Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
+from deep_translator import GoogleTranslator
+import torch
+import string
+
+def preprocess_data(text: str) -> str:
+    return text.lower().translate(str.maketrans("", "", string.punctuation)).strip()
+
+SARCASTIC_MODEL_PATH = "helinivan/english-sarcasm-detector"
+SENTIMENT_MODEL_PATH = "lxyuan/distilbert-base-multilingual-cased-sentiments-student"
+
+sarcasm_tokenizer = AutoTokenizer.from_pretrained(SARCASTIC_MODEL_PATH)
+sarcasm_model = AutoModelForSequenceClassification.from_pretrained(SARCASTIC_MODEL_PATH)
+sentiment_analyzer = pipeline("text-classification", model=SENTIMENT_MODEL_PATH, return_all_scores=True)
+
+def analyze_text(user_input):
+    
+    translated_text = GoogleTranslator(source="auto", target="en").translate(user_input)
+    preprocessed_text = preprocess_data(translated_text)
+    tokenized_text = sarcasm_tokenizer([preprocessed_text], padding=True, truncation=True, max_length=256, return_tensors="pt")
+    with torch.no_grad():
+        output = sarcasm_model(**tokenized_text)
+    probs = torch.nn.functional.softmax(output.logits, dim=-1).tolist()[0]
+    sarcasm_confidence = max(probs)
+    is_sarcastic = probs.index(sarcasm_confidence)
+    if is_sarcastic:
+        return f"Sarcastic"
+    else:
+        sentiment_scores = sentiment_analyzer(translated_text)[0]
+        sentiment_result = max(sentiment_scores, key=lambda x: x["score"])
+        return f"{sentiment_result['label'].capitalize()}"
+    
+iface = gr.Interface(
+    fn=analyze_text,
+    inputs=gr.Textbox(label="Enter your text (Tamil)"),
+    outputs=gr.Textbox(label="Analysis Result"),
+    description="Enter text in TAMIL",
+    examples=[
+        ['ஹாய் மாலினி, நான் இதை சொல்லியே ஆகணும், நீ அவ்ளோ அழகு, இங்க உன்னைவிட ஒரு அழகா யாரும் பாத்துருக்க மாட்டாங்க'],
+        ['இது நல்ல இல்ல'],
+        ['நம்ம ஜெயிச்சிட்டோம் மாறா! ']
+    ],
+)
+
+iface.launch(share=True)