Updated app.py

app.py

@@ -1,17 +1,24 @@
 import gradio as gr
 from transformers import pipeline
 import numpy as np
+from utils import model_predict
 
 
-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")
+roberta_base_detector = AutoModelForSequenceClassification.from_pretrained("Models/fine_tuned/roberta_base_detector-model")
+roberta_base_detector_tknz = AutoTokenizer.from_pretrained("Models/fine_tuned/roberta_base_detector-tokenizer")
+
+chatgpt_lli_hc3_detector = AutoModelForSequenceClassification.from_pretrained("Models/fine_tuned/chatgpt_lli_hc3_detector-model")
+chatgpt_lli_hc3_detector_tknz = AutoTokenizer.from_pretrained("Models/fine_tuned/chatgpt_lli_hc3_detector-tokenizer")
+
+chatgpt_roberta_detector = AutoModelForSequenceClassification.from_pretrained("Models/fine_tuned/chatgpt_roberta_detector-model")
+chatgpt_roberta_detector_tknz = AutoTokenizer.from_pretrained("Models/fine_tuned/chatgpt_roberta_detector-tokenizer")
+
 
 def classify_text(text):
     # Get predictions from each model
-    roberta_base_pred = 1 if roberta_base_detector(text)[0]['label'] == "Fake" else: 0
-    chatgpt_lli_hc3_pred = chatgpt_lli_hc3_detector(text)[0]['label']
-    chatgpt_roberta_pred = chatgpt_roberta_detector(text)[0]['label']
+    roberta_base_pred = model_predict(roberta_base_detector, roberta_base_detector_tknz, text)
+    chatgpt_lli_hc3_pred = model_predict(chatgpt_lli_hc3_detector, chatgpt_lli_hc3_detector_tknz, text)
+    chatgpt_roberta_pred = model_predict(chatgpt_roberta_detector, chatgpt_roberta_detector_tknz, text)
 
     # Count the votes for AI and Human
     votes = {"AI": 0, "Human": 0}
@@ -23,9 +30,9 @@ def classify_text(text):
 
     # Determine final decision based on majority
     if votes["AI"] > votes["Human"]:
-        return chatgpt_lli_hc3_pred
+        return "AI"
     else:
-        return chatgpt_lli_hc3_pred
+        return "Human"
 
 # Create Gradio Interface
 iface = gr.Interface(