app.py

from sentence_transformers import SentenceTransformer
import pickle
import numpy as np
import torch
import gradio as gr

model = SentenceTransformer('paraphrase-MiniLM-L6-v2')

with open("dep_course_title_to_content_embed.pickle", "rb") as handle:
    loaded_map = pickle.load(handle)

dep_name_course_name = list(loaded_map.keys())
deps = list(set([x for (x,y) in dep_name_course_name]))
dep_to_course_name = {}
dep_to_course_embedding = {}

for dep in deps:
    dep_to_course_name[dep] = []
    dep_to_course_embedding[dep] = []

for (dep_name, course_name), embedding in loaded_map.items():
    # print(embedding.shape)
    dep_to_course_name[dep_name].append(course_name)
    dep_to_course_embedding[dep_name].append(np.array(embedding, dtype = np.float32))

cos = torch.nn.CosineSimilarity(dim=1, eps=1e-6)

def give_best_match(query, Department):
    if not Department:
        Department = deps
    course_titles = []
    course_content_embeddings = []
    for dep in Department:
        course_titles +=  dep_to_course_name[dep]
        course_content_embeddings += dep_to_course_embedding[dep]
    course_content_embeddings =  np.stack(course_content_embeddings)
    embed = model.encode(query)
    result = cos(torch.from_numpy(course_content_embeddings),torch.from_numpy(embed))
    indices = reversed(np.argsort(result))
    predictions = {course_titles[i] : float(result[i]) for i in indices}
    return predictions

demo = gr.Interface(fn = give_best_match,
                    inputs=[ 
                        gr.Textbox(label="Describe the course",lines = 5, placeholder = "Type anything related to course/s\n\nTitle, Topics/Sub Topics, Refernce books, Questions asked in exams or some random fun stuff."),
                         gr.CheckboxGroup(deps),
                        ],
                    outputs=gr.Label(label = "Most Relevant Courses", num_top_classes=5)
                    )
demo.launch()