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Update app.py
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app.py
CHANGED
@@ -1,4 +1,3 @@
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import os
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import subprocess
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import streamlit as st
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from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
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@@ -43,8 +42,8 @@ I am confident that I can leverage my expertise to assist you in developing and
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def autonomous_build(self, chat_history, workspace_projects):
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"""
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Autonomous build logic
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"""
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summary = "Chat History:\n" + "\n".join([f"User: {u}\nAgent: {a}" for u, a in chat_history])
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summary += "\n\nWorkspace Projects:\n" + "\n".join(
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@@ -56,7 +55,7 @@ I am confident that I can leverage my expertise to assist you in developing and
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def save_agent_to_file(agent):
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"""Saves the agent's
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if not os.path.exists(AGENT_DIRECTORY):
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os.makedirs(AGENT_DIRECTORY)
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file_path = os.path.join(AGENT_DIRECTORY, f"{agent.name}.txt")
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@@ -67,7 +66,8 @@ def save_agent_to_file(agent):
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file.write(f"Agent Name: {agent.name}\nDescription: {agent.description}")
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st.session_state.available_agents.append(agent.name)
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-
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def load_agent_prompt(agent_name):
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@@ -82,6 +82,7 @@ def load_agent_prompt(agent_name):
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def create_agent_from_text(name, text):
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skills = text.split('\n')
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agent = AIAgent(name, "AI agent created from text input.", skills)
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save_agent_to_file(agent)
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@@ -94,7 +95,7 @@ def chat_interface_with_agent(input_text, agent_name):
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if agent_prompt is None:
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return f"Agent {agent_name} not found."
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# Load the GPT-2 model
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model_name = "gpt2"
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try:
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model = AutoModelForCausalLM.from_pretrained(model_name)
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@@ -103,19 +104,20 @@ def chat_interface_with_agent(input_text, agent_name):
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except EnvironmentError as e:
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return f"Error loading model: {e}"
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# Combine
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combined_input = f"{agent_prompt}\n\nUser: {input_text}\nAgent:"
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# Truncate input text to avoid exceeding the model's maximum length
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max_input_length = 900
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input_ids = tokenizer.encode(combined_input, return_tensors="pt")
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if input_ids.shape[1] > max_input_length:
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input_ids = input_ids[:, :max_input_length]
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# Generate
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outputs = model.generate(
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input_ids,
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)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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return response
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@@ -123,7 +125,7 @@ def chat_interface_with_agent(input_text, agent_name):
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# Basic chat interface (no agent)
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def chat_interface(input_text):
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# Load the GPT-2 model
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model_name = "gpt2"
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try:
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model = AutoModelForCausalLM.from_pretrained(model_name)
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@@ -132,13 +134,14 @@ def chat_interface(input_text):
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except EnvironmentError as e:
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return f"Error loading model: {e}"
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# Generate
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outputs = generator(input_text, max_new_tokens=50, num_return_sequences=1, do_sample=True)
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response = outputs[0]['generated_text']
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return response
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def workspace_interface(project_name):
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project_path = os.path.join(PROJECT_ROOT, project_name)
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if not os.path.exists(PROJECT_ROOT):
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os.makedirs(PROJECT_ROOT)
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@@ -146,13 +149,15 @@ def workspace_interface(project_name):
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os.makedirs(project_path)
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st.session_state.workspace_projects[project_name] = {"files": []}
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st.session_state.current_state['workspace_chat']['project_name'] = project_name
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return f"Project {project_name} created successfully."
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else:
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return f"Project {project_name} already exists."
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def add_code_to_workspace(project_name, code, file_name):
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project_path = os.path.join(PROJECT_ROOT, project_name)
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if os.path.exists(project_path):
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file_path = os.path.join(project_path, file_name)
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@@ -160,13 +165,14 @@ def add_code_to_workspace(project_name, code, file_name):
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file.write(code)
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st.session_state.workspace_projects[project_name]["files"].append(file_name)
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st.session_state.current_state['workspace_chat']['added_code'] = {"file_name": file_name, "code": code}
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return f"Code added to {file_name} in project {project_name} successfully."
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else:
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return f"Project {project_name} does not exist."
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def terminal_interface(command, project_name=None):
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if project_name:
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project_path = os.path.join(PROJECT_ROOT, project_name)
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if not os.path.exists(project_path):
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@@ -174,6 +180,7 @@ def terminal_interface(command, project_name=None):
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result = subprocess.run(command, cwd=project_path, shell=True, capture_output=True, text=True)
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else:
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result = subprocess.run(command, shell=True, capture_output=True, text=True)
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if result.returncode == 0:
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st.session_state.current_state['toolbox']['terminal_output'] = result.stdout
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return result.stdout
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@@ -183,6 +190,7 @@ def terminal_interface(command, project_name=None):
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def summarize_text(text):
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summarizer = pipeline("summarization")
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summary = summarizer(text, max_length=100, min_length=25, do_sample=False)
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st.session_state.current_state['toolbox']['summary'] = summary[0]['summary_text']
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def sentiment_analysis(text):
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analyzer = pipeline("sentiment-analysis")
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sentiment = analyzer(text)
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st.session_state.current_state['toolbox']['sentiment'] = sentiment[0]
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def code_editor_interface(code):
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"""Formats and lints Python code
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try:
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formatted_code = black.format_str(code, mode=black.FileMode())
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lint_result = StringIO()
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lint.Run([
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'--disable=C0114,C0115,C0116',
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'--output-format=text',
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'--reports=n',
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'-'
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], exit=False, do_exit=False)
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lint_message = lint_result.getvalue()
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return formatted_code, lint_message
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@@ -214,12 +223,7 @@ def code_editor_interface(code):
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def translate_code(code, input_language, output_language):
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"""
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Translates code using the Hugging Face translation pipeline.
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Note: This is a basic example and may not be suitable for all code translation tasks.
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Consider using more specialized tools for complex code translation.
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"""
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try:
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translator = pipeline("translation", model=f"{input_language}-to-{output_language}")
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translated_code = translator(code, max_length=10000)[0]['translation_text']
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def generate_code(code_idea):
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"""Generates code using
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try:
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generator = pipeline('text-generation', model='gpt2')
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generated_code = generator(f"```python\n{code_idea}\n```", max_length=1000, num_return_sequences=1)[0][
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'generated_text']
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# Extract code from the generated text
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start_index = generated_code.find("```python") + len("```python")
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end_index = generated_code.find("```", start_index)
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if start_index != -1 and end_index != -1:
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def commit_and_push_changes(commit_message):
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"""Commits and pushes changes
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commands = [
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"git add .",
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f"git commit -m '{commit_message}'",
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break
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# Streamlit App
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st.title("AI Agent Creator")
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# Sidebar navigation
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app_mode = st.sidebar.selectbox("Choose the app mode", ["AI Agent Creator", "Tool Box", "Workspace Chat App"])
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if app_mode == "AI Agent Creator":
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# AI Agent Creator
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st.header("Create an AI Agent from Text")
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st.subheader("From Text")
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agent_name = st.text_input("Enter agent name:")
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text_input = st.text_area("Enter skills (one per line):")
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if st.button("Create Agent"):
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st.session_state.available_agents.append(agent_name)
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elif app_mode == "Tool Box":
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# Tool Box
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st.header("AI-Powered Tools")
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# Chat Interface
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st.subheader("Chat with CodeCraft")
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chat_input = st.text_area("Enter your message:")
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if st.button("Send"):
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if chat_input.startswith("@"):
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agent_name = chat_input.split(" ")[0][1:]
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chat_input = " ".join(chat_input.split(" ")[1:])
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chat_response = chat_interface_with_agent(chat_input, agent_name)
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else:
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chat_response = chat_interface(chat_input)
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st.session_state.chat_history.append((chat_input, chat_response))
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st.write(f"CodeCraft: {chat_response}")
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# Terminal Interface
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st.subheader("Terminal")
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terminal_input = st.text_input("Enter a command:")
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if st.button("Run"):
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st.session_state.terminal_history.append((terminal_input, terminal_output))
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st.code(terminal_output, language="bash")
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# Code Editor Interface
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st.subheader("Code Editor")
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code_editor = st.text_area("Write your code:", height=300)
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if st.button("Format & Lint"):
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st.code(formatted_code, language="python")
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st.info(lint_message)
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# Text Summarization Tool
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st.subheader("Summarize Text")
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text_to_summarize = st.text_area("Enter text to summarize:")
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if st.button("Summarize"):
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summary = summarize_text(text_to_summarize)
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st.write(f"Summary: {summary}")
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# Sentiment Analysis Tool
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st.subheader("Sentiment Analysis")
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sentiment_text = st.text_area("Enter text for sentiment analysis:")
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if st.button("Analyze Sentiment"):
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sentiment = sentiment_analysis(sentiment_text)
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st.write(f"Sentiment: {sentiment}")
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# Text Translation Tool (Code Translation)
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st.subheader("Translate Code")
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code_to_translate = st.text_area("Enter code to translate:")
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source_language = st.selectbox("Source Language", ["en", "fr", "de", "es", "zh", "ja", "ko", "ru"])
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target_language = st.selectbox("Target Language", ["en", "fr", "de", "es", "zh", "ja", "ko", "ru"])
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if st.button("Translate Code"):
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translated_code = translate_code(code_to_translate, source_language, target_language)
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st.code(translated_code, language=target_language.lower())
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# Code Generation
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st.subheader("Code Generation")
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code_idea = st.text_input("Enter your code idea:")
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if st.button("Generate Code"):
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generated_code = generate_code(code_idea)
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st.code(generated_code, language="python")
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# Display Preset Commands
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st.subheader("Preset Commands")
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preset_commands = {
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"Create a new project": "create_project('project_name')",
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st.write(f"{command_name}: `{command}`")
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elif app_mode == "Workspace Chat App":
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# Workspace Chat App
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st.header("Workspace Chat App")
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# Project Workspace Creation
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st.subheader("Create a New Project")
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project_name = st.text_input("Enter project name:")
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if st.button("Create Project"):
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workspace_status = workspace_interface(project_name)
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st.success(workspace_status)
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# Add Code to Workspace
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st.subheader("Add Code to Workspace")
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code_to_add = st.text_area("Enter code to add to workspace:")
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file_name = st.text_input("Enter file name (e.g. 'app.py'):")
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add_code_status = add_code_to_workspace(project_name, code_to_add, file_name)
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st.success(add_code_status)
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# Terminal Interface with Project Context
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st.subheader("Terminal (Workspace Context)")
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terminal_input = st.text_input("Enter a command within the workspace:")
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if st.button("Run Command"):
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terminal_output = terminal_interface(terminal_input, project_name)
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st.code(terminal_output, language="bash")
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# Chat Interface for Guidance
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st.subheader("Chat with CodeCraft for Guidance")
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chat_input = st.text_area("Enter your message for guidance:")
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if st.button("Get Guidance"):
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st.session_state.chat_history.append((chat_input, chat_response))
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st.write(f"CodeCraft: {chat_response}")
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# Display Chat History
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st.subheader("Chat History")
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for user_input, response in st.session_state.chat_history:
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st.write(f"User: {user_input}")
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st.write(f"CodeCraft: {response}")
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# Display Terminal History
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st.subheader("Terminal History")
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for command, output in st.session_state.terminal_history:
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st.write(f"Command: {command}")
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st.code(output, language="bash")
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# Display Projects and Files
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st.subheader("Workspace Projects")
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for project, details in st.session_state.workspace_projects.items():
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st.write(f"Project: {project}")
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for file in details['files']:
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st.write(f" - {file}")
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# Chat with AI Agents
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st.subheader("Chat with AI Agents")
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selected_agent = st.selectbox("Select an AI agent", st.session_state.available_agents)
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agent_chat_input = st.text_area("Enter your message for the agent:")
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st.session_state.chat_history.append((agent_chat_input, agent_chat_response))
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st.write(f"{selected_agent}: {agent_chat_response}")
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# Automate Build Process
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st.subheader("Automate Build Process")
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if st.button("Automate"):
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-
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-
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import subprocess
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import streamlit as st
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from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
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def autonomous_build(self, chat_history, workspace_projects):
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"""
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Autonomous build logic.
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For now, it provides a simple summary and suggests the next step.
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"""
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summary = "Chat History:\n" + "\n".join([f"User: {u}\nAgent: {a}" for u, a in chat_history])
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summary += "\n\nWorkspace Projects:\n" + "\n".join(
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def save_agent_to_file(agent):
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"""Saves the agent's information to files."""
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if not os.path.exists(AGENT_DIRECTORY):
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os.makedirs(AGENT_DIRECTORY)
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file_path = os.path.join(AGENT_DIRECTORY, f"{agent.name}.txt")
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file.write(f"Agent Name: {agent.name}\nDescription: {agent.description}")
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st.session_state.available_agents.append(agent.name)
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# (Optional) Commit and push if you have set up Hugging Face integration.
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# commit_and_push_changes(f"Add agent {agent.name}")
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def load_agent_prompt(agent_name):
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def create_agent_from_text(name, text):
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"""Creates an AI agent from the provided text input."""
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skills = text.split('\n')
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agent = AIAgent(name, "AI agent created from text input.", skills)
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save_agent_to_file(agent)
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if agent_prompt is None:
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return f"Agent {agent_name} not found."
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# Load the GPT-2 model
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model_name = "gpt2"
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try:
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model = AutoModelForCausalLM.from_pretrained(model_name)
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except EnvironmentError as e:
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return f"Error loading model: {e}"
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# Combine agent prompt and user input (truncate if necessary)
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combined_input = f"{agent_prompt}\n\nUser: {input_text}\nAgent:"
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max_input_length = 900
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input_ids = tokenizer.encode(combined_input, return_tensors="pt")
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if input_ids.shape[1] > max_input_length:
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input_ids = input_ids[:, :max_input_length]
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# Generate response
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outputs = model.generate(
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input_ids,
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max_new_tokens=50,
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num_return_sequences=1,
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do_sample=True,
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pad_token_id=tokenizer.eos_token_id
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)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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return response
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# Basic chat interface (no agent)
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def chat_interface(input_text):
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# Load the GPT-2 model
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model_name = "gpt2"
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try:
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model = AutoModelForCausalLM.from_pretrained(model_name)
|
|
|
134 |
except EnvironmentError as e:
|
135 |
return f"Error loading model: {e}"
|
136 |
|
137 |
+
# Generate response
|
138 |
outputs = generator(input_text, max_new_tokens=50, num_return_sequences=1, do_sample=True)
|
139 |
response = outputs[0]['generated_text']
|
140 |
return response
|
141 |
|
142 |
|
143 |
def workspace_interface(project_name):
|
144 |
+
"""Manages project creation."""
|
145 |
project_path = os.path.join(PROJECT_ROOT, project_name)
|
146 |
if not os.path.exists(PROJECT_ROOT):
|
147 |
os.makedirs(PROJECT_ROOT)
|
|
|
149 |
os.makedirs(project_path)
|
150 |
st.session_state.workspace_projects[project_name] = {"files": []}
|
151 |
st.session_state.current_state['workspace_chat']['project_name'] = project_name
|
152 |
+
# (Optional) Commit and push if you have set up Hugging Face integration.
|
153 |
+
# commit_and_push_changes(f"Create project {project_name}")
|
154 |
return f"Project {project_name} created successfully."
|
155 |
else:
|
156 |
return f"Project {project_name} already exists."
|
157 |
|
158 |
|
159 |
def add_code_to_workspace(project_name, code, file_name):
|
160 |
+
"""Adds code to a file in the specified project."""
|
161 |
project_path = os.path.join(PROJECT_ROOT, project_name)
|
162 |
if os.path.exists(project_path):
|
163 |
file_path = os.path.join(project_path, file_name)
|
|
|
165 |
file.write(code)
|
166 |
st.session_state.workspace_projects[project_name]["files"].append(file_name)
|
167 |
st.session_state.current_state['workspace_chat']['added_code'] = {"file_name": file_name, "code": code}
|
168 |
+
# (Optional) Commit and push if you have set up Hugging Face integration.
|
169 |
+
# commit_and_push_changes(f"Add code to {file_name} in project {project_name}")
|
170 |
return f"Code added to {file_name} in project {project_name} successfully."
|
171 |
else:
|
172 |
return f"Project {project_name} does not exist."
|
173 |
|
|
|
174 |
def terminal_interface(command, project_name=None):
|
175 |
+
"""Executes commands in the terminal, optionally within a project's directory."""
|
176 |
if project_name:
|
177 |
project_path = os.path.join(PROJECT_ROOT, project_name)
|
178 |
if not os.path.exists(project_path):
|
|
|
180 |
result = subprocess.run(command, cwd=project_path, shell=True, capture_output=True, text=True)
|
181 |
else:
|
182 |
result = subprocess.run(command, shell=True, capture_output=True, text=True)
|
183 |
+
|
184 |
if result.returncode == 0:
|
185 |
st.session_state.current_state['toolbox']['terminal_output'] = result.stdout
|
186 |
return result.stdout
|
|
|
190 |
|
191 |
|
192 |
def summarize_text(text):
|
193 |
+
"""Summarizes text using a Hugging Face pipeline."""
|
194 |
summarizer = pipeline("summarization")
|
195 |
summary = summarizer(text, max_length=100, min_length=25, do_sample=False)
|
196 |
st.session_state.current_state['toolbox']['summary'] = summary[0]['summary_text']
|
|
|
198 |
|
199 |
|
200 |
def sentiment_analysis(text):
|
201 |
+
"""Analyzes sentiment of text using a Hugging Face pipeline."""
|
202 |
analyzer = pipeline("sentiment-analysis")
|
203 |
sentiment = analyzer(text)
|
204 |
st.session_state.current_state['toolbox']['sentiment'] = sentiment[0]
|
|
|
206 |
|
207 |
|
208 |
def code_editor_interface(code):
|
209 |
+
"""Formats and lints Python code."""
|
210 |
try:
|
211 |
formatted_code = black.format_str(code, mode=black.FileMode())
|
212 |
lint_result = StringIO()
|
213 |
lint.Run([
|
214 |
+
'--disable=C0114,C0115,C0116',
|
215 |
'--output-format=text',
|
216 |
+
'--reports=n',
|
217 |
+
'-'
|
218 |
], exit=False, do_exit=False)
|
219 |
lint_message = lint_result.getvalue()
|
220 |
return formatted_code, lint_message
|
|
|
223 |
|
224 |
|
225 |
def translate_code(code, input_language, output_language):
|
226 |
+
"""Translates code between programming languages."""
|
|
|
|
|
|
|
|
|
|
|
227 |
try:
|
228 |
translator = pipeline("translation", model=f"{input_language}-to-{output_language}")
|
229 |
translated_code = translator(code, max_length=10000)[0]['translation_text']
|
|
|
234 |
|
235 |
|
236 |
def generate_code(code_idea):
|
237 |
+
"""Generates code from a user idea using a Hugging Face pipeline."""
|
238 |
try:
|
239 |
+
generator = pipeline('text-generation', model='gpt2')
|
240 |
generated_code = generator(f"```python\n{code_idea}\n```", max_length=1000, num_return_sequences=1)[0][
|
241 |
'generated_text']
|
242 |
|
243 |
+
# Extract code from the generated text
|
244 |
start_index = generated_code.find("```python") + len("```python")
|
245 |
end_index = generated_code.find("```", start_index)
|
246 |
if start_index != -1 and end_index != -1:
|
|
|
253 |
|
254 |
|
255 |
def commit_and_push_changes(commit_message):
|
256 |
+
"""(Optional) Commits and pushes changes.
|
257 |
+
Needs to be configured for your Hugging Face repository.
|
258 |
+
"""
|
259 |
commands = [
|
260 |
"git add .",
|
261 |
f"git commit -m '{commit_message}'",
|
|
|
268 |
break
|
269 |
|
270 |
|
271 |
+
# --- Streamlit App ---
|
272 |
st.title("AI Agent Creator")
|
273 |
|
274 |
# Sidebar navigation
|
|
|
276 |
app_mode = st.sidebar.selectbox("Choose the app mode", ["AI Agent Creator", "Tool Box", "Workspace Chat App"])
|
277 |
|
278 |
if app_mode == "AI Agent Creator":
|
|
|
279 |
st.header("Create an AI Agent from Text")
|
|
|
|
|
280 |
agent_name = st.text_input("Enter agent name:")
|
281 |
text_input = st.text_area("Enter skills (one per line):")
|
282 |
if st.button("Create Agent"):
|
|
|
285 |
st.session_state.available_agents.append(agent_name)
|
286 |
|
287 |
elif app_mode == "Tool Box":
|
|
|
288 |
st.header("AI-Powered Tools")
|
289 |
|
|
|
290 |
st.subheader("Chat with CodeCraft")
|
291 |
chat_input = st.text_area("Enter your message:")
|
292 |
if st.button("Send"):
|
293 |
if chat_input.startswith("@"):
|
294 |
+
agent_name = chat_input.split(" ")[0][1:]
|
295 |
+
chat_input = " ".join(chat_input.split(" ")[1:])
|
296 |
chat_response = chat_interface_with_agent(chat_input, agent_name)
|
297 |
else:
|
298 |
chat_response = chat_interface(chat_input)
|
299 |
st.session_state.chat_history.append((chat_input, chat_response))
|
300 |
st.write(f"CodeCraft: {chat_response}")
|
301 |
|
|
|
302 |
st.subheader("Terminal")
|
303 |
terminal_input = st.text_input("Enter a command:")
|
304 |
if st.button("Run"):
|
|
|
306 |
st.session_state.terminal_history.append((terminal_input, terminal_output))
|
307 |
st.code(terminal_output, language="bash")
|
308 |
|
|
|
309 |
st.subheader("Code Editor")
|
310 |
code_editor = st.text_area("Write your code:", height=300)
|
311 |
if st.button("Format & Lint"):
|
|
|
313 |
st.code(formatted_code, language="python")
|
314 |
st.info(lint_message)
|
315 |
|
|
|
316 |
st.subheader("Summarize Text")
|
317 |
text_to_summarize = st.text_area("Enter text to summarize:")
|
318 |
if st.button("Summarize"):
|
319 |
summary = summarize_text(text_to_summarize)
|
320 |
st.write(f"Summary: {summary}")
|
321 |
|
|
|
322 |
st.subheader("Sentiment Analysis")
|
323 |
sentiment_text = st.text_area("Enter text for sentiment analysis:")
|
324 |
if st.button("Analyze Sentiment"):
|
325 |
sentiment = sentiment_analysis(sentiment_text)
|
326 |
st.write(f"Sentiment: {sentiment}")
|
327 |
|
|
|
328 |
st.subheader("Translate Code")
|
329 |
code_to_translate = st.text_area("Enter code to translate:")
|
330 |
+
source_language = st.selectbox("Source Language", ["en", "fr", "de", "es", "zh", "ja", "ko", "ru"])
|
331 |
+
target_language = st.selectbox("Target Language", ["en", "fr", "de", "es", "zh", "ja", "ko", "ru"])
|
332 |
if st.button("Translate Code"):
|
333 |
translated_code = translate_code(code_to_translate, source_language, target_language)
|
334 |
st.code(translated_code, language=target_language.lower())
|
335 |
|
|
|
336 |
st.subheader("Code Generation")
|
337 |
code_idea = st.text_input("Enter your code idea:")
|
338 |
if st.button("Generate Code"):
|
339 |
generated_code = generate_code(code_idea)
|
340 |
st.code(generated_code, language="python")
|
341 |
|
|
|
342 |
st.subheader("Preset Commands")
|
343 |
preset_commands = {
|
344 |
"Create a new project": "create_project('project_name')",
|
|
|
353 |
st.write(f"{command_name}: `{command}`")
|
354 |
|
355 |
elif app_mode == "Workspace Chat App":
|
|
|
356 |
st.header("Workspace Chat App")
|
357 |
|
|
|
358 |
st.subheader("Create a New Project")
|
359 |
project_name = st.text_input("Enter project name:")
|
360 |
if st.button("Create Project"):
|
361 |
workspace_status = workspace_interface(project_name)
|
362 |
st.success(workspace_status)
|
363 |
|
|
|
364 |
st.subheader("Add Code to Workspace")
|
365 |
code_to_add = st.text_area("Enter code to add to workspace:")
|
366 |
file_name = st.text_input("Enter file name (e.g. 'app.py'):")
|
|
|
368 |
add_code_status = add_code_to_workspace(project_name, code_to_add, file_name)
|
369 |
st.success(add_code_status)
|
370 |
|
|
|
371 |
st.subheader("Terminal (Workspace Context)")
|
372 |
terminal_input = st.text_input("Enter a command within the workspace:")
|
373 |
if st.button("Run Command"):
|
374 |
terminal_output = terminal_interface(terminal_input, project_name)
|
375 |
st.code(terminal_output, language="bash")
|
376 |
|
|
|
377 |
st.subheader("Chat with CodeCraft for Guidance")
|
378 |
chat_input = st.text_area("Enter your message for guidance:")
|
379 |
if st.button("Get Guidance"):
|
|
|
381 |
st.session_state.chat_history.append((chat_input, chat_response))
|
382 |
st.write(f"CodeCraft: {chat_response}")
|
383 |
|
|
|
384 |
st.subheader("Chat History")
|
385 |
for user_input, response in st.session_state.chat_history:
|
386 |
st.write(f"User: {user_input}")
|
387 |
st.write(f"CodeCraft: {response}")
|
388 |
|
|
|
389 |
st.subheader("Terminal History")
|
390 |
for command, output in st.session_state.terminal_history:
|
391 |
st.write(f"Command: {command}")
|
392 |
st.code(output, language="bash")
|
393 |
|
|
|
394 |
st.subheader("Workspace Projects")
|
395 |
for project, details in st.session_state.workspace_projects.items():
|
396 |
st.write(f"Project: {project}")
|
397 |
for file in details['files']:
|
398 |
st.write(f" - {file}")
|
399 |
|
|
|
400 |
st.subheader("Chat with AI Agents")
|
401 |
selected_agent = st.selectbox("Select an AI agent", st.session_state.available_agents)
|
402 |
agent_chat_input = st.text_area("Enter your message for the agent:")
|
|
|
405 |
st.session_state.chat_history.append((agent_chat_input, agent_chat_response))
|
406 |
st.write(f"{selected_agent}: {agent_chat_response}")
|
407 |
|
|
|
408 |
st.subheader("Automate Build Process")
|
409 |
if st.button("Automate"):
|
410 |
+
if selected_agent:
|
411 |
+
agent = AIAgent(selected_agent, "", [])
|
412 |
+
summary, next_step = agent.autonomous_build(st.session_state.chat_history, st.session_state.workspace_projects)
|
413 |
+
st.write("Autonomous Build Summary:")
|
414 |
+
st.write(summary)
|
415 |
+
st.write("Next Step:")
|
416 |
+
st.write(next_step)
|
417 |
+
else:
|
418 |
+
st.warning("Please select an AI agent first.")
|