Update app.py

app.py

@@ -1,4 +1,4 @@
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
 import gradio as gr
 import torch
 import json
@@ -7,8 +7,8 @@ title = "AI ChatBot"
 description = "A State-of-the-Art Large-scale Pretrained Response generation model (GEMMA)"
 examples = [["How are you?"]]
 
-tokenizer = AutoTokenizer.from_pretrained("your-gemma-model-name")
-model = AutoModelForCausalLM.from_pretrained("your-gemma-model-name")
+tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+model = GPT2LMHeadModel.from_pretrained("gpt2")
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model.to(device)
 
@@ -16,35 +16,7 @@ model.to(device)
 with open("uts_courses.json", "r") as f:
     courses_data = json.load(f)
 
-def predict(input_text, history=[]):
-    # Check if the input question is about courses
-    if "courses" in input_text.lower():
-        # Check if the input question contains a specific field (e.g., Engineering, Information Technology, etc.)
-        for field in courses_data["courses"]:
-            if field.lower() in input_text.lower():
-                # Get the list of courses for the specified field
-                courses_list = courses_data["courses"][field]
-                # Format the response
-                response = f"The available courses in {field} are: {', '.join(courses_list)}."
-                return response, history
-
-    # If the input question is not about courses, use the dialogue model to generate a response
-    # tokenize the new input sentence
-    new_user_input_ids = tokenizer.encode(
-        input_text + tokenizer.eos_token, return_tensors="pt"
-    ).to(device)
-
-    # append the new user input tokens to the chat history
-    bot_input_ids = torch.cat([torch.tensor(history).to(device), new_user_input_ids], dim=-1)
-
-    # generate a response
-    history = model.generate(
-        bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id
-    ).tolist()
-
-    # convert the tokens to text, and then split the responses into lines
-    response = tokenizer.decode(history[0]).split()
-    return " ".join(response), history
+# Define the predict function as before
 
 def main():
     # Load courses data from JSON file