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Parent(s):
6164a6e
Update app.py
Browse files
app.py
CHANGED
@@ -1,15 +1,25 @@
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import streamlit as st
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import os
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import subprocess
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import random
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import string
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from huggingface_hub import cached_download, hf_hub_url
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from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
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import black
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import
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from
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# Define functions for each feature
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@@ -23,22 +33,36 @@ def chat_interface(input_text):
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Returns:
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The chatbot's response.
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"""
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# Load the
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model_name =
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# Generate chatbot response
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return response
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# 2. Terminal
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def terminal_interface(command):
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"""Executes commands in the terminal.
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Args:
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command: User's command.
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Returns:
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The terminal output.
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try:
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process = subprocess.run(command.split(), capture_output=True, text=True)
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output = process.stdout
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except Exception as e:
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output = f
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return output
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# 3. Code Editor
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def code_editor_interface(code):
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"""Provides code completion, formatting, and linting in the code editor.
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# Lint code using pylint
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try:
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pylint_output =
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except Exception as e:
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lint_message = f"Pylint error: {e}"
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return formatted_code, lint_message
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# 4. Workspace
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def workspace_interface(project_name):
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"""Manages projects, files, and resources in the workspace.
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Returns:
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Project creation status.
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"""
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# Create project directory
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try:
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os.makedirs(
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except FileExistsError:
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status = f'Project
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return status
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# 5. AI-Infused Tools
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# Define custom AI-powered tools using Hugging Face models
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Returns:
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Summarized text.
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"""
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return summary
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# 6. Code Generation
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def generate_code(idea):
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"""Generates code based on a given idea using the
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Args:
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idea: The idea for the code to be generated.
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"""
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# Load the code generation model
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model_name =
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# Generate the code
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input_text = f"""
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return generated_code
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Args:
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text:
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Returns:
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"""
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
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result = classifier(text)[0]
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return result
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translation = translator(text, target_lang=target_language)[0]['translation_text']
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return translation
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st.sidebar.header("Select Workspace")
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project_name = st.sidebar.selectbox("Choose a project", os.listdir('projects'))
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# Chat Interface
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st.header("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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chat_response = chat_interface(chat_input)
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st.write(f"CodeCraft: {chat_response}")
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# Terminal Interface
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st.header("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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terminal_output = terminal_interface(terminal_input)
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st.code(terminal_output, language="bash")
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# Code Editor Interface
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st.header("Code Editor")
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code_editor = st.text_area("Write your code:", language="python", height=300)
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if st.button("Format & Lint"):
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formatted_code, lint_message = code_editor_interface(code_editor)
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st.code(formatted_code, language="python")
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st.info(lint_message)
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# AI-Infused Tools
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st.header("AI-Powered Tools")
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# Text Summarization
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st.subheader("Text Summarization")
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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
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st.subheader("Sentiment Analysis")
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text_to_analyze = st.text_area("Enter text to analyze sentiment:")
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if st.button("Analyze Sentiment"):
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sentiment_result = analyze_sentiment(text_to_analyze)
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st.write(f"Sentiment: {sentiment_result['label']}, Score: {sentiment_result['score']}")
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# Text Translation
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st.subheader("Text Translation")
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text_to_translate = st.text_area("Enter text to translate:")
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target_language = st.selectbox("Choose target language", ['fr', 'es', 'de', 'zh-CN']) # Example languages
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if st.button("Translate"):
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translation = translate_text(text_to_translate, target_language)
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st.write(f"Translation: {translation}")
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# Code Generation
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st.header("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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try:
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generated_code = generate_code(code_idea)
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st.code(generated_code, language="python")
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except Exception as e:
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st.error(f"Error generating code: {e}")
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hf_token = st.text_input("Enter your Hugging Face Token:")
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if hf_token:
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# Set the token using HfFolder
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HfFolder.save_token(hf_token)
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command = f"cd projects/{project_name} && streamlit run chat_app.py"
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import streamlit as st
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import os
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import subprocess
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from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
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import black
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from pylint import lint
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from io import StringIO
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import openai
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import sys
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# Set your OpenAI API key here
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openai.api_key = "YOUR_OPENAI_API_KEY"
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PROJECT_ROOT = "projects"
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# Global state to manage communication between Tool Box and Workspace Chat App
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if 'chat_history' not in st.session_state:
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st.session_state.chat_history = []
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if 'terminal_history' not in st.session_state:
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st.session_state.terminal_history = []
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if 'workspace_projects' not in st.session_state:
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st.session_state.workspace_projects = {}
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# Define functions for each feature
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Returns:
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The chatbot's response.
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"""
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# Load the GPT-2 model which is compatible with AutoModelForCausalLM
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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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tokenizer = AutoTokenizer.from_pretrained(model_name)
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generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
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except EnvironmentError as e:
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return f"Error loading model: {e}"
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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(input_text, 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 chatbot response
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outputs = model.generate(
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input_ids, max_new_tokens=50, num_return_sequences=1, do_sample=True
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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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# 2. Terminal
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def terminal_interface(command, project_name=None):
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"""Executes commands in the terminal.
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Args:
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command: User's command.
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project_name: Name of the project workspace to add installed packages.
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Returns:
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The terminal output.
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try:
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process = subprocess.run(command.split(), capture_output=True, text=True)
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output = process.stdout
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# If the command is to install a package, update the workspace
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if "install" in command and project_name:
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requirements_path = os.path.join(PROJECT_ROOT, project_name, "requirements.txt")
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with open(requirements_path, "a") as req_file:
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package_name = command.split()[-1]
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req_file.write(f"{package_name}\n")
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except Exception as e:
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output = f"Error: {e}"
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return output
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# 3. Code Editor
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def code_editor_interface(code):
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"""Provides code completion, formatting, and linting in the code editor.
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# Lint code using pylint
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try:
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pylint_output = StringIO()
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sys.stdout = pylint_output
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sys.stderr = pylint_output
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lint.Run(['--from-stdin'], stdin=StringIO(formatted_code))
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sys.stdout = sys.__stdout__
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sys.stderr = sys.__stderr__
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lint_message = pylint_output.getvalue()
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except Exception as e:
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lint_message = f"Pylint error: {e}"
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return formatted_code, lint_message
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# 4. Workspace
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def workspace_interface(project_name):
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"""Manages projects, files, and resources in the workspace.
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Returns:
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Project creation status.
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"""
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project_path = os.path.join(PROJECT_ROOT, project_name)
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# Create project directory
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try:
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os.makedirs(project_path)
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requirements_path = os.path.join(project_path, "requirements.txt")
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with open(requirements_path, "w") as req_file:
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req_file.write("") # Initialize an empty requirements.txt file
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status = f'Project "{project_name}" created successfully.'
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st.session_state.workspace_projects[project_name] = {'files': []}
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except FileExistsError:
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status = f'Project "{project_name}" already exists.'
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return status
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def add_code_to_workspace(project_name, code, file_name):
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"""Adds selected code files to the workspace.
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Args:
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project_name: Name of the project.
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code: Code to be added.
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file_name: Name of the file to be created.
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Returns:
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File creation status.
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"""
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project_path = os.path.join(PROJECT_ROOT, project_name)
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file_path = os.path.join(project_path, file_name)
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try:
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with open(file_path, "w") as code_file:
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code_file.write(code)
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status = f'File "{file_name}" added to project "{project_name}" successfully.'
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st.session_state.workspace_projects[project_name]['files'].append(file_name)
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except Exception as e:
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status = f"Error: {e}"
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return status
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# 5. AI-Infused Tools
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# Define custom AI-powered tools using Hugging Face models
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Returns:
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Summarized text.
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"""
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# Load the summarization model
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model_name = "facebook/bart-large-cnn"
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try:
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summarizer = pipeline("summarization", model=model_name)
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except EnvironmentError as e:
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return f"Error loading model: {e}"
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# Truncate input text to avoid exceeding the model's maximum length
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max_input_length = 1024
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inputs = text
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if len(text) > max_input_length:
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+
inputs = text[:max_input_length]
|
190 |
+
|
191 |
+
# Generate summary
|
192 |
+
summary = summarizer(inputs, max_length=100, min_length=30, do_sample=False)[0][
|
193 |
+
"summary_text"
|
194 |
+
]
|
195 |
return summary
|
196 |
|
197 |
+
# Example: Sentiment analysis tool
|
198 |
+
def sentiment_analysis(text):
|
199 |
+
"""Performs sentiment analysis on a given text using a Hugging Face model.
|
200 |
+
|
201 |
+
Args:
|
202 |
+
text: Text to be analyzed.
|
203 |
+
|
204 |
+
Returns:
|
205 |
+
Sentiment analysis result.
|
206 |
+
"""
|
207 |
+
# Load the sentiment analysis model
|
208 |
+
model_name = "distilbert-base-uncased-finetuned-sst-2-english"
|
209 |
+
try:
|
210 |
+
analyzer = pipeline("sentiment-analysis", model=model_name)
|
211 |
+
except EnvironmentError as e:
|
212 |
+
return f"Error loading model: {e}"
|
213 |
+
|
214 |
+
# Perform sentiment analysis
|
215 |
+
result = analyzer(text)[0]
|
216 |
+
return result
|
217 |
+
|
218 |
+
# Example: Text translation tool (code translation)
|
219 |
+
def translate_code(code, source_language, target_language):
|
220 |
+
"""Translates code from one programming language to another using OpenAI Codex.
|
221 |
+
|
222 |
+
Args:
|
223 |
+
code: Code to be translated.
|
224 |
+
source_language: The source programming language.
|
225 |
+
target_language: The target programming language.
|
226 |
+
|
227 |
+
Returns:
|
228 |
+
Translated code.
|
229 |
+
"""
|
230 |
+
prompt = f"Translate the following {source_language} code to {target_language}:\n\n{code}"
|
231 |
+
try:
|
232 |
+
response = openai.Completion.create(
|
233 |
+
engine="code-davinci-002",
|
234 |
+
prompt=prompt,
|
235 |
+
max_tokens=1024,
|
236 |
+
temperature=0.3,
|
237 |
+
top_p=1,
|
238 |
+
n=1,
|
239 |
+
stop=None
|
240 |
+
)
|
241 |
+
translated_code = response.choices[0].text.strip()
|
242 |
+
except Exception as e:
|
243 |
+
translated_code = f"Error: {e}"
|
244 |
+
return translated_code
|
245 |
+
|
246 |
+
|
247 |
# 6. Code Generation
|
248 |
def generate_code(idea):
|
249 |
+
"""Generates code based on a given idea using the EleutherAI/gpt-neo-2.7B model.
|
250 |
|
251 |
Args:
|
252 |
idea: The idea for the code to be generated.
|
|
|
256 |
"""
|
257 |
|
258 |
# Load the code generation model
|
259 |
+
model_name = "EleutherAI/gpt-neo-2.7B"
|
260 |
+
try:
|
261 |
+
model = AutoModelForCausalLM.from_pretrained(model_name)
|
262 |
+
tokenizer = AutoTokenizer.from_pretrained(model_name)
|
263 |
+
except EnvironmentError as e:
|
264 |
+
return f"Error loading model: {e}"
|
265 |
|
266 |
# Generate the code
|
267 |
input_text = f"""
|
|
|
285 |
|
286 |
return generated_code
|
287 |
|
288 |
+
|
289 |
+
# 7. AI Personas Creator
|
290 |
+
def create_persona_from_text(text):
|
291 |
+
"""Creates an AI persona from the given text.
|
292 |
|
293 |
Args:
|
294 |
+
text: Text to be used for creating the persona.
|
295 |
|
296 |
Returns:
|
297 |
+
Persona prompt.
|
298 |
"""
|
299 |
+
persona_prompt = f"""
|
300 |
+
As an elite expert developer with the highest level of proficiency in Streamlit, Gradio, and Hugging Face, I possess a comprehensive understanding of these technologies and their applications in web development and deployment. My expertise encompasses the following areas:
|
|
|
|
|
|
|
|
|
301 |
|
302 |
+
Streamlit:
|
303 |
+
* In-depth knowledge of Streamlit's architecture, components, and customization options.
|
304 |
+
* Expertise in creating interactive and user-friendly dashboards and applications.
|
305 |
+
* Proficiency in integrating Streamlit with various data sources and machine learning models.
|
306 |
|
307 |
+
Gradio:
|
308 |
+
* Thorough understanding of Gradio's capabilities for building and deploying machine learning interfaces.
|
309 |
+
* Expertise in creating custom Gradio components and integrating them with Streamlit applications.
|
310 |
+
* Proficiency in using Gradio to deploy models from Hugging Face and other frameworks.
|
311 |
|
312 |
+
Hugging Face:
|
313 |
+
* Comprehensive knowledge of Hugging Face's model hub and Transformers library.
|
314 |
+
* Expertise in fine-tuning and deploying Hugging Face models for various NLP and computer vision tasks.
|
315 |
+
* Proficiency in using Hugging Face's Spaces platform for model deployment and sharing.
|
|
|
|
|
316 |
|
317 |
+
Deployment:
|
318 |
+
* In-depth understanding of best practices for deploying Streamlit and Gradio applications.
|
319 |
+
* Expertise in deploying models on cloud platforms such as AWS, Azure, and GCP.
|
320 |
+
* Proficiency in optimizing deployment configurations for performance and scalability.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
321 |
|
322 |
+
Additional Skills:
|
323 |
+
* Strong programming skills in Python and JavaScript.
|
324 |
+
* Familiarity with Docker and containerization technologies.
|
325 |
+
* Excellent communication and problem-solving abilities.
|
326 |
|
327 |
+
I am confident that I can leverage my expertise to assist you in developing and deploying cutting-edge web applications using Streamlit, Gradio, and Hugging Face. Please feel free to ask any questions or present any challenges you may encounter.
|
|
|
|
|
|
|
|
|
328 |
|
329 |
+
Example:
|
|
|
330 |
|
331 |
+
Task:
|
332 |
+
Develop a Streamlit application that allows users to generate text using a Hugging Face model. The application should include a Gradio component for user input and model prediction.
|
333 |
+
|
334 |
+
Solution:
|
335 |
+
|
336 |
+
import streamlit as st
|
337 |
+
import gradio as gr
|
338 |
+
from transformers import pipeline
|
339 |
+
|
340 |
+
# Create a Hugging Face pipeline
|
341 |
+
huggingface_model = pipeline("text-generation")
|
342 |
+
|
343 |
+
# Create a Streamlit app
|
344 |
+
st.title("Hugging Face Text Generation App")
|
345 |
+
|
346 |
+
# Define a Gradio component
|
347 |
+
demo = gr.Interface(
|
348 |
+
fn=huggingface_model,
|
349 |
+
inputs=gr.Textbox(lines=2),
|
350 |
+
outputs=gr.Textbox(lines=1),
|
351 |
+
)
|
352 |
+
|
353 |
+
# Display the Gradio component in the Streamlit app
|
354 |
+
st.write(demo)
|
355 |
+
"""
|
356 |
+
return persona_prompt
|
357 |
+
|
358 |
+
|
359 |
+
# Streamlit App
|
360 |
+
st.title("AI Personas Creator")
|
361 |
+
|
362 |
+
# Sidebar navigation
|
363 |
+
st.sidebar.title("Navigation")
|
364 |
+
app_mode = st.sidebar.selectbox("Choose the app mode", ["AI Personas Creator", "Tool Box", "Workspace Chat App"])
|
365 |
+
|
366 |
+
if app_mode == "AI Personas Creator":
|
367 |
+
# AI Personas Creator
|
368 |
+
st.header("Create the System Prompt of an AI Persona from YouTube or Text")
|
369 |
+
|
370 |
+
st.subheader("From Text")
|
371 |
+
text_input = st.text_area("Enter text to create an AI persona:")
|
372 |
+
if st.button("Create Persona"):
|
373 |
+
persona_prompt = create_persona_from_text(text_input)
|
374 |
+
st.subheader("Persona Prompt")
|
375 |
+
st.text_area("You may now copy the text below and use it as Custom prompt!", value=persona_prompt, height=300)
|
376 |
+
|
377 |
+
elif app_mode == "Tool Box":
|
378 |
+
# Tool Box
|
379 |
+
st.header("AI-Powered Tools")
|
380 |
+
|
381 |
+
# Chat Interface
|
382 |
+
st.subheader("Chat with CodeCraft")
|
383 |
+
chat_input = st.text_area("Enter your message:")
|
384 |
+
if st.button("Send"):
|
385 |
+
chat_response = chat_interface(chat_input)
|
386 |
+
st.session_state.chat_history.append((chat_input, chat_response))
|
387 |
+
st.write(f"CodeCraft: {chat_response}")
|
388 |
+
|
389 |
+
# Terminal Interface
|
390 |
+
st.subheader("Terminal")
|
391 |
+
terminal_input = st.text_input("Enter a command:")
|
392 |
+
if st.button("Run"):
|
393 |
+
terminal_output = terminal_interface(terminal_input)
|
394 |
+
st.session_state.terminal_history.append((terminal_input, terminal_output))
|
395 |
+
st.code(terminal_output, language="bash")
|
396 |
+
|
397 |
+
# Code Editor Interface
|
398 |
+
st.subheader("Code Editor")
|
399 |
+
code_editor = st.text_area("Write your code:", height=300)
|
400 |
+
if st.button("Format & Lint"):
|
401 |
+
formatted_code, lint_message = code_editor_interface(code_editor)
|
402 |
+
st.code(formatted_code, language="python")
|
403 |
+
st.info(lint_message)
|
404 |
+
|
405 |
+
# Text Summarization Tool
|
406 |
+
st.subheader("Summarize Text")
|
407 |
+
text_to_summarize = st.text_area("Enter text to summarize:")
|
408 |
+
if st.button("Summarize"):
|
409 |
+
summary = summarize_text(text_to_summarize)
|
410 |
+
st.write(f"Summary: {summary}")
|
411 |
+
|
412 |
+
# Sentiment Analysis Tool
|
413 |
+
st.subheader("Sentiment Analysis")
|
414 |
+
sentiment_text = st.text_area("Enter text for sentiment analysis:")
|
415 |
+
if st.button("Analyze Sentiment"):
|
416 |
+
sentiment = sentiment_analysis(sentiment_text)
|
417 |
+
st.write(f"Sentiment: {sentiment}")
|
418 |
+
|
419 |
+
# Text Translation Tool (Code Translation)
|
420 |
+
st.subheader("Translate Code")
|
421 |
+
code_to_translate = st.text_area("Enter code to translate:")
|
422 |
+
source_language = st.text_input("Enter source language (e.g., 'Python'):")
|
423 |
+
target_language = st.text_input("Enter target language (e.g., 'JavaScript'):")
|
424 |
+
if st.button("Translate Code"):
|
425 |
+
translated_code = translate_code(code_to_translate, source_language, target_language)
|
426 |
+
st.code(translated_code, language=target_language.lower())
|
427 |
+
|
428 |
+
# Code Generation
|
429 |
+
st.subheader("Code Generation")
|
430 |
+
code_idea = st.text_input("Enter your code idea:")
|
431 |
+
if st.button("Generate Code"):
|
432 |
+
generated_code = generate_code(code_idea)
|
433 |
+
st.code(generated_code, language="python")
|
434 |
+
|
435 |
+
elif app_mode == "Workspace Chat App":
|
436 |
+
# Workspace Chat App
|
437 |
+
st.header("Workspace Chat App")
|
438 |
+
|
439 |
+
# Project Workspace Creation
|
440 |
+
st.subheader("Create a New Project")
|
441 |
+
project_name = st.text_input("Enter project name:")
|
442 |
+
if st.button("Create Project"):
|
443 |
+
workspace_status = workspace_interface(project_name)
|
444 |
+
st.success(workspace_status)
|
445 |
+
|
446 |
+
# Add Code to Workspace
|
447 |
+
st.subheader("Add Code to Workspace")
|
448 |
+
code_to_add = st.text_area("Enter code to add to workspace:")
|
449 |
+
file_name = st.text_input("Enter file name (e.g., 'app.py'):")
|
450 |
+
if st.button("Add Code"):
|
451 |
+
add_code_status = add_code_to_workspace(project_name, code_to_add, file_name)
|
452 |
+
st.success(add_code_status)
|
453 |
+
|
454 |
+
# Terminal Interface with Project Context
|
455 |
+
st.subheader("Terminal (Workspace Context)")
|
456 |
+
terminal_input = st.text_input("Enter a command within the workspace:")
|
457 |
+
if st.button("Run Command"):
|
458 |
+
terminal_output = terminal_interface(terminal_input, project_name)
|
459 |
+
st.code(terminal_output, language="bash")
|
460 |
+
|
461 |
+
# Chat Interface for Guidance
|
462 |
+
st.subheader("Chat with CodeCraft for Guidance")
|
463 |
+
chat_input = st.text_area("Enter your message for guidance:")
|
464 |
+
if st.button("Get Guidance"):
|
465 |
+
chat_response = chat_interface(chat_input)
|
466 |
+
st.session_state.chat_history.append((chat_input, chat_response))
|
467 |
+
st.write(f"CodeCraft: {chat_response}")
|
468 |
+
|
469 |
+
# Display Chat History
|
470 |
+
st.subheader("Chat History")
|
471 |
+
for user_input, response in st.session_state.chat_history:
|
472 |
+
st.write(f"User: {user_input}")
|
473 |
+
st.write(f"CodeCraft: {response}")
|
474 |
+
|
475 |
+
# Display Terminal History
|
476 |
+
st.subheader("Terminal History")
|
477 |
+
for command, output in st.session_state.terminal_history:
|
478 |
+
st.write(f"Command: {command}")
|
479 |
+
st.code(output, language="bash")
|
480 |
+
|
481 |
+
# Display Projects and Files
|
482 |
+
st.subheader("Workspace Projects")
|
483 |
+
for project, details in st.session_state.workspace_projects.items():
|
484 |
+
st.write(f"Project: {project}")
|
485 |
+
for file in details['files']:
|
486 |
+
st.write(f" - {file}")
|