Update app.py

app.py

@@ -1,25 +1,45 @@
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import gradio as gr
 import torch
+import json
 
 
-title = "????AI ChatBot"
+title = "AI ChatBot"
 description = "A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)"
 examples = [["How are you?"]]
 
 
 tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large")
 model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large")
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+model.to(device)
+
+# Load courses data from JSON file
+with open("uts_courses.json", "r") as f:
+    courses_data = json.load(f)
 
 
 def predict(input, history=[]):
+    # Check if the input question is about courses
+    if "courses" in input.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.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 + tokenizer.eos_token, return_tensors="pt"
-    )
+    ).to(device)
 
     # append the new user input tokens to the chat history
-    bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
+    bot_input_ids = torch.cat([torch.tensor(history).to(device), new_user_input_ids], dim=-1)
 
     # generate a response
     history = model.generate(
@@ -27,15 +47,21 @@ def predict(input, history=[]):
     ).tolist()
 
     # convert the tokens to text, and then split the responses into lines
-    response = tokenizer.decode(history[0]).split("<|endoftext|>")
-    # print('decoded_response-->>'+str(response))
+    response = tokenizer.decode(history[0]).split("")
     response = [
         (response[i], response[i + 1]) for i in range(0, len(response) - 1, 2)
     ]  # convert to tuples of list
-    # print('response-->>'+str(response))
     return response, history
 
 
+def main():
+    pass
+
+
+if __name__ == "__main__":
+    main()
+
+
 gr.Interface(
     fn=predict,
     title=title,
@@ -44,4 +70,4 @@ gr.Interface(
     inputs=["text", "state"],
     outputs=["chatbot", "state"],
     theme="finlaymacklon/boxy_violet",
-).launch()
+).launch()