Update app.py

app.py

@@ -1,31 +1,40 @@
 import streamlit as st
-import json
 import requests
-from transformers import pipeline
 import wikipediaapi
+import os  # Import for accessing environment variables
 
-# Custom User-Agent to avoid API blocking
-USER_AGENT = "HistoricalChatbot/1.0 (Contact: [email protected])"
+# Fetch Hugging Face API key securely from environment variables
+HUGGINGFACE_API_KEY = os.getenv("HUGGINGFACE_API_KEY")
 
-# Load Wikipedia API with custom headers
-wiki_wiki = wikipediaapi.Wikipedia(
-    "en",
-    headers={"User-Agent": USER_AGENT}
-)
+if not HUGGINGFACE_API_KEY:
+    st.error("⚠️ API Key Missing! Please add your Hugging Face API key as a secret in your Space settings.")
+    st.stop()
 
-# Load local AI model (Mistral-7B or Llama-2)
-chat_model = pipeline("text-generation", model="mistralai/Mistral-7B")
+# Set up Wikipedia API
+USER_AGENT = "HistoricalChatbot/1.0 (Contact: your-[email protected])"
+wiki_wiki = wikipediaapi.Wikipedia("en", headers={"User-Agent": USER_AGENT})
 
 def get_wikipedia_summary(name):
+    """Fetch summary from Wikipedia"""
     page = wiki_wiki.page(name)
     return page.summary if page.exists() else "Sorry, I couldn't find information on that historical figure."
 
 def chat_with_ai(person_name, user_input):
+    """Query Hugging Face API for response"""
     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()
+    prompt = f"You are {person_name}. Based on this historical information: {context}\\n\\nUser: {user_input}\\n{person_name}:"
+    
+    API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B"
+    headers = {"Authorization": f"Bearer {HUGGINGFACE_API_KEY}"}
+    
+    response = requests.post(API_URL, headers=headers, json={"inputs": prompt})
+    
+    if response.status_code == 200:
+        return response.json()[0]["generated_text"]
+    else:
+        return "I'm sorry, but I couldn't generate a response at this time."
 
+# Streamlit UI
 st.title("Educational Chatbot")
 mode = st.sidebar.selectbox("Select Chat Mode", ["Chat with Historical Figures", "Study with a Tutor"])
 
@@ -54,9 +63,7 @@ if user_input and mode == "Chat with Historical Figures" and person_name:
     st.session_state.messages.append({"role": "assistant", "content": response})
     st.chat_message("assistant").write(response)
 
-# Instructions for Deployment
+# Deployment instructions
 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.")
+st.sidebar.markdown("1. Add the API key as a secret in Hugging Face Spaces.")
+st.sidebar.markdown("2. Run `streamlit run app.py` locally or deploy on Hugging Face Spaces.")