app.py

import gdown
import joblib
import requests
from gensim.models import KeyedVectors

# Function to download files from Google Drive using gdown
def download_file_from_google_drive(file_id, destination):
    gdown.download(f"https://drive.google.com/uc?export=download&id={file_id}", destination, quiet=False)

# Download models from Google Drive using the file IDs
download_file_from_google_drive('1iN8Hp4cJfEZc2s4vBij7MrKksGRWWJpL', 'word2vec_model.model')
download_file_from_google_drive('19-vsY_0Gy5EbPOd9qO4mf8rjKvndJfry', 'log_reg_model.pkl')
download_file_from_google_drive('1XR_jMg2WpZPdzpiB7ZXL-iUGHA0l4S3J', 'nb_model.pkl')

# Load Word2Vec model (make sure it's in binary format)
word2vec_model = KeyedVectors.load_word2vec_format('word2vec_model.model', binary=True)

# Load Logistic Regression model
log_reg_model = joblib.load('log_reg_model.pkl')

# Load Naive Bayes model
nb_model = joblib.load('nb_model.pkl')

# Function for Word2Vec similarity
def get_similarity(word1, word2):
    try:
        similarity_score = word2vec_model.similarity(word1, word2)
        return similarity_score
    except KeyError:
        return "One or both words are not in the vocabulary"

# Function for Logistic Regression prediction
def log_reg_predict(text):
    prediction = log_reg_model.predict([text])
    return prediction.tolist()

# Function for Naive Bayes prediction
def nb_predict(text):
    prediction = nb_model.predict([text])
    return prediction.tolist()

# Gradio interfaces for each model

iface1 = gr.Interface(fn=get_similarity, 
                     inputs=[gr.Textbox(label="Word 1"), gr.Textbox(label="Word 2")], 
                     outputs="text", 
                     live=True, 
                     title="Word2Vec Similarity")

iface2 = gr.Interface(fn=log_reg_predict, 
                     inputs="text", 
                     outputs="text", 
                     live=True, 
                     title="Logistic Regression Prediction")

iface3 = gr.Interface(fn=nb_predict, 
                     inputs="text", 
                     outputs="text", 
                     live=True, 
                     title="Naive Bayes Prediction")

# Launch interfaces
iface1.launch()
iface2.launch()
iface3.launch()