Update app.py

app.py

@@ -3,29 +3,25 @@ import gradio as gr
 import torch
 import json
 
-
 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=[]):
+def predict(input_text, history=[]):
     # Check if the input question is about courses
-    if "courses" in input.lower():
+    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.lower():
+            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
@@ -35,7 +31,7 @@ def predict(input, 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"
+        input_text + tokenizer.eos_token, return_tensors="pt"
     ).to(device)
 
     # append the new user input tokens to the chat history
@@ -43,37 +39,30 @@ def predict(input, history=[]):
 
     # generate a response
     history = model.generate(
-        bot_input_ids, max_length=4000, pad_token_id=tokenizer.eos_token_id
+        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("")
-    response = [
-        (response[i], response[i + 1]) for i in range(0, len(response) - 1, 2)
-    ]  # convert to tuples of list
-    return response, history
-
+    response = tokenizer.decode(history[0]).split()
+    return " ".join(response), history
 
 def main():
     # Load courses data from JSON file
     with open("uts_courses.json", "r") as f:
         courses_data = json.load(f)
-
     print("Contents of uts_courses.json:")
     print(courses_data)
     print()
 
-
 if __name__ == "__main__":
     main()
 
-
 gr.Interface(
     fn=predict,
     title=title,
     description=description,
     examples=examples,
-    inputs=["text", "state"],
-    outputs=["chatbot", "state"],
-    theme="finlaymacklon/boxy_violet",
+    inputs=["text", "text"],  # Changed input from "state" to "text"
+    outputs=["text", "state"],  # Changed output to match the tuple return type
+    theme="finlaymacklon/boxy_violet"
 ).launch()