Create app.py

app.py

@@ -0,0 +1,64 @@
+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()