Update app.py

app.py

@@ -3,8 +3,12 @@ import time
 import requests
 from streamlit.components.v1 import html
 
-# Your Gemini API key for the help agent
-gemini_api_key = "AIzaSyC1IoNgTzAZ52tx11K-2_9F5ruaCptiArE"
+# Import transformers and cache the help agent for performance
+@st.cache_resource
+def get_help_agent():
+    from transformers import pipeline
+    # Using BlenderBot 400M Distill as the public conversational model
+    return pipeline("conversational", model="facebook/blenderbot-400M-distill")
 
 # Custom CSS for professional look
 def inject_custom_css():
@@ -106,7 +110,7 @@ def show_confetti():
     </script>
     """)
 
-# Enhanced AI question generation for guessing game
+# Enhanced AI question generation for guessing game using Llama model
 def ask_llama(conversation_history, category, is_final_guess=False):
     api_url = "https://api.groq.com/openai/v1/chat/completions"
     headers = {
@@ -150,31 +154,15 @@ def ask_llama(conversation_history, category, is_final_guess=False):
         st.error(f"Error calling Llama API: {str(e)}")
         return "Could not generate question"
 
-# New function for the help AI assistant using Gemini (or any similar model)
+# New function for the help AI assistant using a Hugging Face chatbot model
 def ask_help_agent(query):
-    gemini_api_url = "https://api.gemini.com/v1/chat/completions"
-    headers = {
-        "Authorization": f"Bearer {gemini_api_key}",
-        "Content-Type": "application/json"
-    }
-    system_prompt = "You're an AI help assistant. Provide concise, friendly advice for clarifying questions in a guessing game. Do not refer to the guessing game details."
-    messages = [
-        {"role": "system", "content": system_prompt},
-        {"role": "user", "content": query}
-    ]
-    data = {
-        "model": "gemini-1",  # or another suitable Gemini model identifier
-        "messages": messages,
-        "temperature": 0.7,
-        "max_tokens": 150
-    }
-    try:
-        response = requests.post(gemini_api_url, headers=headers, json=data)
-        response.raise_for_status()
-        return response.json()["choices"][0]["message"]["content"]
-    except Exception as e:
-        st.error(f"Error calling Gemini API: {str(e)}")
-        return "Could not get help at the moment."
+    from transformers import Conversation
+    # Get the cached help agent (BlenderBot)
+    help_agent = get_help_agent()
+    conversation = Conversation(query)
+    result = help_agent(conversation)
+    # The generated response is stored in generated_responses list
+    return result.generated_responses[-1]
 
 # Main game logic
 def main():
@@ -211,7 +199,6 @@ def main():
 
         with st.form("start_form"):
             category_input = st.text_input("Enter category (person/place/object):").strip().lower()
-            
             if st.form_submit_button("Start Game"):
                 if not category_input:
                     st.error("Please enter a category!")
@@ -246,7 +233,6 @@ def main():
         with st.form("answer_form"):
             answer_input = st.text_input("Your answer (yes/no/both):", 
                                        key=f"answer_{st.session_state.current_q}").strip().lower()
-            
             if st.form_submit_button("Submit"):
                 if answer_input not in ["yes", "no", "both"]:
                     st.error("Please answer with 'yes', 'no', or 'both'!")
@@ -279,7 +265,7 @@ def main():
                     
                     st.rerun()
 
-        # Side Help Option: independent chat with an AI help assistant
+        # Side Help Option: independent chat with an AI help assistant (Hugging Face model)
         with st.expander("Need Help? Chat with AI Assistant"):
             help_query = st.text_input("Enter your help query:", key="help_query")
             if st.button("Send", key="send_help"):