Update app.py

app.py

@@ -1,142 +1,248 @@
-import os
import subprocess
import streamlit as st
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
import black
from pylint import lint
from io import StringIO
-import subprocess= "https://huggingface.co/spaces/acecalisto3/DevToolKit"
PROJECT_ROOT = "projects"
AGENT_DIRECTORY = "agents"
-import random
-import json'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': {}
}
-from flaskAIAgent:
def init(self, import Flask, render_templateskills):
self.name = name
self.description = description
self.skills = skills
-from datetime import datetime
-from gradio import Blocks
-from safe_search import safe_search
-from i_search import google, i_search as i_s
-from agent import (
-    ACTION_PROMPT, my name is {self.name}. I possess a comprehensive understanding of the following areas:
{skills_str}
-    ADD_PROMPT,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
-    COMPRESS_HISTORY_PROMPT,
-    LOG_PROMPT,
-    LOG_RESPONSE,
-    MODIFY_PROMPT,
-    PREFIX,
-    SEARCH_QUERY,
-    READ_PROMPT,
-    TASK_PROMPT,
-    UNDERSTAND_TEST_RESULTS_PROMPT,
+import os
import subprocess
import streamlit as st
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
import black
from pylint import lint
from io import StringIO
+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': {}
}
+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):
+    """
+    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()])
+
+    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 locally and then commits to the Hugging Face repository."""
if not os.path.exists(AGENT_DIRECTORY):
os.makedirs(AGENT_DIRECTORY)
file_path = os.path.join(AGENT_DIRECTORY, f"{agent.name}.txt")
config_path = os.path.join(AGENT_DIRECTORY, f"{agent.name}Config.txt")
with open(file_path, "w") as file:
file.write(agent.create_agent_prompt())
with open(config_path, "w") as file:
file.write(f"Agent Name: {agent.name}\nDescription: {agent.description}")
st.session_state.available_agents.append(agent.name)
+
+commit_and_push_changes(f"Add agent {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, 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
+def workspace_interface(project_name):
project_path = os.path.join(PROJECT_ROOT, project_name)
if not os.path.exists(PROJECT_ROOT):
os.makedirs(PROJECT_ROOT)
if not os.path.exists(project_path):
os.makedirs(project_path)
st.session_state.workspace_projects[project_name] = {"files": []}
st.session_state.current_state['workspace_chat']['project_name'] = project_name
commit_and_push_changes(f"Create project {project_name}")
return f"Project {project_name} created successfully."
else:
return f"Project {project_name} already exists."
+def add_code_to_workspace(project_name, code, file_name):
project_path = os.path.join(PROJECT_ROOT, project_name)
if os.path.exists(project_path):
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)
st.session_state.current_state['workspace_chat']['added_code'] = {"file_name": file_name, "code": code}
commit_and_push_changes(f"Add code to {file_name} in project {project_name}")
return f"Code added to {file_name} in project {project_name} successfully."
else:
return f"Project {project_name} does not exist."
+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, cwd=project_path, shell=True, capture_output=True, text=True)
else:
result = subprocess.run(command, shell=True, capture_output=True, text=True)
if result.returncode == 0:
st.session_state.current_state['toolbox']['terminal_output'] = result.stdout
return result.stdout
else:
st.session_state.current_state['toolbox']['terminal_output'] = result.stderr
return result.stderr
+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()
st.session_state.current_state['toolbox']['formatted_code'] = formatted_code
st.session_state.current_state['toolbox']['lint_message'] = lint_message
return formatted_code, lint_message
+def summarize_text(text):
summarizer = pipeline("summarization")
summary = summarizer(text, max_length=50, min_length=25, do_sample=False)
st.session_state.current_state['toolbox']['summary'] = summary[0]['summary_text']
return summary[0]['summary_text']
+def sentiment_analysis(text):
analyzer = pipeline("sentiment-analysis")
sentiment = analyzer(text)
st.session_state.current_state['toolbox']['sentiment'] = sentiment[0]
return sentiment[0]
+def translate_code(code, input_language, output_language):
# Define a dictionary to map programming languages to their corresponding file extensions
language_extensions = {
# ignore the specific languages right now, and continue to EOF
}
+
+# Add code to handle edge cases such as invalid input and unsupported programming languages
+if input_language not in language_extensions:
+    raise ValueError(f"Invalid input language: {input_language}")
+if output_language not in language_extensions:
+    raise ValueError(f"Invalid output language: {output_language}")
+
+# Use the dictionary to map the input and output languages to their corresponding file extensions
+input_extension = language_extensions[input_language]
+output_extension = language_extensions[output_language]
+
+# Translate the code using the OpenAI API
+prompt = f"Translate this code from {input_language} to {output_language}:\n\n{code}"
+response = openai.ChatCompletion.create(
+    model="gpt-4",
+    messages=[
+        {"role": "system", "content": "You are an expert software developer."},
+        {"role": "user", "content": prompt}
+    ]
 )
-from utils import parse_action, parse_file_content, read_python_module_structure
-from huggingface_hub import cached_download, hf_hub_url
-from transformers import InferenceClient
-
-app = Flask(__name__)
-
-@appdef generate_code(code_idea):
response = openai.route(
model="/",
messages=[
{"role": "system", "content": "You are an expert software developer."},
{"role": "user", "content": f"Generate a Python code snippet for the following idea:\n\n{code_idea}"}
]
)
generated_code = response.choices[0].message['content'].strip()
st.session_state.current_state['toolbox']['generated_code'] = generated_code
return generated_code
-def index():
"""Commits and pushes changes to the Hugging Face repository."""
commands = [
"git add .",
f"git commit -m '{commit_message}'",
"git push"
]
for command in commands:
result = subprocess.run(command, shell=True, capture_output=True, text=True)
if result.returncode != 0:
st.error(f"Error executing command '{command}': {result.stderr}")
break
-    return render_template("index.html")
-
-class App(Blocks):
-    defst.sidebar.title("Navigation")
app_mode __init__(self):
-        super()if app_mode == "AI Agent Creator":
# AI Agent Creator
st.__init__()
-        self.app_state = {"components": []}
-        self.terminal_history = ""
-        self.components_registry = {
-            "Button": {
-                "properties": {
-                    "label": "Click Me",
-                    "onclick": ""
-                },
-                "description":
# Tool Box
st.header("A clickable button",
-                "code_snippet": "gr.Button(value='{{label}}', variant='primary')"
-            },
-            "Text Input": {
-                "properties": {
-                    "value": "",
-                    "placeholder": "Enter text"
-                },
-                "description": "A field for entering text",
-                "code_snippet": "gr.Textbox(label='{{placeholder}}')"
-            },
-            "Image": {
-                "properties": {
-                    "src": "#",
-                    "alt": "Image"
-                },
-                "description": "Displays an image",
-                "code_snippet": "gr.Image(label='{{alt}}')"
-            },
-            "Dropdown": {
-                "properties": {
-                    "choices": ["Option 1", "Option 2"],
-                    "value": ""
-                },
-                "description": "A dropdown menu for selecting options",
-                "code_snippet": "gr.Dropdown(choices={{choices}}, label='Dropdown')"
-            }
-        }
-        self.nlp_model_names = [
-            "google/flan-t5-small",
-            "Qwen/CodeQwen1.5-7B-Chat-GGUF",
-            "bartowski/Codestral-22B-v0.1-GGUF",
-            "bartowski/AutoCoder-GGUF"
-        ]
-        self.nlp_models = []
-        self.initialize_nlp_models()
-
-    def initialize_nlp_models(self):
-        for nlp_model_name in self.nlp_model_names:
-            try:
-                cached_download(hf_hub_url(nlp_model_name, revision="main"))
-                self.nlp_models.append(InferenceClient(nlp_model_name))
-            except:
-                self.nlp_models.append(None)
-
-    def get_nlp_response(self, input_text, model_index):
-        if self.nlp_models[model_index]:
-            response = self.nlp_models[model_index].text_generation(input_text)
-            return response.generated_text
-        else:
-            return "NLP model not available."
-
-    class Component:
-        def __init__(self, type, properties=None, id=None):
-            self.id = id or random.randint(1000, 9999)
-            self.type = type
-            self.properties = properties or self.components_registry[type]["properties"].copy()
-
-        def to_dict(self):
-            return {
-                "id": self.id,
-                "type": self.type,
-                "properties": self.properties,
-            }
-
-        def render(self):
-            if self.type == "Dropdown":
-                self.properties["choices"] = str(self.properties["choices"]).replace("[", "").replace("]", "").replace("'", "")
-            return self.components_registry[self.type]["code_snippet"].format(**self.properties)
-
-    def update_app_canvas(self):
-        components_html = "".join([f"<div>Component ID: {component['id']}, Type: {component['type']}, Properties: {component['properties']}</div>" for component in self.app_state["components"]])
-        return components_html
-
-    def add_componentapp_mode == "Workspace Chat App":
# Workspace Chat App
st.header(self, component_type):
-        if component_type in self.components_registry:
-            new_component = self.Component(component_type)
-            self.app_state["components"].append(new_component.to_dict())
-            return (
-                self.update_app_canvas(),
-                f"System: Added component: {component_type}\n",
-            )
-        else:
-            return None, f"Error: Invalid component type: {component_type}\n"
-
-    def run_terminal_command(self, command, history):
-        output = ""
-        try:
-            process = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
-            output, error = process.communicate()
-            if error:
-                output = error
-        except Exception as e:
-            output = str(e)
-        self.terminal_history += f"{command}\n{output.decode('utf-8')}\n"
-        return self.terminal_history
-
-if __name__ == "__main__":
-    app.run(debug=True)
+translated_code = response.choices[0].message['content'].strip()
+
+# Return the translated code
+translated_code = response.choices[0].message['content'].strip()
+st.session_state.current_state['toolbox']['translated_code'] = translated_code
+return translated_code
+def generate_code(code_idea):
response = openai.ChatCompletion.create(
model="gpt-4",
messages=[
{"role": "system", "content": "You are an expert software developer."},
{"role": "user", "content": f"Generate a Python code snippet for the following idea:\n\n{code_idea}"}
]
)
generated_code = response.choices[0].message['content'].strip()
st.session_state.current_state['toolbox']['generated_code'] = generated_code
return generated_code
+def commit_and_push_changes(commit_message):
"""Commits and pushes changes to the Hugging Face repository."""
commands = [
"git add .",
f"git commit -m '{commit_message}'",
"git push"
]
for command in commands:
result = subprocess.run(command, shell=True, capture_output=True, text=True)
if result.returncode != 0:
st.error(f"Error executing command '{command}': {result.stderr}")
break
+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":
# 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"):
+    if chat_input.startswith("@"):
+        agent_name = chat_input.split(" ")[0][1:]  # Extract agent_name from @agent_name
+        chat_input = " ".join(chat_input.split(" ")[1:])  # Remove agent_name from input
+        chat_response = chat_interface_with_agent(chat_input, agent_name)
+    else:
+        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")
+
+# Display Preset Commands
+st.subheader("Preset Commands")
+preset_commands = {
+    "Create a new project": "create_project('project_name')",
+    "Add code to workspace": "add_code_to_workspace('project_name', 'code', 'file_name')",
+    "Run terminal command": "terminal_interface('command', 'project_name')",
+    "Generate code": "generate_code('code_idea')",
+    "Summarize text": "summarize_text('text')",
+    "Analyze sentiment": "sentiment_analysis('text')",
+    "Translate code": "translate_code('code', 'source_language', 'target_language')",
+}
+for command_name, command in preset_commands.items():
+    st.write(f"{command_name}: `{command}`")
+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)
+Display current state for debugging
+st.sidebar.subheader("Current State")
st.sidebar.json(st.session_state.current_state)