app.py

import gradio as gr
import pandas as pd
import numpy as np
import pickle
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity

model = SentenceTransformer('paraphrase-distilroberta-base-v1')

# Load the dataset
data = pd.read_csv('data/Recommender_data.csv')

# Load the precomputed embeddings
with open('data/Model/tool_embeddings.pkl', 'rb') as f:
    tool_embeddings = pickle.load(f)

# # Load the model from the file using pickle
# with open('data/Model/model.pkl', 'rb') as f:
#     model = pickle.load(f)


# def get_sentiment(input_text):
#   return sentiment(input_text)
# Define the recommendation function
def recommend(product_idea, model=model):#, model=model
    # Load the pre-trained model
    #model = SentenceTransformer('paraphrase-distilroberta-base-v1')

    # Compute the embedding for the product idea
    product_embedding = model.encode([product_idea])

    # Compute the cosine similarity between the product idea vector and all tool vectors
    cosine_sim_product = cosine_similarity(product_embedding, tool_embeddings)

    # Sort the results in descending order and get the indices of the most similar tools
    indices = np.argsort(cosine_sim_product)[0][::-1]

    # Return the top 5 most similar tools
    recommendations = {data.iloc[indices[i]]['Tools']: {"Description":data.iloc[indices[i]]['Description'],"URL":data.iloc[indices[i]]['URL'],"Logo":data.iloc[indices[i]]['Logo']} for i in range(4)}

    return recommendations

iface = gr.Interface(fn = recommend, 
                     inputs = "text", 
                     outputs = ['text'],
                     title = 'OHA', 
                     description="Get OHA Recomandation for the given input")
                     
iface.launch(inline = False)