Update app.py

app.py

@@ -1,16 +1,25 @@
-import streamlit as st
 import os
 import subprocess
-from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
+import streamlit as st
+from transformers.pipelines import pipeline
+from transformers import AutoModelForCausalLM, AutoTokenizer, AutoModel, RagRetriever, AutoModelForSeq2SeqLM
 import black
 from pylint import lint
 from io import StringIO
-import openai
 import sys
-
-# Set your OpenAI API key here
-openai.api_key = "YOUR_OPENAI_API_KEY"
-
+import torch
+from huggingface_hub import hf_hub_url, cached_download, HfApi
+from datetime import datetime
+import requests
+import random
+from huggingface_hub.hf_api import Repository  # Assuming this is how you import the Repository class
+
+# Set your Hugging Face API key here
+# hf_token = "YOUR_HUGGING_FACE_API_KEY"  # Replace with your actual token
+# Get Hugging Face token from secrets.toml - this line should already be in the main code
+hf_token = st.secrets["huggingface"]["hf_token"]
+
+HUGGING_FACE_REPO_URL = "https://huggingface.co/spaces/acecalisto3/DevToolKit"
 PROJECT_ROOT = "projects"
 AGENT_DIRECTORY = "agents"
 
@@ -23,165 +32,175 @@ 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': {}
+    }
+
+# 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):
+    def __init__(self, name, description, skills, hf_api=None):
         self.name = name
         self.description = description
         self.skills = skills
+        self._hf_api = hf_api
+        self._hf_token = hf_token  # Store the token here
 
-    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):
-        """
-        Autonomous build logic that continues based on the state of chat history and workspace projects.
-        """
-        # Example logic: Generate a summary of chat history and workspace state
+    @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
         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()])
 
-        # Example: Generate the next logical step in the project
+        # 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."
 
-        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:
-        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()
-
-# Chat interface using a selected agent
-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."
-
-    # Load the GPT-2 model which is compatible with AutoModelForCausalLM
-    model_name = "gpt2"
-    try:
-        model = AutoModelForCausalLM.from_pretrained(model_name)
-        tokenizer = AutoTokenizer.from_pretrained(model_name)
-        generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
-    except EnvironmentError as e:
-        return f"Error loading model: {e}"
-
-    # Combine the agent prompt with user input
-    combined_input = f"{agent_prompt}\n\nUser: {input_text}\nAgent:"
-    
-    # Truncate input text to avoid exceeding the model's maximum length
-    max_input_length = 900
-    input_ids = tokenizer.encode(combined_input, return_tensors="pt")
-    if input_ids.shape[1] > max_input_length:
-        input_ids = input_ids[:, :max_input_length]
-
-    # Generate chatbot response
-    outputs = model.generate(
-        input_ids, max_new_tokens=50, num_return_sequences=1, do_sample=True
-    )
-    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return response
-
-# Terminal interface
-def terminal_interface(command, project_name=None):
-    if project_name:
+        # Ensure project folder exists
         project_path = os.path.join(PROJECT_ROOT, project_name)
-        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):
-    formatted_code = black.format_str(code, mode=black.FileMode())
-    pylint_output = lint.Run([formatted_code], do_exit=False)
-    pylint_output_str = StringIO()
-    pylint_output.linter.reporter.write_messages(pylint_output_str)
-    return formatted_code, pylint_output_str.getvalue()
-
-# 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):
-    # Placeholder for translation logic
-    return f"Translated {source_language} code to {target_language}."
-
-# Code generation tool
-def generate_code(idea):
-    response = openai.Completion.create(
-        engine="davinci-codex",
-        prompt=idea,
-        max_tokens=150
-    )
-    return response.choices[0].text.strip()
-
-# 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."
-
-# 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."
+        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
     
-    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}'."
-
-# Chat interface
-def chat_interface(input_text):
-    response = openai.Completion.create(
-        engine="davinci-codex",
-        prompt=input_text,
-        max_tokens=150
-    )
-    return response.choices[0].text.strip()
+    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.")
+
+        # Assuming you have a function to get the files for your Space
+        repository_name = f"my-awesome-space_{datetime.now().timestamp()}" 
+        files = get_built_space_files() # Placeholder - you'll need to define this function
+
+        # Create the Space
+        create_space(self.hf_api, repository_name, "Description", True, files) 
+
+        st.markdown("## Congratulations! Successfully deployed Space 🚀 ##")
+        st.markdown(f"[Check out your new Space here](https://huggingface.co/spaces/{repository_name})")
+
+
+# Add any missing functions from your original code (e.g., get_built_space_files)
+def get_built_space_files():
+    # Replace with your logic to gather the files you want to deploy
+    return {
+        "app.py": "# Your Streamlit app code here",
+        "requirements.txt": "streamlit\ntransformers" 
+        # Add other files as needed
+    }
+
+# ... (Rest of your existing functions: save_agent_to_file, load_agent_prompt, 
+# create_agent_from_text, chat_interface_with_agent, terminal_interface, 
+# code_editor_interface, summarize_text, sentiment_analysis, translate_code, 
+# generate_code, chat_interface, workspace_interface, add_code_to_workspace) 
+
+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": []
+    }
+    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)  # You might need to implement this if it's not already defined
+
+    return Repository(name=name, api=api)
 
 # Streamlit App
 st.title("AI Agent Creator")
@@ -190,6 +209,8 @@ st.title("AI Agent Creator")
 st.sidebar.title("Navigation")
 app_mode = st.sidebar.selectbox("Choose the app mode", ["AI Agent Creator", "Tool Box", "Workspace Chat App"])
 
+# ... (Rest of your Streamlit app logic, including the 'Automate' button callback)
+
 if app_mode == "AI Agent Creator":
     # AI Agent Creator
     st.header("Create an AI Agent from Text")
@@ -202,132 +223,17 @@ if app_mode == "AI Agent Creator":
         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)
-
-    # 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.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.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}")
-
-    # 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)
-        st.write("Autonomous Build Summary:")
-        st.write(summary)
-        st.write("Next Step:")
-        st.write(next_step)
+# ... (Rest of your Streamlit app logic for other app modes)
+
+# Using the modified and extended class and functions, update the callback for the 'Automate' button in the Streamlit UI:
+if st.button("Automate", args=(hf_token,)):
+    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, hf_token)
+    st.write("Autonomous Build Summary:")
+    st.write(summary)
+    st.write("Next Step:")
+    st.write(next_step)
+
+    # If everything went well, proceed to deploy the Space
+    if agent._hf_api and agent.has_valid_hf_token():
+        agent.deploy_built_space_to_hf()