Update app.py

app.py

@@ -51,59 +51,6 @@ classifier = pipeline("text-classification", model="facebook/bart-large-mnli")
 model = AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x7B-Instruct-v0.1", use_auth_token=os.environ.get("huggingface_token"))
 tokenizer = AutoTokenizer.from_pretrained("mistralai/Mixtral-8x7B-Instruct-v0.1", use_auth_token=os.environ.get("huggingface_token"))
 
-import os
-import subprocess
-import streamlit as st
-from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
-from langchain_community.llms import HuggingFaceHub
-from langchain_community.embeddings import HuggingFaceHubEmbeddings
-from langchain_community.document_loaders import PyPDFLoader
-from langchain_community.vectorstores import FAISS
-from langchain.chains import ConversationalRetrievalChain
-from langchain.chains.question_answering import load_qa_chain
-from llama_cpp import Llama, LlamaCppPythonProvider, LlamaCppAgent
-from llama_cpp.llama_cpp_agent import get_messages_formatter_type, get_context_by_model
-from io import StringIO
-import tempfile
-
-# --- Global Variables ---
-CURRENT_PROJECT = {}  # Store project data (code, packages, etc.)
-MODEL_OPTIONS = {
-    "CodeQwen": "Qwen/CodeQwen1.5-7B-Chat-GGUF",
-    "Codestral": "bartowski/Codestral-22B-v0.1-GGUF",
-    "AutoCoder": "bartowski/AutoCoder-GGUF",
-}
-MODEL_FILENAMES = {
-    "CodeQwen": "codeqwen-1_5-7b-chat-q6_k.gguf",
-    "Codestral": "Codestral-22B-v0.1-Q6_K.gguf",
-    "AutoCoder": "AutoCoder-Q6_K.gguf",
-}
-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': {}
-    }
-
-# --- Load NLP Pipelines ---
-classifier = pipeline("text-classification", model="facebook/bart-large-mnli")
-
-# --- Load the model and tokenizer ---
-model = AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x7B-Instruct-v0.1", use_auth_token=os.environ.get("huggingface_token"))
-tokenizer = AutoTokenizer.from_pretrained("mistralai/Mixtral-8x7B-Instruct-v0.1", use_auth_token=os.environ.get("huggingface_token"))
-
 # --- Utility Functions ---
 def install_and_import(package_name):
     """Installs a package using pip and imports it."""
@@ -335,133 +282,6 @@ inputs = tokenizer(prompt, return_tensors="pt")
             edited_code = tokenizer.decode(output[0], skip_special_tokens=True).split("
 
 
-python\n")[1].split("\n
-
-st.success(f"Code edited successfully!\n{edited_code}")
-            update_project_data("code", edited_code)
-            code_area.value = edited_code
-        except Exception as e:
-            st.error(f"Error editing code: {e}")
-
-# --- Prebuilt Tools ---
-st.markdown("## Prebuilt Tools:")
-with st.expander("Generate Code"):
-    code_input = st.text_area("Enter your code request:", key="code_input")
-    if st.button("Generate"):
-        code_output = generate_code_tool(code_input, chat_history)
-        st.markdown(code_output)
-
-with st.expander("Analyze Code"):
-    code_input = st.text_area("Enter your code:", key="analyze_code_input")
-    if st.button("Analyze"):
-        analysis_output = analyze_code_tool(code_input, chat_history)
-        st.markdown(analysis_output)
-
-# --- Additional Features ---
-# Add features like:
-# - Code editing
-# - Integration with external APIs
-# - Advanced AI agents for more complex tasks
-# - User account management
-
-    # --- AI Agent Interaction ---
-    if USER_INTENT is None:
-        add_message("System", analyze_user_intent(input_text))
-        add_message("System", "What kind of mini-app do you have in mind?")
-    elif not MINI_APPS:
-        add_message("System", "Here are some ideas:")
-        for idea in generate_mini_app_ideas(input_text):
-            add_message("System", f"- {idea}")
-        add_message("System", "Which one would you like to build?")
-    elif CURRENT_APP["name"] is None:
-        selected_app = input_text
-        app_description = next((app for app in MINI_APPS if selected_app in app), None)
-        if app_description:
-            add_message("System", f"Generating code for {app_description}...")
-            code = generate_app_code(selected_app, app_description, "CodeQwen", history)  # Use CodeQwen by default
-            add_message("System", f"
-
-
-python\n{code}\n
-
-add_message("System", "Code generated! What else can I do for you?")
-            update_project_data("code", code)
-            update_project_data("app_name", selected_app)
-            update_project_data("app_description", app_description)
-        else:
-            add_message("System", "Please choose from the provided mini-app ideas.")
-    else:
-        add_message("System", "You already have an app in progress. Do you want to start over?")
-
-    return history, dynamic_functions
-
-# --- Prebuilt Tools ---
-def generate_code_tool(input_text, history):
-    """Prebuilt tool for code generation."""
-    code = generate_app_code("MyTool", "A tool to do something", "CodeQwen", history)  # Use CodeQwen by default
-    return f"
-
-
-python\n{code}\n
-
-def analyze_code_tool(input_text, history):
-    """Prebuilt tool for code analysis."""
-    agent = get_agent("Codestral")
-    analysis = agent.chat(input_text, history)
-    return analysis
-
-# --- Streamlit Interface ---
-st.title("AI4ME: Your Personal AI App Workshop")
-st.markdown("## Let's build your dream app together! 🤖")
-
-# --- Hugging Face Token Input ---
-huggingface_token = st.text_input("Enter your Hugging Face Token", type="password", key="huggingface_token")
-os.environ["huggingface_token"] = huggingface_token
-
-# --- Chat Interface ---
-chat_history = []
-chat_input = st.text_input("Tell me your idea...", key="chat_input")
-if chat_input:
-    chat_history, dynamic_functions = handle_chat(chat_input, chat_history)
-    for sender, message in chat_history:
-        st.markdown(f"**{sender}:** {message}")
-
-# --- Code Execution and Deployment ---
-if CURRENT_APP["code"]:
-    st.markdown("## Your App Code:")
-    code_area = st.text_area("Your App Code", value=CURRENT_APP["code"], key="code_area")
-
-    st.markdown("## Deploy Your App (Coming Soon!)")
-    # Add deployment functionality here using Streamlit's deployment features.
-    # For example, you could use Streamlit's `st.button` to trigger deployment.
-
-    # --- Code Execution ---
-    st.markdown("## Run Your App:")
-    if st.button("Execute Code"):
-        try:
-            # Use Hugging Face's text-generation pipeline for code execution
-            inputs = tokenizer(code_area, return_tensors="pt")
-            output = model.generate(**inputs, max_length=500, num_return_sequences=1)
-            output = tokenizer.decode(output[0], skip_special_tokens=True)
-            st.success(f"Code executed successfully!\n{output}")
-        except Exception as e:
-            st.error(f"Error executing code: {e}")
-
-    # --- Code Editing ---
-    st.markdown("## Edit Your Code:")
-    if st.button("Edit Code"):
-        try:
-            # Use Hugging Face's text-generation pipeline for code editing
-            prompt = f"Improve the following Python code:\n
-
-
-python\n{code_area}\n
-
-inputs = tokenizer(prompt, return_tensors="pt")
-            output = model.generate(**inputs, max_length=500, num_return_sequences=1)
-            edited_code = tokenizer.decode(output[0], skip_special_tokens=True).split("
-
-
 python\n")[1].split("\n
 
 st.success(f"Code edited successfully!\n{edited_code}")