Update app.py

app.py

@@ -1,8 +1,10 @@
+# Import required libraries
 from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import json
 
+# Create FastAPI app instance
 app = FastAPI()
 
 # Load DialoGPT model and tokenizer
@@ -10,38 +12,60 @@ try:
     tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large")
     model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large")
 except Exception as e:
-    raise HTTPException(status_code=500, detail=f"Failed to load model: {e}")
+    raise HTTPException(status_code=500, detail=f"Model loading failed: {e}")
 
-# Load courses data from JSON file
+# Load courses data
 try:
     with open("uts_courses.json", "r") as file:
         courses_data = json.load(file)
 except Exception as e:
-    raise HTTPException(status_code=500, detail=f"Failed to load courses data: {e}")
+    raise HTTPException(status_code=500, detail=f"Courses data loading failed: {e}")
 
+# Define user input model
 class UserInput(BaseModel):
     user_input: str
 
+# Generate response function
 def generate_response(user_input: str):
+    """
+    Generate response based on user input
+
+    Args:
+        user_input: User input text
+
+    Returns:
+        Generated response text
+    """
     if user_input.lower() == "help":
-        return "I can help you with information about UTS courses. Feel free to ask!"
+        return "I can help you with UTS courses information, feel free to ask!"
     elif user_input.lower() == "exit":
         return "Goodbye!"
     elif user_input.lower() == "list courses":
+        # Generate course list
         course_list = "\n".join([f"{category}: {', '.join(courses)}" for category, courses in courses_data["courses"].items()])
         return f"Here are the available courses:\n{course_list}"
     elif user_input.lower() in courses_data["courses"]:
-        return f"The courses in {user_input} are: {', '.join(courses_data['courses'][user_input])}"
+        # List courses under the specified course category
+        return f"The courses in {user_input} category are: {', '.join(courses_data['courses'][user_input])}"
     else:
-        # Tokenize the user input
+        # Use DialoGPT model to generate response
         input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors="pt")
-        # Generate a response
         response_ids = model.generate(input_ids, max_length=100, pad_token_id=tokenizer.eos_token_id)
-        # Decode the response
         response = tokenizer.decode(response_ids[0], skip_special_tokens=True)
         return response
 
+# Define API route
 @app.post("/")
-def chat(user_input: UserInput):
+async def chat(user_input: UserInput):
+    """
+    Process user input and return response
+
+    Args:
+        user_input: User input JSON data
+
+    Returns:
+        JSON data containing the response text
+    """
     response = generate_response(user_input.user_input)
     return {"response": response}
+