Spaces:
Runtime error
Runtime error
File size: 2,496 Bytes
3e6e3a1 65fceff 3e6e3a1 65fceff 3e6e3a1 912706a 65fceff 3e6e3a1 65fceff 912706a 3e6e3a1 65fceff 3e6e3a1 65fceff 3e6e3a1 |
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 |
import gradio as gr
import numpy as np
from transformers import pipeline
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
import PyPDF2
# Load local models for inference
stt_model = pipeline("automatic-speech-recognition", model="openai/whisper-base")
conversation_model = pipeline("text-generation", model="facebook/blenderbot-400M-distill")
# Load a pre-trained model for vector embeddings
embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
# Parse PDF and create resume content
def parse_resume(pdf):
"""Extract text from an uploaded PDF file."""
reader = PyPDF2.PdfReader(pdf)
text = "\n".join(page.extract_text() for page in reader.pages if page.extract_text())
sections = {"Resume Content": text}
return sections
# Process resume and generate embeddings
def process_resume(pdf):
resume_content = parse_resume(pdf)
resume_embeddings = {
section: embedding_model.encode(content) for section, content in resume_content.items()
}
return resume_embeddings
# Generate a conversation response
def generate_conversation_response(user_input):
prompt = f"The user said: {user_input}. Respond appropriately as a recruiter."
response = conversation_model(prompt, max_length=100, num_return_sequences=1)
return response[0]["generated_text"]
# Generate question from user response
def generate_question(user_input, resume_embeddings):
"""Find the most relevant section in the resume and generate a question."""
user_embedding = embedding_model.encode(user_input)
similarities = {
section: cosine_similarity([user_embedding], [embedding])[0][0]
for section, embedding in resume_embeddings.items()
}
most_relevant_section = max(similarities, key=similarities.get)
return f"Based on your experience in {most_relevant_section}, can you elaborate more?"
# Gradio interface
def mock_interview(audio, pdf):
resume_embeddings = process_resume(pdf)
transcription = stt_model(audio)["text"]
question = generate_question(transcription, resume_embeddings)
return transcription, question
interface = gr.Interface(
fn=mock_interview,
inputs=[gr.Audio(source="microphone", type="filepath"), gr.File(label="Upload Resume (PDF)")],
outputs=["text", "text"],
title="Mock Interview AI",
description="Upload your resume and answer questions in a mock interview."
)
if __name__ == "__main__":
interface.launch()
|