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generativeai_srh

https://github.com/sarahkas/generativeai_srh

Science Score: 31.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ○
    .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (2.2%) to scientific vocabulary
Last synced: 11 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: SarahKaS
  • Language: Python
  • Default Branch: main
  • Size: 19.5 KB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created about 2 years ago · Last pushed almost 2 years ago
Metadata Files
Readme Citation

README.md

** GPTdatawinesrh: ** Mini GPT model trained on the "Wine review Dataset" Based on Apoorv Nandan's GPT tutorial (Keras website) and the Generative Deep Learning book from David Foster.

** Citationofthe_day: ** This app gives a daily citation/quote based on people plan today.

Input: response to "What are you doing today?" Ouput: Citation

** LifeCoach_srh: ** The app behaves like a lifecoach chatboot, encouraging healthy, proactive and happy life and based on the current user feelings.

Owner

  • Name: Sarah Kamoun Sdika
  • Login: SarahKaS
  • Kind: user

Citation (Citation_of_the_day_srh.py) 

from typing import List
import os
import getpass
from fastapi import FastAPI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_openai import ChatOpenAI
from langserve import add_routes
# pass_langChain =
# pass_openAI =

# Set environment variables
os.environ['LANGCHAIN_TRACING_V2'] = 'true'
os.environ["LANGCHAIN_API_KEY"] = pass_langChain # getpass.getpass()



# Verify the environment variables are set
print("LANGCHAIN_TRACING_V2:", os.environ['LANGCHAIN_TRACING_V2'])
print("LANGCHAIN_API_KEY:", os.environ['LANGCHAIN_API_KEY'])

os.environ["OPENAI_API_KEY"] = pass_openAI  # getpass.getpass()



# Create prompt template
system_template = ("Give a citation/quote based on people plan today: {What are you doing today?}:"
                   "For example, if people go working today, you can write: Successful people are not gifted; they just work hard, then succeed on purpose. — G.K. Nielson")


prompt_template = ChatPromptTemplate.from_messages([
    ('system', system_template),
    ('system', 'based on people plan today: {What are you doing today?}')
])

# 2. Create model
model = ChatOpenAI()

# Create parser to get only the response
parser = StrOutputParser()

# Chain our steps
chain = prompt_template | model | parser


# App definition
app = FastAPI(
  title="LangChain Server",
  version="1.0",
  description="A simple API server using LangChain's Runnable interfaces",
)

# Adding chain route

add_routes(
    app,
    chain,
    path="/chain",
)

if __name__ == "__main__":
    import uvicorn

    uvicorn.run(app, host="localhost", port=8555)

# Run the app on: http://localhost:8555/chain/playground/

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