Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load models and tokenizers
+sarcasm_tokenizer = AutoTokenizer.from_pretrained("microsoft/deberta-v3-base")
+sarcasm_model = AutoModelForSequenceClassification.from_pretrained("dnzblgn/Sarcasm-Detection-Customer-Reviews")
+sentiment_tokenizer = AutoTokenizer.from_pretrained("facebook/roberta-base")
+sentiment_model = AutoModelForSequenceClassification.from_pretrained("dnzblgn/Sentiment-Analysis-Customer-Reviews")
+
+def process_text_pipeline(user_input):
+    sentences = user_input.split("\n")
+    results = []
+    for sentence in sentences:
+        # Sentiment analysis
+        sentiment_inputs = sentiment_tokenizer(sentence, return_tensors="pt", truncation=True, padding=True, max_length=512)
+        with torch.no_grad():
+            sentiment_outputs = sentiment_model(**sentiment_inputs)
+            sentiment_logits = sentiment_outputs.logits
+            sentiment_class = torch.argmax(sentiment_logits, dim=-1).item()
+            sentiment = "Positive" if sentiment_class == 0 else "Negative"
+        
+        # Sarcasm detection for positive sentences
+        if sentiment == "Positive":
+            sarcasm_inputs = sarcasm_tokenizer(sentence, return_tensors="pt", truncation=True, padding=True, max_length=512)
+            with torch.no_grad():
+                sarcasm_outputs = sarcasm_model(**sarcasm_inputs)
+                sarcasm_logits = sarcasm_outputs.logits
+                sarcasm_class = torch.argmax(sarcasm_logits, dim=-1).item()
+                if sarcasm_class == 1:  # Sarcasm detected
+                    sentiment = "Negative (Sarcasm detected)"
+        
+        results.append(f"{sentence}: {sentiment}")
+    return "\n".join(results)
+
+# Gradio UI
+interface = gr.Interface(
+    fn=process_text_pipeline,
+    inputs=gr.Textbox(lines=10, placeholder="Enter one or more sentences, each on a new line."),
+    outputs="text",
+    title="Sarcasm Detection for Customer Reviews",
+    description="This web app analyzes the sentiment of customer reviews and detects sarcasm for positive reviews.",
+)
+
+# Run interface
+if __name__ == "__main__":
+    interface.launch()