Update app.py

app.py

@@ -51,6 +51,59 @@ 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."""
@@ -282,6 +335,133 @@ 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}")