First GUI Update

app.py

@@ -1,7 +1,39 @@
 import gradio as gr
+from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
 
-def greet(name):
-    return "Hello " + name + "!!"
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+models = ["bert-base-uncased", "model2"]
+datasets = ["vedantgaur/GPTOutputs-MWP", "dataset2"]
+
+# Mapping of user-selected model and dataset to actual model name on Hugging Face
+model_mapping = {
+    ("bert-base-uncased", "vedantgaur/GPTOutputs-MWP"): "SkwarczynskiP/bert-base-uncased-finetuned-vedantgaur",
+    ("bert-base-uncased", "dataset2"): "finetuned_model1_on_dataset2",
+    ("model2", "vedantgaur/GPTOutputs-MWP"): "finetuned_model2_on_dataset1",
+    ("model2", "dataset2"): "finetuned_model2_on_dataset2",
+}
+
+def detect_ai_generated_text(model: str, dataset: str, text: str) -> str:
+    # Get the fine-tuned model using mapping
+    finetuned_model = model_mapping.get((model, dataset))
+
+    # Load the specific fine-tuned model
+    tokenizer = AutoTokenizer.from_pretrained(finetuned_model)
+    model = AutoModelForSequenceClassification.from_pretrained(finetuned_model)
+
+    # Classify the input based on the fine-tuned model
+    classifier = pipeline('text-classification', model=model, tokenizer=tokenizer)
+    result = classifier(text)
+    return "AI-generated" if result[0]['label'] == 'LABEL_1' else "Not AI-generated"
+
+iface = gr.Interface(
+    fn=detect_ai_generated_text,
+    inputs=[
+        gr.inputs.Dropdown(choices=models, label="Model"),
+        gr.inputs.Dropdown(choices=datasets, label="Dataset"),
+        gr.inputs.Textbox(lines=5, label="Input Text")
+    ],
+    outputs=gr.outputs.Textbox(label="Output"),
+)
+
 iface.launch()