Update app.py

app.py

@@ -1,9 +1,12 @@
 from transformers import pipeline
-import gradio as gr  # Import Gradio for the interface
+import gradio as gr  # Import Gradio for UI
 
 # Load a text-generation model
 chatbot = pipeline("text-generation", model="microsoft/DialoGPT-medium")
 
+# Load the classification model
+classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
+
 # Customize the bot's knowledge base with predefined responses
 faq_responses = {
     "study tips": "Here are some study tips: 1) Break your study sessions into 25-minute chunks (Pomodoro Technique). 2) Test yourself frequently. 3) Stay organized using planners or apps like Notion or Todoist.",
@@ -15,10 +18,19 @@ faq_responses = {
 
 # Define the chatbot's response function
 def faq_chatbot(user_input):
-    # Check if the user's input matches any FAQ keywords
-    for key, response in faq_responses.items():
-        if key in user_input.lower():
-            return response
+    # Classify user input based on predefined FAQ categories
+    classified_user_input = classifier(user_input, candidate_labels=list(faq_responses.keys()))
+
+    # Get the highest confidence score label
+    predicted_label = classified_user_input["labels"][0]
+    confidence_score = classified_user_input["scores"][0]
+
+    # Confidence threshold (adjust as needed)
+    threshold = 0.5
+
+    # If classification confidence is high, return the corresponding FAQ response
+    if confidence_score > threshold:
+        return faq_responses[predicted_label]
 
     # If no FAQ match, use the AI model to generate a response
     conversation = chatbot(user_input, max_length=50, num_return_sequences=1)
@@ -26,12 +38,12 @@ def faq_chatbot(user_input):
 
 # Create the Gradio interface
 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
+    fn=faq_chatbot,  # Function to process user input
+    inputs=gr.Textbox(lines=2, placeholder="Ask me about study tips, resources, or time management..."),  # Input field
+    outputs="text",  # Output text
     title="Student FAQ Chatbot",
-    description="Ask me for study tips, time management advice, or about resources to help with your studies!"
+    description="Ask me study tips, time management strategies, or where to find good study resources!"
 )
 
-# Launch the chatbot and make it public
+# Launch the chatbot and make it accessible via a public Gradio link
 interface.launch(share=True)