datasciencemonkey

import random
from utils import get_country_data_and_corr
class WriteEssayAfterParsingUserQuery(dspy.Module):
    def __init__(self):
        super().__init__()
        self.country = dspy.Predict(ExtractCountry)
        self.essay_writer = dspy.ChainOfThought(EssayOnePass)
    
    def forward(self, question, constraint, persona):
        with dspy.settings.context(lm=qwen2): # Call QWEN2 for extracting country name

datasciencemonkey

# %%
from huggingface_hub import login
from dotenv import load_dotenv

load_dotenv("/notebooks/.env")
import os

os.environ["TOKENIZERS_PARALLELISM"] = "false"

login(token=os.getenv("HUGGINGFACE_TOKEN"))

datasciencemonkey

# %%
# this is run from /notebooks on paperspace
from huggingface_hub import login
from dotenv import load_dotenv

load_dotenv("/notebooks/.env")
import os
os.environ["TOKENIZERS_PARALLELISM"]="false"
login(token=os.getenv("HUGGINGFACE_TOKEN"))

datasciencemonkey

from flair.embeddings import (
    FlairEmbeddings,
    TransformerWordEmbeddings,
    StackedEmbeddings,
)
from flair.data import Sentence
from flair.embeddings import DocumentPoolEmbeddings
from sklearn.metrics.pairwise import cosine_similarity

# init Flair embeddings

datasciencemonkey

# import libraries
import os
import swat
import pandas as pd
import toml

with open('/tmp/config.toml','r') as f:
    server_config = toml.load(f)

cas_settings = server_config.get('cas35')

datasciencemonkey

CarouselSlider(
                options: CarouselOptions(
                    height: _deviceHeight * 0.1,
                    viewportFraction: 1.0,
                    autoPlay: true,
                    enlargeCenterPage: true,
                    aspectRatio: 2.0,
                    onPageChanged: (index, reason) {
                      setState(() {
                        _current = index;

datasciencemonkey

class NetworkHelper {
  NetworkHelper(this.url);

  final String url;
  final Xml2Json xml2json = new Xml2Json();

  Future getData() async {
    print(url);
    http.Response response =
        await http.get(url, headers: {'Accept': 'application/json'});

datasciencemonkey

void updateUI(dynamic recommendationJSON) {
    setState(() {
      if (recommendationJSON == null) {
        recommendationText = '';
        return;
      }
      recommendationText =
          recommendationJSON['events'][1]['event']['Recommendation'];
      print(recommendationText);
    });

datasciencemonkey

import 'networking.dart';
import '../constants/esp_settings.dart';

const esp_rec_model_url =
    '$hostname:$http_port/SASESP/events/Recommendation_Engine/cq1/Final_Recommendation';

class RecommendationModel {
  Future<dynamic> getRecommendation(String id) async {
    NetworkHelper networkHelper =
        NetworkHelper('$esp_rec_model_url?filter=in(cust_id,$id)');