Update app.py

app.py

@@ -189,7 +189,7 @@ text_bc_model_path = "polygraf-ai/v3-bert-3-2m-trun-bc-lighter-spec"
 text_bc_tokenizer = AutoTokenizer.from_pretrained(text_bc_model_path)
 text_bc_model = AutoModelForSequenceClassification.from_pretrained(text_bc_model_path).to(device)
 
-text_mc_model_path = "polygraf-ai/text-detect-mc-bert-base-uncased-v1-bert-429k-lighter-spec"
+text_mc_model_path = "polygraf-ai/text-detect-mc-bert-base-uncased-mistral-openai-447k-256"
 text_mc_tokenizer = AutoTokenizer.from_pretrained(text_mc_model_path)
 text_mc_model = AutoModelForSequenceClassification.from_pretrained(text_mc_model_path).to(device)
 
@@ -302,14 +302,10 @@ def ai_generated_test(ai_option, input):
     average_mc_scores = np.mean(mc_scores_array, axis=0)
     bc_score_list = average_bc_scores.tolist()
     mc_score_list = average_mc_scores.tolist()
-
-    # Temporary
-    mc_score_list[1] = mc_score_list[0] +  mc_score_list[1]
-    mc_score_list = mc_score_list[1:]
     
     bc_score = {"AI": bc_score[1].item(), "HUMAN": bc_score[0].item()}
     mc_score = {}
-    label_map = ["OpenAI GPT", "CLAUDE", "BARD", "LLAMA 2"]
+    label_map = ["OpenAI GPT", "Mistral", "CLAUDE", "Gemini", "LLAMA 2"]
     
     for score, label in zip(mc_score_list, label_map):
         mc_score[label.upper()] = score
@@ -489,7 +485,7 @@ with gr.Blocks() as demo:
     d1 = today.strftime("%d/%B/%Y")
     d1 = d1.split("/")
 
-    model_list = ["OpenAI GPT", "CLAUDE", "BARD", "LLAMA2"]
+    model_list = ["OpenAI GPT", "Mistral", "CLAUDE", "Gemini", "LLAMA2"]
     domain_list = ["com", "org", "net", "int", "edu", "gov", "mil"]
     gr.Markdown(
         """