| import streamlit as st |
| import google.generativeai as genai |
| import re |
| import os |
| import platform |
|
|
| |
| API_KEY = st.secrets["GOOGLE_API_KEY"] |
|
|
| |
| st.set_page_config(page_title="🐍✨ Gemini2 Pip Gen Pro", page_icon="🐍✨", layout="wide") |
|
|
| |
| def send_message_to_model(message, model_name, temperature, top_p, top_k, max_tokens): |
| """Sends a message to the AI model and returns the response.""" |
| try: |
| |
| GENERATION_CONFIG = { |
| "temperature": temperature, |
| "top_p": top_p, |
| "top_k": top_k, |
| "response_mime_type": "text/plain", |
| "max_output_tokens": max_tokens, |
| } |
| MODEL = genai.GenerativeModel( |
| model_name=model_name, |
| generation_config=GENERATION_CONFIG, |
| ) |
| |
| response = MODEL.start_chat(history=[]).send_message(message) |
| return response.text |
| except Exception as e: |
| st.error(f"❌ Error communicating with the AI: {e}") |
| return None |
|
|
| def generate_pip_command(prompt_base, model_name, temperature, top_p, top_k, max_tokens, selected_libraries, selected_groups, custom_requirements, specific_details, pip_options, target_os): |
| """Generates a pip command based on user settings.""" |
| prompt = f""" |
| You are an expert Python development assistant. Your task is to generate a complete and efficient pip command based on the following description: |
| |
| **Goal:** Create the most complete, detailed, and efficient pip install command possible, considering all variables, edge cases, and potential scenarios. |
| |
| **Target Operating System:** {target_os} |
| |
| **Command Description:** {prompt_base} |
| |
| **Selected Libraries:** {selected_libraries if selected_libraries else "None"} |
| **Selected Groups:** {selected_groups if selected_groups else "None"} |
| **Custom Requirements:** {custom_requirements if custom_requirements else "None"} |
| **Specific Details:** {specific_details if specific_details else "None"} |
| **Pip Options:** {pip_options if pip_options else "None"} |
| |
| **Response Format:** |
| - Respond in Markdown format, including a pip command code block, a bash code block and powershell code block with its original formatting, without line breaks. |
| - The pip command code block must be delimited by ```pip and ```. |
| - The bash code block must be delimited by ```bash and ```. |
| - The powershell code block must be delimited by ```powershell and ```. |
| - Do not include comments, explanations, or any other text outside the code block. |
| - The code must maintain its full vertical formatting, respecting indentation and line breaks. |
| - Explore different approaches, techniques, and advanced practices, always prioritizing security and efficiency. |
| - Use advanced pip resources such as version constraints, index options, and requirements files when necessary. |
| - If a specific version of a library is specified, use it. |
| - Unless the user specifies otherwise, use the most current versions of the libraries and pip, using and following best practices. |
| - Use incremental reasoning to add improvements, expansions, and considerations to your code. |
| - Use the history of the conversations so that the response is incremental. |
| - If the target OS is Windows, use pip commands compatible with Windows, and if the target OS is Linux, use pip commands compatible with Linux |
| |
| **Important:** |
| - Generate only one command at a time. |
| - Create the longest, most complete, and detailed command possible to cover a wide range of possibilities and scenarios. |
| - Consider all the details of the request, expanding the response and improving the command. |
| - If the prompt asks to install a library that is not in a given group, install it in the command, but include in the prompt that library is not in the selected groups. |
| - If a version of a library is specified, install it using a version constraint. |
| - Consider if the user needs a requirements.txt file or not and if needed use it in your command. |
| """ |
|
|
| response = send_message_to_model(prompt, model_name, temperature, top_p, top_k, max_tokens) |
| return response |
|
|
| def parse_and_save_code(ai_code, short_title): |
| """Parses the markdown and saves the code.""" |
| pip_match = re.search(r'```pip\s*(.*?)\s*```', ai_code, re.DOTALL | re.IGNORECASE) |
| bash_match = re.search(r'```bash\s*(.*?)\s*```', ai_code, re.DOTALL | re.IGNORECASE) |
| ps1_match = re.search(r'```powershell\s*(.*?)\s*```', ai_code, re.DOTALL | re.IGNORECASE) |
| |
| pip_code = pip_match.group(1).strip() if pip_match else ai_code.strip() |
| bash_code = bash_match.group(1).strip() if bash_match else None |
| ps1_code = ps1_match.group(1).strip() if ps1_match else None |
|
|
| base_file_name = f"pip_command_{short_title}" |
|
|
| files = {} |
| |
| files["pip"]= {"name":f"{base_file_name}.txt", "code":pip_code} |
|
|
| if bash_code: |
| files["bash"]= {"name":f"{base_file_name}.sh", "code":bash_code} |
| |
| if ps1_code: |
| files["powershell"]= {"name":f"{base_file_name}.ps1", "code":ps1_code} |
| |
| for key, value in files.items(): |
| with open(value["name"], "w") as f: |
| f.write(value["code"]) |
|
|
| return files |
|
|
|
|
| def main(): |
| st.title("🐍✨ Gemini2 Pip Gen Pro by [Elias Andrade](https://github.com/chaos4455)") |
| st.markdown("Generate advanced pip install commands with ease! 🚀") |
| st.markdown("---") |
|
|
| |
| col1, col2 = st.columns([1, 3]) |
| |
| with col1: |
| st.header("⚙️ Settings") |
| |
| with st.expander("✨ AI Settings"): |
| model_name = st.selectbox("🤖 AI Model", ["gemini-2.0-flash-exp", "gemini-1.5-flash"], index=0, help="Choose the AI model.") |
| temperature = st.slider("🌡️ Temperature", min_value=0.1, max_value=1.0, value=0.7, step=0.1, help="Adjust the AI's creativity.") |
| top_p = st.slider("Top P", min_value=0.1, max_value=1.0, value=0.8, step=0.1, help="Adjust the AI's sampling.") |
| top_k = st.slider("Top K", min_value=1, max_value=100, value=40, step=1, help="Adjust the AI's number of candidate tokens.") |
| max_tokens = st.number_input("📏 Max Tokens", min_value=128, max_value=8192, value=8192, step=128, help="Adjust the maximum size of the response.") |
| |
| with st.expander("📝 Libraries & Groups"): |
| available_libraries = { |
| "📊 Data Analysis": ["pandas", "numpy", "scipy", "matplotlib", "seaborn", "plotly", "bokeh", "statsmodels", "scikit-image", "geopandas", "altair", "holoviews", "datashader", "missingno", "vaex", "dask", "xarray", "polars", "arrow", "numba", "cudf", "cupy", "streamz", "panel", "hvplot"], |
| "🧠 Machine Learning": ["scikit-learn", "tensorflow", "torch", "keras", "xgboost", "lightgbm", "catboost", "pytorch-lightning", "transformers", "optuna", "mlflow", "gradio", "huggingface-hub", "sentence-transformers", "fastai", "librosa", "gensim", "spacy", "nltk", "opencv-python", "imbalanced-learn", "sktime", "umap-learn", "fairlearn", "shap", "eli5", "snorkel", "thinc"], |
| "🕸️ Web Development": ["flask", "fastapi", "django", "requests", "beautifulsoup4", "aiohttp", "uvicorn", "gunicorn", "jinja2", "starlette", "websockets", "flask-restful", "django-rest-framework", "scrapy", "selenium", "playwright", "httpx", "rich", "pyramid", "bottle", "tornado", "dash", "plotly-dash", "streamlit", "gradio"], |
| "🗄️ Database": ["sqlalchemy", "psycopg2", "pymongo", "mysql-connector-python", "sqlite3", "redis", "cassandra-driver", "pyodbc", "aiosqlite", "kafka-python", "motor", "neo4j", "influxdb", "elasticsearch", "pyarrow", "clickhouse-driver", "arangodb", "couchbase", "dgraph-python", "tinydb", "dataset"], |
| "☁️ Cloud": ["boto3", "google-cloud-storage", "azure-storage-blob", "kubernetes", "docker", "apache-libcloud", "pulumi", "awscli", "google-cloud-sdk", "azure-cli", "terraform", "openstack", "ansible", "salt", "chef", "cdktf", "serverless", "aws-cdk", "google-cloud-build", "azure-pipelines", "docker-compose", "moto", "localstack"], |
| "🤖 IA & LLM": ["langchain", "openai", "diffusers", "sentence-transformers", "stable-diffusion", "huggingface-hub", "pyannote-audio", "whisper", "nltk", "spacy", "transformers", "accelerate", "datasets", "einops", "faiss-cpu", "bitsandbytes", "peft", "trl", "haystack", "llama-index", "deepsparse", "nanogpt", "autogpt", "babyagi","ml-agents", "tensorboardx"], |
| "🛠️ Dev Tools": ["pytest", "flake8", "mypy", "black", "isort", "pylint", "tox", "pre-commit", "bandit", "coverage", "virtualenv", "pipenv", "poetry", "invoke", "nox", "twine", "wheel", "setuptools", "build", "debugpy", "pyinstrument", "memory-profiler", "cProfile", "pdbpp", "ipython"], |
| "⚙️ Data Engineering": ["apache-airflow", "dask", "luigi", "pyspark", "ray", "prefect", "dbt-core", "pandas-gbq", "petl", "sqlalchemy-redshift", "fugue", "ibis-framework", "mindsdb", "koalas", "vaex-core", "feast", "flink", "beam", "kafka-python", "clickhouse-driver", "superset", "metabase", "trino"], |
| "🌐 Networking": ["requests", "httpx", "socketio", "paramiko", "netmiko", "scapy", "dnspython", "asyncssh", "tqdm", "gevent", "websocket-client", "pyserial", "grpcio", "aiozmq", "uvloop", "aiofiles", "urllib3", "fastsocket", "twisted", "aiohttp-socks", "aioredis", "websockets"], |
| "🔒 Security": ["cryptography", "pyjwt", "requests-oauthlib", "paramiko", "pyOpenSSL", "passlib", "hashlib", "bcrypt", "python-nmap", "sqlmap", "scapy", "yara-python", "pwn", "mitmproxy", "themis", "pycryptodome", "tpm2-py", "pyspy", "volatility3", "pefile", "dpkt"], |
| "🎨 GUI": ["tkinter", "pyqt5", "kivy", "wxpython", "pygame", "dearpygui", "pyglet", "flet", "toga", "eel", "qt-material", "pyside6", "pywebview", "guizero", "remif", "taipy"], |
| "🧪 Testing": ["unittest", "pytest-cov", "hypothesis", "behave", "locust", "nose", "selenium", "playwright", "mock", "freezegun", "ddt", "tox", "robotframework", "hypothesis", "vcrpy", "responses", "faker", "factory-boy", "coveragepy", "parameterized"], |
| "🧰 Utilities": ["click", "typer", "argparse", "rich", "tqdm", "colorama", "schedule", "python-dotenv", "shutil", "pathlib", "arrow", "toml", "json5", "xmltodict", "humanize", "pendulum", "inflect", "unidecode", "boltons", "delegator.py"], |
| "📚 Documentation": ["sphinx", "mkdocs", "pdoc", "readthedocs-sphinx-search", "numpydoc", "docutils", "recommonmark", "sphinx-rtd-theme", "furo", "m2r2", "autoapi", "plantuml", "pycco", "doc8", "griffe", "mistletoe", "marko"], |
| "🎮 Game Dev": ["pygame", "pyglet", "arcade", "panda3d", "ursina", "cocos2d", "renpy", "pyopengl", "cymunk", "pytmx", "pybox2d", "pymunk", "moderngl", "glumpy", "glfw", "sdl2"], |
| "💹 Finance": ["numpy-financial", "pandas-datareader", "scikit-portfolio", "yfinance", "finmarketpy", "quantstats", "TA-Lib", "riskfolio-optimization", "alphalens", "pyfolio", "zipline", "backtrader", "quantlib-python", "pyfin", "quandl", "vnpy"], |
| "🤖 Robotics": ["rospy", "pybullet", "mujoco-py", "open3d", "numpy-stl", "pyserial", "opencv-python", "pymavlink", "robotframework", "webots", "vpython", "transforms3d", "trimesh", "pyrealsense2", "aiortc", "gymnasium", "stable-baselines3"], |
| "🧬 Bioinformatics": ["biopython", "scikit-bio", "pysam", "pandas-genomics", "statsmodels", "pybedtools", "matplotlib", "seaborn", "pyteomics", "networkx", "pyfaidx", "lifelines", "ngslib", "bcbio-nextgen", "scanpy", "anndata", "gseapy", "deepchem", "methylpy"], |
| "🔭 Astronomy": ["astropy", "astroquery", "scikit-image", "photutils", "pyephem", "poliastro", "pyvista", "vtk", "healpy", "astroplan", "specutils", "sunpy", "gwpy", "lhorizon", "dustmaps", "reproject", "pymc3", "celerite"], |
| "🗺️ Geospatial": ["geopandas", "rasterio", "shapely", "pyproj", "fiona", "cartopy", "folium", "geopy", "earthpy", "descartes", "osmnx", "geohash", "momepy", "pyvista", "whitebox", "earthengine-api", "gisalchemist", "mapclassify", "spatialpandas"], |
| "💡 IoT": ["pyserial", "paho-mqtt", "RPi.GPIO", "smbus2", "pysnmp", "can-utils", "zeroconf", "asyncio", "bluepy", "adafruit-circuitpython", "iotconnect", "thinger-io", "micropython", "homeassistant", "openhab", "tasmota", "esphome", "nodemcu"], |
| "💻 System": ["psutil", "subprocess32", "watchdog", "inotify", "pywin32", "sh", "delegator.py", "appdirs", "click", "typer", "pathlib", "colorama", "fire", "daemonize", "lsof", "ruamel.yaml", "py-cpuinfo", "platformdirs"] |
| } |
|
|
| selected_libraries = {} |
| for group, libraries in available_libraries.items(): |
| selected_libraries[group] = st.multiselect(f"{group} ", libraries, default=[],key=f"lib_{group}") |
|
|
| selected_groups = st.multiselect("🗂️ Groups", list(available_libraries.keys()), default=[], help="Choose groups to include libraries.") |
|
|
|
|
| with st.expander("⚙️ Pip Settings"): |
| custom_requirements = st.text_input("➕ Custom Libraries:", placeholder="Specific libraries or versions (e.g., requests==2.28.1, numpy>=1.23.0)", help="List specific libraries and versions.") |
| specific_details = st.text_input("ℹ️ Specific Details", placeholder="Special pip install options, edge cases", help="Add specific details for the pip command generation.") |
| pip_options = st.text_input("⚙️ Pip Options", placeholder="Add custom pip options like --index-url or --no-cache", help="Add custom options to your pip command") |
| target_os = st.selectbox("🎯 Target OS", ["Linux 🐧", "Windows 🪟"], index=0, help="Choose the target Operating System.") |
|
|
|
|
|
|
| with col2: |
| |
| prompt_base = st.text_input("📝 Describe the Pip Command:", placeholder="Ex: Install all libraries for web development and data analysis", key="prompt_base") |
| |
| if st.button("✨ Generate Pip Command"): |
| if not prompt_base and not selected_libraries and not selected_groups and not custom_requirements: |
| st.error("⚠️ Please enter a command description or select libraries/groups") |
| return |
|
|
| selected_libs = [] |
| for group in selected_groups: |
| selected_libs.extend(selected_libraries[group]) |
| |
| with st.spinner("⏳ Generating command..."): |
| ai_code = generate_pip_command( |
| prompt_base, |
| model_name, |
| temperature, |
| top_p, |
| top_k, |
| max_tokens, |
| selected_libs, |
| selected_groups, |
| custom_requirements, |
| specific_details, |
| pip_options, |
| target_os |
| ) |
|
|
| if ai_code: |
| st.markdown("### ✅ Generated Command:") |
| st.code(ai_code, language="text") |
|
|
| short_title = prompt_base[:30].strip().replace(" ", "_").lower() |
| files = parse_and_save_code(ai_code, short_title) |
| |
| for key, value in files.items(): |
| st.download_button( |
| label=f"⬇️ Download Command (.{(key)})", |
| data=value["code"], |
| file_name=value["name"], |
| mime="text/plain", |
| ) |
|
|
| else: |
| st.error("❌ Error generating the command. Check the connection with the AI and try again.") |
| |
|
|
| if __name__ == "__main__": |
| main() |
