Update app.py

app.py

@@ -1,302 +1,348 @@
+import os
 import sys
 import subprocess
-from typing import List, Dict, Optional, Union, Tuple
-from functools import lru_cache
-from dataclasses import dataclass, field
-from enum import Enum, auto
-import webbrowser
-import tempfile
-import os
-import importlib
-
-class AppType(Enum):
-    WEB_APP = auto()
-    GRADIO_APP = auto()
-    STREAMLIT_APP = auto()
-    REACT_APP = auto()
-
-@dataclass(frozen=True)
-class Code:
-    content: str
-    language: str
-
-@dataclass(frozen=True)
-class Prompt:
-    content: str
-
-@dataclass(frozen=True)
-class Space:
-    content: str
-
-@dataclass(frozen=True)
-class Tutorial:
-    content: str
-
-@dataclass(frozen=True)
-class File:
-    name: str
-    content: str
-    language: str
-
-@dataclass(frozen=True)
-class AppInfo:
-    name: str
-    description: str
-    features: Tuple[str, ...]
-    dependencies: Tuple[str, ...]
-    space: Optional[Space] = None
-    tutorial: Optional[Tutorial] = None
-
-@dataclass(frozen=True)
-class App:
-    code: Code
-
-    def run(self):
-        raise NotImplementedError("Subclasses must implement run method")
-
-@dataclass(frozen=True)
-class WebApp(App):
-    def run(self):
-        with tempfile.NamedTemporaryFile(mode='w', delete=False, suffix='.html') as f:
-            f.write(self.code.content)
-        webbrowser.open('file://' + f.name)
-        print(f"Opened WebApp in default browser. Temporary file: {f.name}")
-
-@dataclass(frozen=True)
-class GradioApp(App):
-    def run(self):
-        with tempfile.NamedTemporaryFile(mode='w', delete=False, suffix='.py') as f:
-            f.write(self.code.content)
-        try:
-            subprocess.run([sys.executable, f.name], check=True)
-        except subprocess.CalledProcessError:
-            print("Error running Gradio app. Make sure Gradio is installed: pip install gradio")
-        finally:
-            os.unlink(f.name)
-
-@dataclass(frozen=True)
-class StreamlitApp(App):
-    def run(self):
-        with tempfile.NamedTemporaryFile(mode='w', delete=False, suffix='.py') as f:
-            f.write(self.code.content)
-        try:
-            subprocess.run([sys.executable, "-m", "streamlit", "run", f.name], check=True)
-        except subprocess.CalledProcessError:
-            print("Error running Streamlit app. Make sure Streamlit is installed: pip install streamlit")
-        finally:
-            os.unlink(f.name)
-
-
-@dataclass(frozen=True)
-class ReactApp(App):
-    def run(self):
-        print("To run a React app, you need to set up a proper React environment.")
-        print("Here's how you might typically run a React app:")
-        print("1. Make sure you have Node.js and npm installed")
-        print("2. Create a new React app: npx create-react-app my-app")
-        print("3. Replace the contents of src/App.js with the generated code")
-        print("4. Run the app: npm start")
-        print("\nHere's the code for your App.js:")
-        print(self.code.content)
-
-class AppFactory:
-    @staticmethod
-    @lru_cache(maxsize=128)
-    def create_prompt(app_type: AppType, app_info: AppInfo) -> Prompt:
-        return Prompt(
-            content=f"""
-            Create a {app_type.name} web application with the following details:
-            Name: {app_info.name}
-            Description: {app_info.description}
-            Features: {', '.join(app_info.features)}
-            Dependencies: {', '.join(app_info.dependencies)}
-            Space: {app_info.space.content if app_info.space else 'N/A'}
-            Tutorial: {app_info.tutorial.content if app_info.tutorial else 'N/A'}
-            Please generate the code for this application.
-            """
-        )
-
-    @staticmethod
-    @lru_cache(maxsize=128)
-    def create_space(app_info: AppInfo) -> Space:
-        return Space(
-            content=f"""
-            {app_info.name}
-            {app_info.description}
-            Features: {', '.join(app_info.features)}
-            Dependencies: {', '.join(app_info.dependencies)}
-            """
-        )
-
-    @staticmethod
-    @lru_cache(maxsize=128)
-    def create_app_type_prompt(app_type: AppType, app_info: AppInfo) -> Prompt:
-        return Prompt(
-            content=f"""
-            Is the following web application a {app_type.name}?
-            {app_info.name}
-            {app_info.description}
-            Features: {', '.join(app_info.features)}
-            Dependencies: {', '.join(app_info.dependencies)}
-            Please answer with either "Yes" or "No".
-            """
-        )
-
-    @staticmethod
-    def get_app(app_type: AppType, app_info: AppInfo) -> App:
-        app_creators = {
-            AppType.WEB_APP: AppFactory._create_web_app,
-            AppType.GRADIO_APP: AppFactory._create_gradio_app,
-            AppType.STREAMLIT_APP: AppFactory._create_streamlit_app,
-            AppType.REACT_APP: AppFactory._create_react_app,
-        }
-        return app_creators[app_type](app_info)
-
-    @staticmethod
-    @lru_cache(maxsize=128)
-    def _create_web_app(app_info: AppInfo) -> WebApp:
-        code = Code(
-            content=f"""
-            <!DOCTYPE html>
-            <html lang="en">
-            <head>
-                <meta charset="UTF-8">
-                <meta name="viewport" content="width=device-width, initial-scale=1.0">
-                <title>{app_info.name}</title>
-            </head>
-            <body>
-                <h1>{app_info.name}</h1>
-                <p>{app_info.description}</p>
-            </body>
-            </html>
-            """,
-            language="html"
-        )
-        return WebApp(code=code)
-
-    @staticmethod
-    @lru_cache(maxsize=128)
-    def _create_gradio_app(app_info: AppInfo) -> GradioApp:
-        code = Code(
-            content="""
-import gradio as gr
-
-def greet(name):
-    return f"Hello, {name}!"
-
-demo = gr.Interface(greet, "text", "text")
-
-if __name__ == "__main__":
-    demo.launch()
-""",
-            language="python"
-        )
-        return GradioApp(code=code)
-
-    @staticmethod
-    @lru_cache(maxsize=128)
-    def _create_streamlit_app(app_info: AppInfo) -> StreamlitApp:
-        code = Code(
-            content=f"""
+import base64
+import json
+from io import StringIO
+from typing import Dict, List
+
 import streamlit as st
+import torch
+from transformers import pipeline, AutoModelForSeq2SeqLM, AutoTokenizer, HfApi
+from pylint import lint
+import black
+
+# Add your Hugging Face API token here
+hf_token = st.secrets["huggingface"]
+
+# Constants
+PROJECT_ROOT = "./projects"
+AGENT_DIRECTORY = "./agents"
+AVAILABLE_CODE_GENERATIVE_MODELS = ["codegen", "gpt-neo", "codeparrot"]
+
+# Global state management
+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 = []
+
+# Load pre-trained models
+@st.cache(allow_output_mutation=True)
+def load_models():
+    rag_retriever = pipeline("question-answering", model="facebook/rag-token-nq")
+    chat_model = AutoModelForSeq2SeqLM.from_pretrained("microsoft/DialoGPT-medium")
+    tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
+    return rag_retriever, chat_model, tokenizer
+
+rag_retriever, chat_model, tokenizer = load_models()
+
+def process_input(user_input: str) -> str:
+    # 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():
+        rag_output = rag_retriever(question=user_input, context=user_input)
+        rag_answer = rag_output['answer']
+
+    # Chat model: Refine response
+    chat_input = tokenizer(rag_answer, return_tensors="pt")
+    with torch.no_grad():
+        chat_output = chat_model.generate(**chat_input)
+        refined_response = tokenizer.decode(chat_output[0], skip_special_tokens=True)
+
+    return refined_response
+
+class AIAgent:
+    def __init__(self, name: str, description: str, skills: List[str], hf_api=None):
+        self.name = name
+        self.description = description
+        self.skills = skills
+        self._hf_api = hf_api
+        self._hf_token = hf_token
+
+    @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)
+
+    def create_agent_prompt(self):
+        return f"Name: {self.name}\nDescription: {self.description}\nSkills:\n" + "\n".join(self.skills)
+
+    async def autonomous_build(self, chat_history: List[str], workspace_projects: Dict[str, str], project_name: str, selected_model: str):
+        summary = "Chat History:\n" + "\n".join(chat_history)
+        summary += "\n\nWorkspace Projects:\n" + "\n".join(workspace_projects.items())
+
+        next_step = "Based on the current state, the next logical step is to implement the main application logic."
+
+        project_path = os.path.join(PROJECT_ROOT, project_name)
+        if not os.path.exists(project_path):
+            os.makedirs(project_path)
+
+        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")
+
+        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)
+
+        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 deploy_built_space_to_hf(self):
+        if not self.has_valid_hf_token():
+            st.error("Invalid Hugging Face token. Please check your configuration.")
+            return
 
-st.title('{app_info.name}')
-st.write('{app_info.description}')
-""",
-            language="python"
-        )
-        return StreamlitApp(code=code)
-
-    @staticmethod
-    @lru_cache(maxsize=128)
-    def _create_react_app(app_info: AppInfo) -> ReactApp:
-        code = Code(
-            content=f"""
-import React from 'react';
-
-function App() {{
-    return (
-        <div className="App">
-            <h1>{app_info.name}</h1>
-            <p>{app_info.description}</p>
-        </div>
-    );
-}}
-
-export default App;
-""",
-            language="javascript"
-        )
-        return ReactApp(code=code)
-
-    @staticmethod
-    @lru_cache(maxsize=128)
-    def parse_tutorial(app_info: AppInfo) -> Tutorial:
-        return Tutorial(
-            content=f"""
-            ## {app_info.name} Tutorial
-            **Introduction**
-            {app_info.description}
-            **Prerequisites**
-            * Basic knowledge of web development
-            * Familiarity with {', '.join(app_info.dependencies)}
-            **Steps**
-            {chr(10).join(f"{i+1}. {feature}" for i, feature in enumerate(app_info.features))}
-            **Conclusion**
-            Congratulations! You have successfully created a {app_info.name} application.
-            """
-        )
-
-    @staticmethod
-    def generate_files(app_type: AppType, app_info: AppInfo) -> List[File]:
-        app = AppFactory.get_app(app_type, app_info)
-        file_name = {
-            AppType.WEB_APP: "index.html",
-            AppType.GRADIO_APP: "app.py",
-            AppType.STREAMLIT_APP: "app.py",
-            AppType.REACT_APP: "App.js",
-        }[app_type]
-        return [File(name=file_name, content=app.code.content, language=app.code.language)]
-
-    @staticmethod
-    def run_app(app: App):
-        app.run()
-
-if __name__ == "__main__":
-    app_info = AppInfo(
-        name="My Cool App",
-        description="A simple web application",
-        features=("Feature 1", "Feature 2", "Feature 3"),
-        dependencies=("Python", "JavaScript"),
-    )
-    
-    # Check if required packages are installed
-    required_packages = ['gradio', 'streamlit']
-    missing_packages = []
-    for package in required_packages:
         try:
-            importlib.import_module(package)
-        except ImportError:
-            missing_packages.append(package)
-    
-    if missing_packages:
-        print(f"The following packages are missing: {', '.join(missing_packages)}")
-        print("Please install them using:")
-        print(f"pip install {' '.join(missing_packages)}")
-        sys.exit(1)
-
-    # Create and run a WebApp
-    web_app = AppFactory.get_app(AppType.WEB_APP, app_info)
-    AppFactory.run_app(web_app)
-
-    # Create and run a GradioApp
-    gradio_app = AppFactory.get_app(AppType.GRADIO_APP, app_info)
-    AppFactory.run_app(gradio_app)
-
-    # Create and run a StreamlitApp
-    streamlit_app = AppFactory.get_app(AppType.STREAMLIT_APP, app_info)
-    AppFactory.run_app(streamlit_app)
-
-    # Create and display info for a ReactApp
-    react_app = AppFactory.get_app(AppType.REACT_APP, app_info)
-    AppFactory.run_app(react_app)
+            files = get_built_space_files()
+            create_space_on_hugging_face(self.hf_api, self.name, self.description, True, files)
+            st.success(f"Successfully deployed {self.name} to Hugging Face Spaces!")
+        except Exception as e:
+            st.error(f"Error deploying to Hugging Face Spaces: {str(e)}")
+
+def get_built_space_files() -> Dict[str, str]:
+    return {
+        "app.py": "# Your Streamlit app code here",
+        "requirements.txt": "streamlit\ntransformers"
+    }
+
+def save_agent_to_file(agent: AIAgent):
+    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: str) -> str:
+    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: str, text: str) -> str:
+    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: str, agent_name: str) -> str:
+    agent_prompt = load_agent_prompt(agent_name)
+    if agent_prompt is None:
+        return f"Agent {agent_name} not found."
+
+    model_name = "microsoft/DialoGPT-medium"
+    try:
+        generator = pipeline("text-generation", model=model_name)
+        generated_response = generator(
+            f"{agent_prompt}\n\nUser: {input_text}\nAgent:", max_length=100, do_sample=True, top_k=50)[0]["generated_text"]
+        return generated_response
+    except Exception as e:
+        return f"Error loading model: {e}"
+
+def terminal_interface(command: str, project_name: str = None) -> str:
+    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
+
+def code_editor_interface(code: str) -> str:
+    try:
+        formatted_code = black.format_str(code, mode=black.FileMode())
+    except black.NothingChanged:
+        formatted_code = code
+
+    result = StringIO()
+    sys.stdout = result
+    sys.stderr = result
+
+    lint.Run(['--rcfile=/dev/null', '-'], exit=False)
+    lint_message = result.getvalue()
+
+    sys.stdout = sys.__stdout__
+    sys.stderr = sys.__stderr__
+
+    return formatted_code, lint_message
+
+def summarize_text(text: str) -> str:
+    summarizer = pipeline("summarization")
+    summary = summarizer(text, max_length=130, min_length=30, do_sample=False)
+    return summary[0]['summary_text']
+
+def sentiment_analysis(text: str) -> str:
+    analyzer = pipeline("sentiment-analysis")
+    result = analyzer(text)
+    return result[0]['label']
+
+def translate_code(code: str, source_language: str, target_language: str) -> str:
+    translator = pipeline("translation", model="Helsinki-NLP/opus-mt-en-ROMANCE")
+    translated_code = translator(code, max_length=512)[0]['translation_text']
+    return translated_code
+
+def generate_code(code_idea: str, model_name: str) -> str:
+    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: str) -> str:
+    chatbot = pipeline("text-generation", model="microsoft/DialoGPT-medium")
+    response = chatbot(input_text, max_length=50, num_return_sequences=1)[0]['generated_text']
+    return response
+
+def workspace_interface(project_name: str) -> str:
+    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."
+
+def add_code_to_workspace(project_name: str, code: str, file_name: str) -> str:
+    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}'."
+
+def create_space_on_hugging_face(api, name, description, public, files, entrypoint="app.py"):
+    try:
+        repo = api.create_repo(name, exist_ok=True, private=not public)
+        for filename, content in files.items():
+            api.upload_file(
+                path_or_fileobj=content.encode(),
+                path_in_repo=filename,
+                repo_id=repo.repo_id,
+                repo_type="space",
+            )
+        return repo
+    except Exception as e:
+        st.error(f"Error creating Hugging Face Space: {str(e)}")
+        return None
+
+# 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"])
+
+if app_mode == "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":
+    st.header("AI-Powered Tools")
+
+    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}")
+
+    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")
+
+    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)
+
+    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}")
+
+    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}")
+
+    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())
+
+    st.subheader("Code Generation")
+    code_idea = st.text_input("Enter your code idea:")
+    if st.button("Generate Code"):
+        generated_code = generate_code(code_idea, "gpt2")
+        st.code(generated_code, language="python")
+
+elif app_mode == "Workspace Chat App":
+    st.header("Workspace Chat App")
+
+    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)
+
+    st.subheader("Add Code to Workspace")
+    code_to_add = st.text_area("Enter code to add to workspace:")