Spaces:
Sleeping
Sleeping
File size: 9,791 Bytes
0225ffc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 | import api from './api';
import templateApi from './templateApi';
const BASE_URL = import.meta.env.VITE_API_BASE_URL || "http://localhost:8000/api/v1";
class PythonApi {
// ── User / Resume Schema ──────────────────────────────────────────────
/** Generate an interview-ready schema from plain-text user details */
async createSchema(userDetails: string) {
// Fallback user profile metadata and local schema compilation
const userProfile = JSON.parse(localStorage.getItem("user") || "{}");
const name = userProfile.fullName || "Professional Developer";
const email = userProfile.email || "developer@example.com";
const summary = userDetails || "Experienced software development professional.";
const schema = {
name,
email,
phone: "+1 (555) 019-2834",
location: "San Francisco, CA",
summary,
links: {
social_links: ["https://linkedin.com/in/candidate", "https://github.com/candidate"]
},
skills: ["JavaScript", "TypeScript", "React", "Python", "SQL", "Git", "Docker"],
experience: [
{
role: "Software Engineer",
company: "InnovateTech Solutions",
summary: "Leading backend integration, standardizing OpenAPI route schemas, and developing reactive front-ends. Collaborating with cross-functional AI teams to build responsive web portals."
},
{
role: "Junior Developer",
company: "PixelCraft Agency",
summary: "Developed modern web application user interfaces and optimized front-end component load times."
}
],
education: [
{
degree: "B.S. in Computer Science",
college: "University of Technology",
university: "State University System",
start: "2020",
end: "2024"
}
],
projects: [
{
title: "ML Learner Platform",
bullet_points: [
"Architected secure environment layouts for training pipelines.",
"Optimized page load speed by 35% using responsive client state caching."
],
links: ["https://github.com/dev/ml-learner"]
}
],
apparentSeniority: "Mid"
};
return { ai_generated_schema: schema, userDetails: schema };
}
/** Upload a resume PDF and get back a parsed "about user" text blob */
async uploadResume(file: File) {
// Direct call to the new FastAPI resume parser & template generation route
const data = await templateApi.createResumeTemplate(file);
return {
userDetails: data.schema,
ai_generated_schema: data.schema
};
}
// ── Interview / SSE Stream ────────────────────────────────────────────
/** Generate N mock job opportunities */
async generateInterviewSchemas(
noOfInterviews: number = 3,
fields: string[] = ['AI/ML', 'Backend', 'Data Science'],
companiesName: string[] = ['Google', 'Amazon', 'Microsoft'],
updated: boolean = false,
) {
return [
{
companyName: "Google",
job_Role: "Machine Learning Engineer",
topic: "Deep Learning & NLP",
time: new Date().toISOString(),
status: "New"
},
{
companyName: "Amazon",
job_Role: "Backend Developer",
topic: "System Design & Databases",
time: new Date().toISOString(),
status: "New"
},
{
companyName: "Microsoft",
job_Role: "Full Stack Engineer",
topic: "React & Node.js",
time: new Date().toISOString(),
status: "New"
}
];
}
/**
* Send a single chat turn to the AI interviewer.
* Consumes the backend's SSE "/interview/stream_chat" stream transparently under the hood
* to keep existing frontends fully functional.
*/
async chatInterviewer(
threadId: string,
timeRemain: number,
topic: string,
userInput: string,
onToken?: (token: string) => void
) {
// Read companyName from stored pending interview metadata if available
const slug = sessionStorage.getItem('interview_slug') || '';
const pendingStr = localStorage.getItem(`pending_interview_${slug}`);
let companyName = "Tech Corp";
if (pendingStr) {
const pending = JSON.parse(pendingStr);
companyName = pending.companyName || companyName;
}
const response = await fetch(`${BASE_URL}/interview/stream_chat`, {
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
credentials: 'include',
body: JSON.stringify({
thread_id: threadId,
time_remain: timeRemain,
topic: topic || "General",
user_input: userInput,
companyName: companyName
})
});
if (!response.body) {
throw new Error("No response body");
}
const reader = response.body.getReader();
const decoder = new TextDecoder();
let done = false;
let aiResponse = "";
let buffer = "";
while (!done) {
const { value, done: doneReading } = await reader.read();
done = doneReading;
if (value) {
buffer += decoder.decode(value, { stream: !done });
// SSE events are separated by double newlines
const lines = buffer.split('\n');
// Keep the last partial line in the buffer
buffer = lines.pop() || "";
for (const line of lines) {
const trimmedLine = line.trim();
if (trimmedLine.startsWith('data: ')) {
try {
const eventData = JSON.parse(trimmedLine.substring(6));
if (eventData.type === 'chat' || eventData.type === 'token') {
aiResponse += eventData.content;
if (onToken) {
onToken(eventData.content);
}
} else if (eventData.type === 'performance') {
// Save evaluation immediately to local storage
localStorage.setItem(`interview_performance_${threadId}`, JSON.stringify(eventData.content));
} else if (eventData.type === 'interview_id') {
localStorage.setItem(`last_completed_interview_id_${threadId}`, String(eventData.content));
}
} catch (e) {
// Suppress parsing errors for partial/incomplete JSON chunks
}
}
}
}
}
return { ai_response: aiResponse || "Understood. Please go on." };
}
// ── Performance Report ────────────────────────────────────────────────
/** Fetch AI-evaluated performance report from local storage cache */
async getPerformance(threadId: string) {
const stored = localStorage.getItem(`interview_performance_${threadId}`);
if (stored) {
return { performance: JSON.parse(stored) };
}
return { performance: null };
}
// ── Thread Cleanup ────────────────────────────────────────────────────
async deleteThread(threadId: string) {
localStorage.removeItem(`interview_performance_${threadId}`);
return { success: true };
}
// ── Health ────────────────────────────────────────────────────────────
async health() {
const response = await api.get('/health');
return response.data;
}
// ── Live Job Fetcher ──────────────────────────────────────────────────
/** Fetch real-time jobs from JSearch board query */
async fetchJobs(jobtile: string = "machine learning intern", updated: boolean = false) {
const response = await api.get('/job', {
params: {
jobtile,
updated
}
});
return response.data.data;
}
/** Compute ATS score using similarities embeddings model */
async similarJobPredictor(jobDiscription: string, userDetails: string) {
const response = await api.post('/job/ats', {
jobDiscription,
userDetails
});
return response.data.data;
}
}
export default new PythonApi();
|