Update app.py

app.py

@@ -4,10 +4,6 @@ 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.",
@@ -19,26 +15,23 @@ faq_responses = {
 
 # 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
-
- # If no FAQ match, use the AI model to generate a response
-    conversation = chatbot(user_input, max_length=50, num_return_sequences=1)
+    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
+    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)