Create app.py

app.py

@@ -0,0 +1,93 @@
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import gradio as gr
+
+# ============================================================
+# CONFIG
+# ============================================================
+MODEL_NAME = "bert-base-uncased"
+WEIGHTS_PATH = "bert_sentiment_model.pt"  # Upload this file to your Space
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Define label mappings (same as your training code)
+id2label = {
+    0: "Positive",
+    1: "Negative",
+    2: "Neutral"
+}
+label2id = {v: k for k, v in id2label.items()}
+
+# ============================================================
+# LOAD MODEL AND TOKENIZER
+# ============================================================
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+
+model = AutoModelForSequenceClassification.from_pretrained(
+    MODEL_NAME,
+    num_labels=len(id2label),
+    id2label=id2label,
+    label2id=label2id
+)
+
+# Load fine-tuned weights
+model.load_state_dict(torch.load(WEIGHTS_PATH, map_location=DEVICE))
+model.to(DEVICE)
+model.eval()
+
+# ============================================================
+# INFERENCE FUNCTION
+# ============================================================
+def predict_sentiment(text):
+    if not text.strip():
+        return {"Sentiment": "⚠️ Please enter some text.", "Confidence": 0.0}
+
+    encoding = tokenizer(
+        text,
+        add_special_tokens=True,
+        max_length=256,
+        padding="max_length",
+        truncation=True,
+        return_attention_mask=True,
+        return_tensors="pt"
+    )
+
+    input_ids = encoding["input_ids"].to(DEVICE)
+    attention_mask = encoding["attention_mask"].to(DEVICE)
+
+    with torch.no_grad():
+        outputs = model(input_ids=input_ids, attention_mask=attention_mask)
+        logits = outputs.logits
+        probabilities = torch.softmax(logits, dim=1)
+        predicted_class = torch.argmax(probabilities, dim=1).item()
+        confidence = probabilities[0][predicted_class].item()
+
+    sentiment = id2label[predicted_class]
+    return {"Sentiment": sentiment, "Confidence": round(confidence, 4)}
+
+# ============================================================
+# GRADIO INTERFACE
+# ============================================================
+demo = gr.Interface(
+    fn=predict_sentiment,
+    inputs=gr.Textbox(lines=4, placeholder="Type your review or feedback here...", label="💬 Enter Text"),
+    outputs=[
+        gr.Label(label="Predicted Sentiment"),
+        gr.Number(label="Confidence", precision=4)
+    ],
+    title="BERT Sentiment Analyzer",
+    description="Fine-tuned BERT model for classifying text into Positive, Negative, or Neutral sentiments.",
+    examples=[
+        ["I love this product! It's fantastic!"],
+        ["This is terrible, worst experience ever."],
+        ["It's okay, nothing special."],
+        ["Amazing quality and great service!"],
+        ["Very disappointed with this product."]
+    ],
+    theme="gradio/soft"
+)
+
+# ============================================================
+# LAUNCH
+# ============================================================
+if __name__ == "__main__":
+    demo.launch()