tweet-sentiment / app.py
efecelik's picture
Create app.py
b3a6447 verified
import pickle
import gradio as gr
import numpy as np
from gensim.models import Word2Vec
# Load the logistic regression model
with open('llogistic_model.pkl', 'rb') as file:
model = pickle.load(file)
# Load the Word2Vec model
word2vec_model = Word2Vec.load('word2vec_model.model')
def sentence_vector(tokens, model):
"""Calculate the sentence vector by averaging word vectors."""
valid_words = [word for word in tokens if word in model.wv]
if valid_words:
return np.mean(model.wv[valid_words], axis=0)
else:
return np.zeros(model.vector_size)
def classify_comment(comment):
"""Classify the sentiment of a comment as bearish, bullish, or neutral."""
try:
# Tokenize the comment
tokens = comment.lower().split()
# Generate sentence vector using Word2Vec
processed_comment = sentence_vector(tokens, word2vec_model).reshape(1, -1)
# Predict sentiment
prediction = model.predict(processed_comment)[0]
# Map prediction to labels (ensure the model output aligns with these labels)
sentiment_map = {0: "neutral", 1: "bullish", 2: "bearish"}
sentiment = sentiment_map.get(prediction, "unknown")
return sentiment
except Exception as e:
return f"Error: {str(e)}"
# Create Gradio interface
interface = gr.Interface(
fn=classify_comment,
inputs=gr.Textbox(label="Enter your comment (e.g., about BTC or stock markets):"),
outputs=gr.Label(label="Sentiment"),
title="BTC Sentiment Analyzer",
description="Predict whether a comment is bullish, bearish, or neutral using a logistic regression model."
)
# Launch the Gradio interface
interface.launch()