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app.py

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+import gradio as gr
+from transformers import pipeline
+from  transformers import AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
+
+# Load the text classification pipeline from Hugging Face
+classifier = pipeline("text-classification", model="./clickbait-classifier-model-90", tokenizer=tokenizer)
+
+
+def classify_text(text):
+    prediction = classifier(text)[0]
+    clickbait_label = "LABEL_1"  # Assuming LABEL_1 corresponds to clickbait
+    non_clickbait_label = "LABEL_0"  # Assuming LABEL_0 corresponds to non-clickbait
+
+    predicted_label = prediction["label"]
+    predicted_score = prediction["score"] * 100
+
+    clickbait_score = predicted_score if predicted_label == clickbait_label else 0
+    non_clickbait_score = predicted_score if predicted_label == non_clickbait_label else 0
+
+    return clickbait_score, non_clickbait_score
+
+# Example clickbait headline
+clickbait_example = ["You'll Never Believe What This Dog Did Next!"]
+
+# Example non-clickbait headline
+non_clickbait_example = ["Local School Board Approves New Budget"]
+
+# Combine into a list of examples
+examples = [clickbait_example, non_clickbait_example]
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=classify_text,
+    inputs=[gr.Textbox(lines=2, placeholder="Enter a text headline...")],
+    outputs=[
+        gr.Slider(label="Clickbait", minimum=0, maximum=100, step=1),
+        gr.Slider(label="Non-Clickbait", minimum=0, maximum=100, step=1),
+    ],
+    title="Clickbait Detector",
+    examples=examples,
+)
+
+# Launch the interface
+iface.launch()

config.json

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+{
+  "_name_or_path": "bert-base-cased",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.35.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 28996
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:92a922d579ec96ac374aa6289ff32e226def1858593c658eef584e81becbf78f
+size 433270768

requirements.txt

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+gradio
+transformers
+torch