Update app.py

app.py

@@ -7,17 +7,23 @@ 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."
+    "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
+     # 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, i.e., the most likely FAQ
+
+    # 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]
 
@@ -27,9 +33,12 @@ def faq_chatbot(user_input):
     # If the 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)
-    return conversation[0]["generated_text"]
-
-
+    
+    
+    # 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)