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app.py

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+from transformers import TextClassificationPipeline, AutoTokenizer, AutoModelForSequenceClassification
+import gradio as gr
+
+# def get_model(model_name='Overfit-GM/temp_dist'):
+#     id2label = {0: 'INSULT', 1: 'OTHER',
+#                 2: 'PROFANITY', 3: 'RACIST', 4: 'SEXIST'}
+#     label2id = {v: k for k, v in id2label.items()}
+#     tokenizer = AutoTokenizer.from_pretrained(model_name)
+#     model = AutoModelForSequenceClassification.from_pretrained(model_name,
+#                                                                problem_type="single_label_classification",
+#                                                                id2label=id2label,
+#                                                                label2id=label2id,
+#                                                                num_labels=5,
+#                                                                output_hidden_states=False,
+#                                                                )
+#     return model, tokenizer
+
+
+models = [
+    "Overfit-GM/temp_dist",
+    "deprem-ml/deprem_bert_128k"
+]
+
+model_box=[
+    gr.load(models[0], src='models'),
+    gr.load(models[1], src='models'),
+]
+
+def sentiment_analysis(text, model_choice):
+    
+    a_variable = model_box[model_choice]
+    output = a_variable(text)
+    return output
+
+with gr.Blocks() as demo:
+    gr.HTML("""<h1 style="font-weight:600;font-size:50;margin-top:4px;margin-bottom:4px;text-align:center;">No Offense Classifier</h1></div>""")
+    with gr.Row():
+        with gr.Column():
+            model_choice = gr.Dropdown(label="Select Model", choices=[m for m in models], type="index", interactive=True)
+            input_text = gr.Textbox(label="Input", placeholder="senin ben amk")
+            the_button = gr.Button(label="Run")
+        with gr.Column():
+            output_window = gr.Label(num_top_classes=5)
+
+    the_button.click(sentiment_analysis, inputs=[input_text, model_choice], outputs=[output_window])
+    examples = gr.Examples(examples=["bu adamların ülkesine dönmesi lazım", "adam olsan oraya gitmezdin"],
+                           inputs=[input_text])
+
+demo.launch()

requirements.txt

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+torch
+transformers
+numpy
+gradio