per line):")
+    if st.button("Create Agent"):
+        agent_prompt = create_agent_from_text(agent_name, text_input)
+        st.success(f"Agent '{agent_name}' created and saved successfully.")
+        st.session_state.available_agents.append(agent_name)
+
+elif app_mode == "Tool Box":
+    # Tool Box
+    st.header("AI-Powered Tools")
+
+    # Chat Interface
+    st.subheader("Chat with CodeCraft")
+    chat_input = st.text_area("Enter your message:")
+    if st.button("Send"):
+        chat_response = chat_interface(chat_input)
+        st.session_state.chat_history.append((chat_input, chat_response))
+        st.write(f"CodeCraft: {chat_response}")
+
+    # Terminal Interface
+    st.subheader("Terminal")
+    terminal_input = st.text_input("Enter a command:")
+    if st.button("Run"):
+        terminal_output = terminal_interface(terminal_input)
+        st.session_state.terminal_history.append((terminal_input, terminal_output))
+        st.code(terminal_output, language="bash")
+
+    # Code Editor Interface
+    st.subheader("Code Editor")
+    code_editor = st.text_area("Write your code:", height=300)
+    if st.button("Format & Lint"):
+        formatted_code, lint_message = code_editor_interface(code_editor)
+        st.code(formatted_code, language="python")
+        st.info(lint_message)
+
+    # Text Summarization Tool
+    st.subheader("Summarize Text")
+    text_to_summarize = st.text_area("Enter text to summarize:")
+    if st.button("Summarize"):
+        summary = summarize_text(text_to_summarize)
+        st.write(f"Summary: {summary}")
+
+    # Sentiment Analysis Tool
+    st.subheader("Sentiment Analysis")
+    sentiment_text = st.text_area("Enter text for sentiment analysis:")
+    if st.button("Analyze Sentiment"):
+        sentiment = sentiment_analysis(sentiment_text)
+        st.write(f"Sentiment: {sentiment}")
+
+    # Text Translation Tool (Code Translation)
+    st.subheader("Translate Code")
+    code_to_translate = st.text_area("Enter code to translate:")
+    source_language = st.text_input("Enter source language (e.g., 'Python'):")
+    target_language = st.text_input("Enter target language (e.g., 'JavaScript'):")
+    if st.button("Translate Code"):
+        translated_code = translate_code(code_to_translate, source_language, target_language)
+        st.code(translated_code, language=target_language.lower())
+
+    # Code Generation
+    st.subheader("Code Generation")
+    code_idea = st.text_input("Enter your code idea:")
+    if st.button("Generate Code"):
+        generated_code = generate_code(code_idea)
+        st.code(generated_code, language="python")
+
+elif app_mode == "Workspace Chat App":
+    # Workspace Chat App
+    st.header("Workspace Chat App")
+
+    # Project Workspace Creation
+    st.subheader("Create a New Project")
+    project_name = st.text_input("Enter project name:")
+    if st.button("Create Project"):
+        workspace_status = workspace_interface(project_name)
+        st.success(workspace_status)
+
+        # Automatically create requirements.txt and app.py
+        project_path = os.path.join(PROJECT_ROOT, project_name)
+        requirements_file = os.path.join(project_path, "requirements.txt")
+        if not os.path.exists(requirements_file):
+            with open(requirements_file, "w") as f:
+                f.write("# Add your project's dependencies here\n")
+
+        app_file = os.path.join(project_path, "app.py")
+        if not os.path.exists(app_file):
+            with open(app_file, "w") as f:
+                f.write("# Your project's main application logic goes here\n")
+
+    # Add Code to Workspace
+    st.subheader("Add Code to Workspace")
+    code_to_add = st.text_area("Enter code to add to workspace:")
+    file_name = st.text_input("Enter file name (e.g., 'app.py'):")
+    if st.button("Add Code"):
+        add_code_status = add_code_to_workspace(project_name, code_to_add, file_name)
+        st.session_state.terminal_history.append((f"Add Code: {code_to_add}", add_code_status))
+        st.success(add_code_status)
+
+    # Terminal Interface with Project Context
+    st.subheader("Terminal (Workspace Context)")
+    terminal_input = st.text_input("Enter a command within the workspace:")
+    if st.button("Run Command"):
+        terminal_output = terminal_interface(terminal_input, project_name)
+        st.session_state.terminal_history.append((terminal_input, terminal_output))
+        st.code(terminal_output, language="bash")
+
+    # Chat Interface for Guidance
+    st.subheader("Chat with CodeCraft for Guidance")
+    chat_input = st.text_area("Enter your message for guidance:")
+    if st.button("Get Guidance"):
+        chat_response = chat_interface(chat_input)
+        st.session_state.chat_history.append((chat_input, chat_response))
+        st.write(f"CodeCraft: {chat_response}")
+
+    # Display Chat History
+    st.subheader("Chat History")
+    for user_input, response in st.session_state.chat_history:
+        st.write(f"User: {user_input}")
+        st.write(f"CodeCraft: {response}")
+
+    # Display Terminal History
+    st.subheader("Terminal History")
+    for command, output in st.session_state.terminal_history:
+        st.write(f"Command: {command}")
+        st.code(output, language="bash")
+
+    # Display Projects and Files
+    st.subheader("Workspace Projects")
+    for project, details in st.session_state.workspace_projects.items():
+        st.write(f"Project: {project}")
+        for file in details['files']:
+            st.write(f"  - {file}")
+
+    # Chat with AI Agents
+    st.subheader("Chat with AI Agents")
+    selected_agent = st.selectbox("Select an AI agent", st.session_state.available_agents)
+    agent_chat_input = st.text_area("Enter your message for the agent:")
+    if st.button("Send to Agent"):
+        agent_chat_response = chat_interface_with_agent(agent_chat_input, selected_agent)
+        st.session_state.chat_history.append((agent_chat_input, agent_chat_response))
+        st.write(f"{selected_agent}: {agent_chat_response}")
+
+    # Code Generation
+    st.subheader("Code Generation")
+    code_idea = st.text_input("Enter your code idea:")
+
+    # Model Selection Menu
+    selected_model = st.selectbox("Select a code-generative model", AVAILABLE_CODE_GENERATIVE_MODELS)
+
+    if st.button("Generate Code"):
+        generated_code = generate_code(code_idea, selected_model)
+        st.code(generated_code, language="python")
+
+    # Automate Build Process
+    st.subheader("Automate Build Process")
+    if st.button("Automate"):
+        agent = AIAgent(selected_agent, "", [])  # Load the agent without skills for now
+        summary, next_step = agent.autonomous_build(st.session_state.chat_history, st.session_state.workspace_projects, project_name, selected_model)
+        st.write("Autonomous Build Summary:")
+        st.write(summary)
+        st.write("Next Step:")
+        st.write(next_step)
+
+        # Use the hf_token to interact with the Hugging Face API
+        api = HfApi(token=hf_token)
+        # Function to create a Space on Hugging Face
+def create_space(api, name, description, public, files, entrypoint="launch.py"):
+    url = f"{hf_hub_url()}spaces/{name}/prepare-repo"
+    headers = {"Authorization": f"Bearer {api.access_token}"}````
+
+i need to integrate logic from these below, into my existing app.py above: 
+````import os
+import subprocess
+import streamlit as st
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer, AutoModel, RagRetriever, AutoModelForSeq2SeqLM
+import black
+from pylint import lint
+from io import StringIO
+import sys
+import torch
+from huggingface_hub import hf_hub_url, cached_download, HfApi
+
+# Set your Hugging Face API key here
+hf_token = "YOUR_HUGGING_FACE_API_KEY"  # Replace with your actual token
+
+# Other code remains unchanged
+
+class AIAgent:
+    def __init__(self, name, description, skills, hf_api=None):
+        self.name = name
+        self.description = description
+        self.skills = skills
+        self._hf_api = hf_api
+
+    @property
+    def hf_api(self):
+        if not self._hf_api and self.has_valid_hf_token():
+            self._hf_api = HfApi(token=self._hf_token)
+        return self._hf_api
+
+    def has_valid_hf_token(self):
+        return bool(self._hf_token)
+
+    async def autonomous_build(self, chat_history, workspace_projects, project_name, selected_model, hf_token):
+        self._hf_token = hf_token
+        # Continuation of previous methods
+
+    def deploy_built_space_to_hf(self):
+        if not self._hf_api or not self._hf_token:
+            raise ValueError("Cannot deploy the Space since no valid Hugoging Face API connection was established.")
+
+        repository_name = f"my-awesome-space_{datetime.now().timestamp()}"
+        files = get_built_space_files()
+
+        commit_response = self.hf_api.commit_repo(
+            repo_id=repository_name,
+            branch="main",
+            commits=[{"message": "Built Space Commit", "tree": tree_payload}]
+        )
+
+        print("Commit successful:", commit_response)
+        self.publish_space(repository_name)
+
+    def publish_space(self, repository_name):
+        publishing_response = self.hf_api.create_model_version(
+            model_name=repository_name,
+            repo_id=repository_name,
+            model_card={},
+            library_card={}
+        )
+
+        print("Space published:", publishing_response)
+
+def create_space(api, name, description, public, files, entrypoint="launch.py"):
+    url = f"{hf_hub_url()}spaces/{name}/prepare-repo"
+    headers = {"Authorization": f"Bearer {api.access_token}"}
+
+    payload = {
+        "public": public,
+        "gitignore_template": "web",
+        "default_branch": "main",
+        "archived": False,
+        "files": []
+    }
 
-    # Set node colors based on type
-    node_colors = []
-    for node in graph.nodes():
-        if graph.nodes[node]['type'] == 'issue':
-            node_colors.append('red')
-        elif graph.nodes[node]['type'] == 'author':
-            node_colors.append('blue')
-        else:
-            node_colors.append('green')
-
-    # Set node labels
-    node_labels = []
-    for node in graph.nodes():
-        node_labels.append(node)
-
-    node_trace.marker.color = node_colors
-    node_trace.text = node_labels
-
-    # Create the figure
-    fig = go.Figure(data=[edge_trace, node_trace],
-                   layout=go.Layout(
-                       title="GitHub Issue Network Graph",
-                       showlegend=False,
-                       hovermode='closest',
-                       margin=dict(b=20, l=5, r=5, t=40),
-                       xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
-                       yaxis=dict(showgrid=False, zeroline=False, showticklabels=False)
-                   )
-                  )
-
-    fig.show()
-
-# Function to build a predictive model for issue resolution time
-def build_predictive_model(df):
-    # Feature engineering
-    df['created_at_day'] = df['created_at'].dt.day
-    df['created_at_weekday'] = df['created_at'].dt.weekday
-    df['created_at_hour'] = df['created_at'].dt.hour
-    df['author_encoded'] = df['author'].astype('category').cat.codes
-    df['assignee_encoded'] = df['assignee'].astype('category').cat.codes
-
-    # Select features and target variable
-    features = ['created_at_day', 'created_at_weekday', 'created_at_hour', 'author_encoded', 'assignee_encoded', 'sentiment']
-    target = 'resolution_time'
-
-    # Split data into training and testing sets
-    X_train, X_test, y_train, y_test = train_test_split(df[features], df[target], test_size=0.2, random_state=42)
-
-    # Create a pipeline for feature scaling and model training
-    pipeline = Pipeline([
-        ('scaler', StandardScaler()),
-        ('model', RandomForestClassifier(random_state=42))
-    ])
-
-    # Train the model
-    pipeline.fit(X_train, y_train)
-
-    # Evaluate the model
-    y_pred = pipeline.predict(X_test)
-    accuracy = accuracy_score(y_test, y_pred)
-    print("Accuracy:", accuracy)
-    print(classification_report(y_test, y_pred))
-
-    # Make predictions on new data
-    # ...
-
-# Main function
-if __name__ == "__main__":
-    # Replace with your GitHub username and repository name
-    username = "miagiii"
-    repository = "miagiii"
-
-    # Fetch issue data from GitHub
-    issues_data = fetch_issue_data(username, repository, 1, 10)  # Fetch issues from pages 1 to 10
-
-    # Clean and structure the data
-    df = clean_and_structure_data(issues_data)
-
-    # Perform exploratory data analysis (EDA)
-    perform_eda(df)
-
-    # Analyze text content using NLP
-    analyze_text_content(df)
-
-    # Create a network graph of issues, authors, and assignees
-    create_network_graph(df)
-
-    # Build a predictive model for issue resolution time
-    build_predictive_model(df)
+    for filename, contents in files.items():
+        data = {
+            "content": contents,
+            "path": filename,
+            "encoding": "utf-8",
+            "mode": "overwrite" if "#{random.randint(0, 1)}" not in contents else "merge",
+        }
+        payload["files"].append(data)
+
+    response = requests.post(url, json=payload, headers=headers)
+    response.raise_for_status()
+    location = response.headers.get("Location")
+    wait_for_processing(location, api)
+
+    return Repository(name=name, api=api)