Update app.py

app.py

@@ -1,5 +1,5 @@
-import gradio as gr
 from transformers import pipeline
+import gradio as gr  # Import Gradio for the interface
 
 # Load a text-generation model
 chatbot = pipeline("text-generation", model="microsoft/DialoGPT-medium")
@@ -13,7 +13,6 @@ faq_responses = {
     "how to avoid procrastination": "Break tasks into smaller steps, set deadlines, and reward yourself after completing milestones. Tools like Trello can help you stay organized."
 }
 
-
 # Define the chatbot's response function
 def faq_chatbot(user_input):
     # Check if the user's input matches any FAQ keywords
@@ -25,17 +24,14 @@ def faq_chatbot(user_input):
     conversation = chatbot(user_input, max_length=50, num_return_sequences=1)
     return conversation[0]['generated_text']
 
-
 # Create the Gradio interface
-label = gr.Text("**Academic Advisor**")
-
-# Combine the label with the chatbot interface (specifying the output type)
-interface = gr.Interface(fn=faq_chatbot, inputs=gr.Textbox(lines=2, placeholder="Ask me about studying tips or resources..."), outputs="text",  # Define the output type
-                          title="**Academic Advisor**",
-                          description="Ask me for study tips, time management advice, or about resources to help with your studies!")
-
-# Stack the label on top of the interface vertically
-interface = gr.Interface(label, interface, layout="vertical")
-
-# Launch the chatbot
-interface.launch(share=True)
+interface = gr.Interface(
+    fn=faq_chatbot,  # The function to handle user input
+    inputs=gr.Textbox(lines=2, placeholder="Ask me about studying tips or resources..."),  # Input text box
+    outputs="text",  # Output as text
+    title="Student FAQ Chatbot",
+    description="Ask me for study tips, time management advice, or about resources to help with your studies!"
+)
+
+# Launch the chatbot and make it public
+interface.launch(share=True)