file uploads

app.py

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+import os
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+
+# Use a BERT model PRE-FINETUNED for sentiment analysis
+# BERT fine-tuned on SST-2 dataset
+model_name = "textattack/bert-base-uncased-SST-2"
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Device configuration
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = model.to(device)
+
+
+def analyze_sentiment(text):
+    # Tokenize input
+    inputs = tokenizer(text,
+                       return_tensors="pt",
+                       truncation=True,
+                       padding=True,
+                       max_length=512).to(device)
+
+    # Get predictions
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = torch.softmax(outputs.logits, dim=1)
+
+    # Process results
+    labels = ["NEGATIVE", "POSITIVE"]
+    confidence, pred_class = torch.max(probs, dim=1)
+
+    return {
+        "text": text,
+        "prediction": labels[pred_class],
+        "confidence": confidence.item(),
+        "probabilities": dict(zip(labels, probs.tolist()[0]))
+    }
+
+
+# Example usage
+text = "Hugging Face is amazing!"
+result = analyze_sentiment(text)
+
+print(f"\nInput: {result['text']}")
+print(f"Model: {model_name}")
+print(f"Prediction: {result['prediction']} ({result['confidence']:.2%})")
+print("Probabilities:")
+for label, prob in result['probabilities'].items():
+    print(f"  {label}: {prob:.4f}")
+
+# Additional tokenization info
+print("\nTokenization details:")
+tokens = tokenizer.tokenize(text)
+print(f"Tokens: {tokens}")
+print(f"Token IDs: {tokenizer.encode(text)}")