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import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained('distilbert/distilbert-base-uncased')
model = AutoModel.from_pretrained('distilbert/distilbert-base-uncased')
options = [
"Visual", "Auditory", "Kinesthetic",
"Recorded lectures", "Instant messaging (e.g., chat)", "Video calls",
"Clarify concepts", "Improve grades", "Prepare for exams", "Career guidance",
"1-2 hours", "3-5 hours", "6+ hours",
"0 years", " 2 years", "5 years",
"AIML", "Web dev", "App dev",
"Slow and steady", "Moderate", "Fast-paced",
"Explaining with examples", "Interactive discussions", "Problem-solving exercises",
"Yes", "No",
"Trouble with specific concepts", "Time management", "Test anxiety",
"Experienced teacher", "Industry experience", "Should answer question"
]
def calculate_similarity(student_inputs, mentor_inputs):
"""
Check for values in options that are not in student_inputs.
Then append a 'not' to such options and place them at same index as in student_inputs.
Also we need to take care of value shifts after adding 'not' to some options, so use a new list
"""
# student = []
# mentor = []
# for i in range(len(options)):
# if options[i] in student_inputs:
# student.append(options[i])
# else:
# student.append(f"NOT {options[i]}")
# for i in range(len(options)):
# if options[i] in mentor_inputs:
# mentor.append(options[i])
# else:
# mentor.append(f"NOT {options[i]}")
# print(f"Student inputs: {student}")
# print(f"Mentor inputs: {mentor}")
student_tokens = tokenizer(student_inputs, return_tensors="pt", padding=True)
mentor_tokens = tokenizer(mentor_inputs, return_tensors="pt", padding=True)
with torch.no_grad():
student_output = model(**student_tokens).last_hidden_state.mean(dim=1)
mentor_output = model(**mentor_tokens).last_hidden_state.mean(dim=1)
similarity = torch.nn.functional.cosine_similarity(student_output, mentor_output)
print(similarity)
similarity = similarity.mean().item()
print(similarity)
normalized_similarity = (similarity - 0.9)/0.1 * 100
print(normalized_similarity)
return round(normalized_similarity, 2)
with gr.Blocks() as demo:
with gr.Column():
with gr.Row():
gr.Markdown("Student Options")
with gr.Column():
student_inputs = gr.CheckboxGroup(choices=options, label="Student Options")
mentor_inputs = gr.CheckboxGroup(choices=options, label="Mentor Options")
with gr.Row():
btn = gr.Button("Calculate Similarity")
with gr.Row():
output = gr.Number()
btn.click(
fn=calculate_similarity,
inputs=[student_inputs, mentor_inputs],
outputs=output
)
demo.launch(debug=True)
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