Delete analyzer.py

analyzer.py

@@ -1,64 +0,0 @@
-import streamlit as st
-from transformers import pipeline
-
-# Initialize sentiment analysis pipeline
-sentiment_pipeline = pipeline("sentiment-analysis")
-
-# Use a sarcasm detection model
-sarcasm_model_name = "mrm8488/t5-base-finetuned-sarcasm-twitter"  # Correct model name for sarcasm detection
-
-# Create the sarcasm detection pipeline
-sarcasm_pipeline = pipeline("text2text-generation", model=sarcasm_model_name)
-
-def classify_sentence(sentence):
-    # Detect sarcasm
-    sarcasm_result = sarcasm_pipeline(sentence)[0]['generated_text']
-    is_sarcastic = sarcasm_result.strip().lower() == 'true'
-
-    # Detect sentiment
-    sentiment_result = sentiment_pipeline(sentence)[0]
-    sentiment_label = sentiment_result['label']
-    sentiment_score = sentiment_result['score']
-
-    # Determine sentiment
-    if sentiment_label == "NEGATIVE":
-        sentiment = "negative"
-    elif sentiment_label == "POSITIVE":
-        sentiment = "positive"
-    else:
-        sentiment = "neutral"
-
-    # Handle sarcasm
-    if is_sarcastic:
-        sentiment += " (sarcastic)"
-
-    return sentiment
-
-# Streamlit app
-st.title("Sentence Analyzer")
-
-# User input
-sentence = st.text_input("Enter a sentence:", "")
-
-if st.button("Analyze"):
-    if sentence:
-        classification = classify_sentence(sentence)
-        st.write(f"Sentence: {sentence}")
-        st.write(f"Classification: {classification}")
-    else:
-        st.write("Please enter a sentence to analyze.")
-
-# Example sentences
-st.subheader("Example Sentences")
-example_sentences = [
-    "they are so beautiful",
-    "This is the best day of my life.",
-    "I'm not happy with your work.",
-    "Yeah,you are not a good person!"
-]
-
-if st.button("Analyze Example Sentences"):
-    for sentence in example_sentences:
-        st.write(f"Sentence: {sentence}")
-        st.write(f"Classification: {classify_sentence(sentence)}")
-        st.write()