Added Apllication File

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model
+model_name = "textattack/bert-base-uncased-SST-2"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+
+def analyze_sentiment(text):
+    inputs = tokenizer(text, return_tensors="pt",
+                       truncation=True, padding=True)
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = torch.softmax(outputs.logits, dim=1)
+
+    return {
+        "POSITIVE": float(probs[0][1]),
+        "NEGATIVE": float(probs[0][0])
+    }
+
+
+# Create interface
+demo = gr.Interface(
+    fn=analyze_sentiment,
+    inputs=gr.Textbox(label="Input text", placeholder="Enter text here..."),
+    outputs=gr.Label(label="Sentiment Probabilities"),
+    examples=[
+        ["I love this product!"],
+        ["This was terrible experience"],
+        ["It was okay, nothing special"]
+    ],
+    title="BERT Sentiment Analysis",
+    description="Predicts sentiment using BERT model fine-tuned on SST-2 dataset"
+)
+
+demo.launch()

requirements.txt

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+torch
+transformers
+gradio