made the interface

app.py

@@ -1,7 +1,37 @@
 import gradio as gr
+from transformers import pipeline
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Load your models
+# Adjust these lines according to how your models are set up
+roberta_base_detector = pipeline("text-classification", model="Models/fine_tuned/roberta-base-openai-detector-model", tokenizer="Models/fine_tuned/roberta-base-openai-detector-tokenizer")
+chatgpt_lli_hc3_detector = pipeline("text-classification", model="Models/fine_tuned/chatgpt-detector-lli-hc3-model", tokenizer="Models/fine_tuned/chatgpt-detector-lli-hc3-tokenizer")
+chatgpt_roberta_detector = pipeline("text-classification", model="Models/fine_tuned/chatgpt-detector-roberta-model", tokenizer="Models/fine_tuned/chatgpt-detector-roberta-tokenizer")
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+def classify_text(text):
+    # Get predictions from each model
+    roberta_base_pred = roberta_base_detector(text)[0]['label']
+    chatgpt_lli_hc3_pred = chatgpt_lli_hc3_detector(text)[0]['label']
+    chatgpt_roberta_pred = chatgpt_roberta_detector(text)[0]['label']
+
+    # Count the votes for AI and Human
+    votes = {"AI": 0, "Human": 0}
+    for pred in [roberta_base_pred, chatgpt_lli_hc3_pred, chatgpt_roberta_pred]:
+        if pred == "AI":
+            votes["AI"] += 1
+        else:
+            votes["Human"] += 1
+
+    # Determine final decision based on majority
+    if votes["AI"] > votes["Human"]:
+        return "AI"
+    else:
+        return "Human"
+
+# Create Gradio Interface
+iface = gr.Interface(
+    fn=classify_text, 
+    inputs=gr.inputs.Textbox(lines=2, placeholder="Enter a sentence to classify..."), 
+    outputs="text"
+)
+
+iface.launch()