Update app.py

app.py

@@ -1,60 +1,50 @@
 import streamlit as st
-import json
-import requests
-from transformers import pipeline
 import wikipediaapi
+import requests
 
-# Load historical figures and tutor topics from Wikipedia dynamically
-wiki_wiki = wikipediaapi.Wikipedia(
-    user_agent='HistoricalFiguresApp/1.0 ([email protected])',  # Replace with your contact info
-    language='en'
-)
-
-# Load local AI model (HuggingFaceH4/zephyr-7b-beta)
-chat_model = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-beta")
-
+API_URL = "https://api-inference.huggingface.co/models/HuggingFaceH4/zephyr-7b-beta"
+headers = {"Authorization": f"Bearer {st.secrets['HUGGINGFACE_TOKEN']}"}
 
+@st.cache_data
 def get_wikipedia_summary(name):
-    page = wiki_wiki.page(name)
-    return page.summary if page.exists() else "Sorry, I couldn't find information on that historical figure."
+    wiki = wikipediaapi.Wikipedia('en')
+    page = wiki.page(name)
+    return page.summary if page.exists() else None
+
+def query_model(payload):
+    response = requests.post(API_URL, headers=headers, json=payload)
+    return response.json()
 
 def chat_with_ai(person_name, user_input):
     context = get_wikipedia_summary(person_name)
-    prompt = f"You are {person_name}. Based on this historical information: {context}\n\nUser: {user_input}\n{person_name}:"
-    response = chat_model(prompt, max_length=200, truncation=True)
-    return response[0]["generated_text"].split("User:")[0].strip()
+    if not context:
+        return f"Could not find information about {person_name}"
+    
+    prompt = f"You are {person_name}. Context: {context}\n\nUser: {user_input}\n{person_name}:"
+    try:
+        response = query_model({"inputs": prompt})
+        return response[0]["generated_text"].split("User:")[0].strip()
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+st.title("Historical Figures Chat")
+person_name = st.text_input("Enter historical figure name:")
 
-st.title("Educational Chatbot")
-mode = st.sidebar.selectbox("Select Chat Mode", ["Chat with Historical Figures", "Study with a Tutor"])
-
-if mode == "Chat with Historical Figures":
-    person_name = st.text_input("Enter the name of a historical figure:")
-    if person_name:
-        st.write(f"You are now chatting with **{person_name}**!")
-
-elif mode == "Study with a Tutor":
-    topic = st.sidebar.selectbox("Choose a Study Topic", ["Mathematics", "History", "Physics"])
-    st.write(f"You are now studying **{topic}**!")
-
-# Chat input
 if "messages" not in st.session_state:
     st.session_state.messages = []
 
 for msg in st.session_state.messages:
     st.chat_message(msg["role"]).write(msg["content"])
 
-user_input = st.chat_input("Type your message here...")
-
-if user_input and mode == "Chat with Historical Figures" and person_name:
+if user_input := st.chat_input("Message..."):
+    if not person_name:
+        st.error("Please enter a historical figure's name")
+        st.stop()
+        
     st.session_state.messages.append({"role": "user", "content": user_input})
     st.chat_message("user").write(user_input)
-    response = chat_with_ai(person_name, user_input)
-    st.session_state.messages.append({"role": "assistant", "content": response})
-    st.chat_message("assistant").write(response)
-
-# Instructions for Deployment
-st.sidebar.subheader("Deployment Instructions")
-st.sidebar.markdown("1. Ensure `transformers` and `wikipedia-api` libraries are installed.")
-st.sidebar.markdown("2. Rename `chatbot.py` to `app.py` for Hugging Face Spaces.")
-st.sidebar.markdown("3. Run `streamlit run app.py` locally.")
-st.sidebar.markdown("4. Deploy on [Hugging Face Spaces](https://huggingface.co/spaces) or Replit.")
+    
+    with st.spinner("Thinking..."):
+        response = chat_with_ai(person_name, user_input)
+        st.session_state.messages.append({"role": "assistant", "content": response})
+        st.chat_message("assistant").write(response)