Update app.py

app.py

@@ -1,49 +1,43 @@
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
 import json
-from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
 
-# Load the JSON data from the file
-with open('uts_courses.json') as f:
-    data = json.load(f)
+title = "AI ChatBot"
+description = "A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)"
+examples = [["How are you?"]]
 
-# Load the question-answering pipeline
-qa_pipeline = pipeline("question-answering", model="distilbert-base-cased-distilled-squad")
-
-# Load tokenizer and model for dialog generation
 tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large")
 model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large")
 
-# Main function to interact with the user
-def main():
-    print("Welcome! I'm the UTS Course Chatbot. How can I assist you today?")
-    print("You can ask questions about UTS courses or type 'exit' to end the conversation.")
-
-    while True:
-        user_input = input("You: ")
-        if user_input.lower() == 'exit':
-            print("Bot: Exiting the program.")
-            break
+def predict(input, history=[], file_path=None):
+    if file_path:
+        json_data = read_json_file(file_path)
+        print(f"Contents of {file_path}:")
+        print(json_data)
+        print()
+    new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors="pt")
+    bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
+    history = model.generate(bot_input_ids, max_length=4000, pad_token_id=tokenizer.eos_token_id).tolist()
+    response = tokenizer.decode(history[0]).split("\n")  # Splitting on new lines
+    return response[0], history
+
+def read_json_file(file_path):
+    with open(file_path, 'r') as file:
+        data = json.load(file)
+    return data
 
-        if "courses" in user_input.lower() and "available" in user_input.lower():
-            field = user_input.split("in ")[-1]
-            courses = data['courses'].get(field, [])
-            if courses:
-                response = f"Courses in {field}: {', '.join(courses)}"
-            else:
-                response = f"No courses found in {field}."
-        else:
-            response = generate_dialog_response(user_input)
-        print("Bot:", response)
-
-# Function to generate response using dialog generation model
-def generate_dialog_response(user_input):
-    input_text = user_input + tokenizer.eos_token
-    input_ids = tokenizer.encode(input_text, return_tensors="pt")
-
-    # Generate response
-    response_ids = model.generate(input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
-    response_text = tokenizer.decode(response_ids[0], skip_special_tokens=True)
+def main():
+    gr.Interface(
+        fn=predict,
+        title=title,
+        description=description,
+        examples=examples,
+        inputs=[gr.inputs.Textbox(label="User Input"), gr.inputs.File(label="JSON File")],
+        outputs=["text", "text"],
+        theme="finlaymacklon/boxy_violet",
+    ).launch()
 
-    return response_text
 
 if __name__ == "__main__":
     main()