maybe build on pc?

finalise_from_context/Cargo.toml

@@ -0,0 +1,12 @@
+[package]
+name = "unnamed_chatgpt_project"
+version = "0.1.0"
+edition = "2021"
+
+# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
+
+[dependencies]
+serde = { version = "1.0", features = ["derive"] }
+serde_json = "1.0.91"
+rfd = "0.10.0"
+rust-bert = "0.20.0"

finalise_from_context/finalise_from_context.iml

@@ -0,0 +1,12 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="RUST_MODULE" version="4">
+  <component name="NewModuleRootManager" inherit-compiler-output="true">
+    <exclude-output />
+    <content url="file://$MODULE_DIR$">
+      <sourceFolder url="file://$MODULE_DIR$/src" isTestSource="false" />
+      <excludeFolder url="file://$MODULE_DIR$/target" />
+    </content>
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

finalise_from_context/src/main.rs

@@ -0,0 +1,98 @@
+use std::fs::File;
+use std::io;
+use std::io::{BufReader, Read, Write};
+
+use serde::{Serialize, Deserialize};
+
+use rust_bert::bert::{BertConfigResources, BertModelResources, BertVocabResources};
+use rust_bert::pipelines::common::ModelType;
+use rust_bert::pipelines::question_answering::Answer;
+use rust_bert::pipelines::question_answering::{
+    QaInput, QuestionAnsweringConfig, QuestionAnsweringModel,
+};
+use rust_bert::resources::RemoteResource;
+
+
+
+#[derive(Deserialize, Serialize)]
+struct Human {
+    firstName: String,
+    lastName: String,
+    gender: String,
+    age: String,
+    country: String,
+    job: String,
+    bio: String,
+}
+
+fn main() {
+    //load in the file with bio's and names
+    let current_path = std::env::current_dir().unwrap();
+    let res = rfd::FileDialog::new().set_directory(&current_path).pick_file().unwrap();
+    let mut file = File::open(res.as_path()).unwrap();
+    let mut json_string:String = String::new();
+    file.read_to_string(&mut json_string).unwrap();
+    let mut Humans: Vec<Human> = serde_json::from_str(&json_string).unwrap();
+    //prep final file
+    let save_res = rfd::FileDialog::new().set_directory(&current_path).save_file().unwrap();
+    let mut i = 0;
+    let mut l = &Humans.len().clone();
+    println!("there are {} humans to process", l - i);
+
+    for mut human in &mut Humans {
+        let (gender, age, country, job) = getHumanFromContext(human.bio.clone(), human.firstName.clone());
+        human.gender = gender;
+        human.age = age;
+        human.country = country;
+        human.job = job;
+        println!("just did {} at index {}", human.firstName.clone(), i);
+        println!("There are {} humans left to process", l - i);
+        i = i+1;
+    }
+
+    let serialized: String = serde_json::to_string(&Humans).unwrap();
+    let mut file = File::create(save_res.as_path()).unwrap();
+    file.write_all(serialized.as_bytes()).expect("oopsie");
+}
+
+
+fn getHumanFromContext(context: String, firstName: String) -> (String, String, String, String) {
+    //TODO use the other ai to get answers from a given context
+    let bertconfig = QuestionAnsweringConfig::new(
+        ModelType::Bert,
+        RemoteResource::from_pretrained(BertModelResources::BERT_QA),
+        RemoteResource::from_pretrained(BertConfigResources::BERT_QA),
+        RemoteResource::from_pretrained(BertVocabResources::BERT_QA),
+        None, //merges resource only relevant with ModelType::Roberta
+        false,
+        false,
+        None,
+    );
+    let mut model = QuestionAnsweringModel::new(bertconfig).unwrap();
+    let mut genderQuestion = QaInput {
+        question: format!("What is {}'s gender?", firstName),
+        context: context.clone()
+    };
+    let mut ageQuestion = QaInput {
+        question: format!("What is {}'s age?", firstName),
+        context: context.clone()
+    };
+    let mut countryQuestion = QaInput {
+        question: format!("Where does {} live?", firstName),
+        context: context.clone()
+    };
+    let mut jobQuestion = QaInput {
+        question: format!("What is {}'s job?", firstName),
+        context: context.clone()
+    };
+
+    let mut answers = model.predict(&[genderQuestion, ageQuestion, countryQuestion, jobQuestion], 1, 32);
+    let mut looper = answers.iter();
+    let mut gender = looper.next().unwrap().first().unwrap().answer.clone();
+    let mut age = looper.next().unwrap().first().unwrap().answer.clone();
+    let mut country= looper.next().unwrap().first().unwrap().answer.clone();
+    let mut job = looper.next().unwrap().first().unwrap().answer.clone();
+
+
+    return (gender, age, country, job)
+}