Delete app

app/app.py

@@ -1,66 +0,0 @@
-import streamlit as st
-import transformers
-import torch
-
-# Load the model and tokenizer
-model = transformers.AutoModelForSequenceClassification.from_pretrained("ikoghoemmanuell/finetuned_sentiment_model")
-tokenizer = transformers.AutoTokenizer.from_pretrained("ikoghoemmanuell/finetuned_sentiment_tokenizer")
-
-# Define the function for sentiment analysis
-@st.cache_resource
-def predict_sentiment(text):
-    # Load the pipeline.
-    pipeline = transformers.pipeline("sentiment-analysis")
-
-    # Predict the sentiment.
-    prediction = pipeline(text)
-    sentiment = prediction[0]["label"]
-    score = prediction[0]["score"]
-
-    return sentiment, score
-
-# Setting the page configurations
-st.set_page_config(
-    page_title="Sentiment Analysis App",
-    page_icon=":smile:",
-    layout="wide",
-    initial_sidebar_state="auto",
-)
-
-# Add description and title
-st.write("""
-# How Positive or Negative is your Text?
-Enter some text and we'll tell you if it has a positive, negative, or neutral sentiment!
-""")
-
-
-# Add image
-image = st.image("https://i0.wp.com/thedatascientist.com/wp-content/uploads/2018/10/sentiment-analysis.png", width=400)
-
-# Get user input
-text = st.text_input("Enter some text here:")
-
-# Define the CSS style for the app
-st.markdown(
-"""
-<style>
-body {
-    background-color: #f5f5f5;
-}
-h1 {
-    color: #4e79a7;
-}
-</style>
-""",
-unsafe_allow_html=True
-)
-
-# Show sentiment output
-if text:
-    sentiment, score = predict_sentiment(text)
-    if sentiment == "Positive":
-        st.success(f"The sentiment is {sentiment} with a score of {score*100:.2f}%!")
-    elif sentiment == "Negative":
-        st.error(f"The sentiment is {sentiment} with a score of {score*100:.2f}%!")
-    else:
-        st.warning(f"The sentiment is {sentiment} with a score of {score*100:.2f}%!")