Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+
+model_name = "alperugurcan/nlp-disaster"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+def predict(text):
+    # Tokenize the input text
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    # Get model predictions
+    with torch.no_grad():
+        outputs = model(**inputs)
+    predictions = torch.argmax(outputs.logits, dim=-1)
+    return "Positive" if predictions.item() == 1 else "Negative"
+
+# Create a Gradio interface
+iface = gr.Interface(fn=predict, 
+                     inputs="text", 
+                     outputs="text", 
+                     title="Sentiment Analysis",
+                     description="Enter text to analyze sentiment (Positive/Negative).")
+
+# Launch the app
+iface.launch()