Update app.py

app.py

@@ -1,37 +1,38 @@
 from transformers import pipeline
-import gradio as gr  # Import Gradio for the interface
+import gradio as gr # Import Gradio for the interface
+
 
 # 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.",
-    "resources for studying": "You can find free study resources on websites like Khan Academy, Coursera, and edX. For research papers, check Google Scholar.",
-    "how to focus": "To improve focus, try studying in a quiet place, remove distractions like your phone, and use apps like Forest or Focus@Will.",
-    "time management tips": "Start by creating a to-do list each morning. Prioritize tasks using methods like Eisenhower Matrix and allocate specific time blocks for each task.",
-    "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):
+    # Classify the user input by passing the FAQ keywords as labels
+    classified_user_input = classifier(user_input, candidate_labels=list(faq_responses.keys()))
+
+    # Get the highest confidence score label, ie. the most likely of the FAQ
+    predicted_label = classified_user_input["labels"][0]
+    confidence_score = classified_user_input["scores"][0]
+
+    # Confidence threshold (adjust if needed)
+    threshold = 0.5
+
+    # If the classification confidence is high, return the corresponding FAQ response
+    if confidence_score > threshold:
+        return faq_responses[predicted_label]
+    
+    
     # 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
+    # for key, response in faq_responses.items():
+    #     if key in user_input.lower():
+    #         return response
 
-    # If no FAQ match, use the AI model to generate a response
+ # If no FAQ match, use the AI model to generate a response
     conversation = chatbot(user_input, max_length=50, num_return_sequences=1)
-    return conversation[0]['generated_text']
-
-# 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
-    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)