Update app.py

app.py

@@ -2,8 +2,8 @@ import gradio as gr
 from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
 
 # Load your model and tokenizer
-tokenizer = AutoTokenizer.from_pretrained("allenai/llama")
-model = AutoModelForCausalLM.from_pretrained("allenai/llama")
+tokenizer = AutoTokenizer.from_pretrained("allenai/tulu-v1-llama2-13b")
+model = AutoModelForCausalLM.from_pretrained("allenai/tulu-v1-llama2-13b")
 
 # Load a content moderation pipeline
 moderation_pipeline = pipeline("text-classification", model="typeform/mobilebert-uncased-mnli")
@@ -16,12 +16,13 @@ def load_bad_words(filepath):
 # Load bad words list
 bad_words = load_bad_words('badwords.txt')  # Adjust the path to your bad words file
 
-def is_inappropriate_or_offtopic(message, topics):
-    # Check for inappropriate content using the loaded bad words list
+# List of topics for the dropdown
+topics_list = ['Aviation', 'Science', 'Education', 'Air Force Pilot', 'Space Exploration', 'Technology']
+
+def is_inappropriate_or_offtopic(message, selected_topics):
     if any(bad_word in message.lower() for bad_word in bad_words):
         return True
-    # Check if off-topic
-    if topics and not any(topic.lower() in message.lower() for topic in topics if topic):
+    if selected_topics and not any(topic.lower() in message.lower() for topic in selected_topics if topic):
         return True
     return False
 
@@ -31,27 +32,23 @@ def check_content(message):
         return True
     return False
 
-def generate_response(prompt, topic1, topic2, topic3):
-    topics = [topic1, topic2, topic3]  # Collect user-defined topics
-    if is_inappropriate_or_offtopic(prompt, topics):
+def generate_response(message, selected_topics):
+    if is_inappropriate_or_offtopic(message, selected_topics):
         return "Sorry, let's try to keep our conversation focused on positive and relevant topics!"
-    if check_content(prompt):
+    if check_content(message):
         return "I'm here to provide a safe and friendly conversation. Let's talk about something else."
     
-    inputs = tokenizer.encode(prompt, return_tensors="pt")
+    inputs = tokenizer.encode(message, return_tensors="pt")
     outputs = model.generate(inputs, max_length=50, do_sample=True)
     response = tokenizer.decode(outputs[0], skip_special_tokens=True)
     return response
 
-# Define Gradio interface with topic inputs
-iface = gr.Interface(fn=generate_response, 
-                     inputs=[gr.inputs.Textbox(lines=2, placeholder="Type your message here..."),
-                             gr.inputs.Textbox(label="Topic 1", placeholder="Optional", default=""),
-                             gr.inputs.Textbox(label="Topic 2", placeholder="Optional", default=""),
-                             gr.inputs.Textbox(label="Topic 3", placeholder="Optional", default="")],
+# Define Gradio interface
+iface = gr.Interface(fn=generate_response,
+                     inputs=[gr.components.Chatbot(), gr.inputs.Dropdown(choices=topics_list, label="Select Topics", allow_multiple=True)],
                      outputs="text",
                      title="Child-Safe Chatbot",
-                     description="A chatbot that stays on topic and filters inappropriate content. Define up to three topics.")
+                     description="A chatbot that stays on topic and filters inappropriate content. Select relevant topics.")
 
 # Run the app
 if __name__ == "__main__":