Talk to your DataFrames with PandasAI

python -m venv pdai #create the venv | python3 if you are on linux

pdai\Scripts\activate #activate venv (windows)
source pdai/bin/activate #(linux)

pip install pandasai==2.0.37 #pip install -r requirements.txt #all at once

version: '3'
services:
  ollama:
    image: ollama/ollama
    container_name: ollama
    ports:
      - "11434:11434"
    volumes:
      - ollama_data:/root/.ollama
    command: ollama run tinyllama #or any other model
    #command: tail -f /dev/null    

volumes:
  ollama_data:

#docker run -d --name python_container_for_dev -p 8502:8501 pythonaicontainer tail -f /dev/null
docker exec -it ollama /bin/bash
ollama run tinyllama

#linux
curl http://localhost:11434/api/generate -d '{
  "model": "tinyllama",
  "prompt": "Why is the sky blue?",
  "stream": false
}'

#Windows
curl.exe -X POST http://192.168.3.200:11434/api/generate -d "{\"model\":\"tinyllama\",\"prompt\":\"Who are you?\",\"stream\":false}"

import pandas as pd #https://github.com/thepycoach/pandasai-tutorial/blob/main/PandasAI-Tutorial.ipynb
from pandasai import PandasAI
from pandasai.llm.openai import OpenAI

df = pd.read_csv("supermarket_sales.csv")
df = df[['Gender', 'Product line', 'Total']]

OPENAI_API_KEY = "your-api-key"
llm = OpenAI(api_token=OPENAI_API_KEY)

pandas_ai = PandasAI(llm)

pandas_ai.run(df, prompt="Which products are in Product Line")
pandas_ai.run(df, prompt="Plot a barplot that shows the total spent by each gender")

FROM python:3.10-slim

# Install git
RUN apt-get update && apt-get install -y git nano

# Set up the working directory
WORKDIR /app

docker build --no-cache -t pythonaicontainer . #> build_log.txt 2>&1

version: '3'

services:
  ollama:
    image: ollama/ollama
    container_name: ollamapython
    ports:
      - "11434:11434"
    volumes:
      - ollama_data:/root/.ollama
    #command: ollama run tinyllama

  dev-pythonaicontainer:
    image: pythonaicontainer
    container_name: pythonaicontainer
    volumes:
      - ai_pythonaicontainer:/app
    working_dir: /app
    command: tail -f /dev/null
    ports:
      - "8503:8501"

volumes:
  ollama_data:
  ai_pythonaicontainer:

pip install langchain

os.environ['LAMBDAPROMPT_BACKEND'] = 'StarCoder'
os.environ['SKETCH_USE_REMOTE_LAMBDAPROMPT'] = 'False'
os.environ['HF_ACCESS_TOKEN'] = 'your_hugging_face_token'

pip install duckdb

version: '3.6'

services:
  api:
    # See https://localai.io/basics/getting_started/#container-images for
    # a list of available container images (or build your own with the provided Dockerfile)
    # Available images with CUDA, ROCm, SYCL
    # Image list (quay.io): https://quay.io/repository/go-skynet/local-ai?tab=tags
    # Image list (dockerhub): https://hub.docker.com/r/localai/localai
    image: quay.io/go-skynet/local-ai:master-ffmpeg-core
    build:
      context: .
      dockerfile: Dockerfile
      args:
      - IMAGE_TYPE=core
      - BASE_IMAGE=ubuntu:22.04
    ports:
      - 8080:8080
    env_file:
      - .env
    environment:
      - MODELS_PATH=/models
    #  - DEBUG=true
    volumes:
      - ./models:/models:cached
      - ./images/:/tmp/generated/images/
    command:
    # Here we can specify a list of models to run (see quickstart https://localai.io/basics/getting_started/#running-models )
    # or an URL pointing to a YAML configuration file, for example:
    # - https://gist.githubusercontent.com/mudler/ad601a0488b497b69ec549150d9edd18/raw/a8a8869ef1bb7e3830bf5c0bae29a0cce991ff8d/phi-2.yaml
    - phi-2