Update app.py

app.py

@@ -1,51 +1,55 @@
-import torch
-from transformers import AutoTokenizer ,AutoModelForSequenceClassification
-import gradio as gr
-
-
-model_name = "Anurag3703/bert-spam-classifier"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSequenceClassification.from_pretrained(model_name)
-
-def classify_text(text):
-
-    inputs = tokenizer(text, return_tensors="pt", truncation=True,padding=True,max_lennght=512)
-
-    # Get predictions
-    with torch.no_grad():
-        outputs = model(**inputs)
-        predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
-
-    # Get probabilities
-    ham_prob = predictions[0][0].item()
-    spam_prob = predictions[0][1].item()
-
-    return {
-        "Not Spam (Ham)": ham_prob,
-        "Spam": spam_prob
-    }
-
-# Create Gradio interface
-demo = gr.Interface(
-    fn=classify_text,
-    inputs=gr.Textbox(
-        lines=3,
-        placeholder="Enter a message to classify...",
-        label="Input Text"
-    ),
-    outputs=gr.Label(num_top_classes=2, label="Classification Results"),
-    title="BERT Spam Classifier",
-    description="Enter a text message to check if it's spam or not spam.",
-    examples=[
-        ["Win a free iPhone now! Click here!"],
-        ["Hey, can we meet for coffee tomorrow?"],
-        ["URGENT: Your account will be closed. Verify now!"],
-        ["Thanks for the meeting today, let's catch up next week."]
-    ],
-    theme="soft"
-)
-
-if __name__ == "__main__":
-    demo.launch()
-
-
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import os
+
+# Load model and tokenizer with authentication
+model_name = "Anurag3703/bert-spam-classifier"
+token = os.environ.get("HF_TOKEN")  # Get token from environment
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, token=token)
+model = AutoModelForSequenceClassification.from_pretrained(model_name, token=token)
+
+
+def classify_text(text):
+    """Classify text as spam or not spam"""
+    # Tokenize input
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+
+    # Get predictions
+    with torch.no_grad():
+        outputs = model(**inputs)
+        predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+
+    # Get probabilities
+    ham_prob = predictions[0][0].item()
+    spam_prob = predictions[0][1].item()
+
+    return {
+        "Not Spam (Ham)": ham_prob,
+        "Spam": spam_prob
+    }
+
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=classify_text,
+    inputs=gr.Textbox(
+        lines=3,
+        placeholder="Enter a message to classify...",
+        label="Input Text"
+    ),
+    outputs=gr.Label(num_top_classes=2, label="Classification Results"),
+    title="BERT Spam Classifier",
+    description="Enter a text message to check if it's spam or not spam.",
+    examples=[
+        ["Win a free iPhone now! Click here!"],
+        ["Hey, can we meet for coffee tomorrow?"],
+        ["URGENT: Your account will be closed. Verify now!"],
+        ["Thanks for the meeting today, let's catch up next week."]
+    ],
+    theme="soft"
+)
+
+if __name__ == "__main__":
+    demo.launch()