Create appy.py

appy.py

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+import streamlit as st
+from transformers import pipeline
+from googletrans import Translator
+
+# Initialize translation and classification pipelines
+translator = Translator()
+emotion_classifier = pipeline("text-classification", model="arpanghoshal/EmoRoBERTa")
+
+
+def classify_emotion(text, src_lang="auto", target_lang="en"):
+    """
+    Classifies emotion in text after translating to English (if needed).
+
+    Args:
+        text: Input text sentence in any language.
+        src_lang: Source language of the text (default: "auto").
+        target_lang: Target language for translation (default: "en").
+
+    Returns:
+        A dictionary containing the predicted emotion and its probability.
+    """
+    # Translate to English if necessary
+    if src_lang != target_lang:
+        translated_text = translator.translate(text, dest=target_lang).text
+    else:
+        translated_text = text
+
+    # Classify emotion using EmoRoBERTa
+    predictions = emotion_classifier(translated_text)
+    return predictions[0]
+
+
+def split_sentences_with_conjunctions(paragraph):
+    """
+    Splits a paragraph into sentences considering conjunctions and punctuation.
+
+    Args:
+        paragraph: The input paragraph as a string.
+
+    Returns:
+        A list of individual sentences.
+    """
+    sentences = []
+    current_sentence = ""
+    conjunctions = ["and", "but", "or", "for", "nor", "so", "yet"]  # Common conjunctions
+
+    for word in paragraph.split():
+        current_sentence += word + " "
+        if word.lower() in conjunctions or word.endswith(".") or word.endswith("?"):
+            sentences.append(current_sentence.strip())
+            current_sentence = ""
+
+    # Add any remaining sentence fragment
+    if current_sentence.strip():
+        sentences.append(current_sentence.strip())
+
+    return sentences
+paragraph = st.text_area("Enter your input: ")
+
+# Split paragraph into sentences considering conjunctions
+sentences = split_sentences_with_conjunctions(paragraph)
+
+# Classify emotion for each sentence
+emotion_results = []
+for sentence in sentences:
+    if sentence.strip():  # Check if sentence is not empty after stripping
+        emotion_result = classify_emotion(sentence)
+        emotion_results.append(emotion_result)
+
+# Print emotions for each sentence
+for sentence, emotion_result in zip(sentences, emotion_results):
+    print(f"Sentence: {sentence} | Emotion: {emotion_result['label']} ({emotion_result['score']:.2f})")