Create app.py

app.py

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+import os
+import json
+import streamlit as st
+import google.generativeai as genai
+from dotenv import load_dotenv
+
+# --- CONFIGURATION ---
+# Load environment variables from .env file for local development
+load_dotenv()
+
+# Configure the Gemini API key
+try:
+    genai.configure(api_key=os.getenv("GEMINI_API_KEY"))
+except AttributeError:
+    st.error("⚠️ Gemini API key not found. Please set it in your secrets.")
+    st.stop() # Halts execution if no key is found
+
+# --- 1. CONTEXT PROVIDER (Simulated Notion) ---
+# Use st.cache_data to load the database only once
+@st.cache_data
+def load_mock_db():
+    """Loads the mock database from the JSON file."""
+    with open('mock-notion-db.json', 'r') as f:
+        return json.load(f)
+
+notion_data = load_mock_db()
+
+def get_context(query: str) -> str | None:
+    """Finds the most relevant context using keyword matching."""
+    query_words = set(query.lower().split())
+    best_match = None
+    max_score = 0
+
+    for item in notion_data:
+        keywords = set(item.get("keywords", []))
+        score = len(query_words.intersection(keywords))
+        if score > max_score:
+            max_score = score
+            best_match = item
+    return best_match["content"] if best_match else None
+
+# --- 2. LLM PROVIDER (Gemini) ---
+model = genai.GenerativeModel('gemini-1.5-flash')
+
+def generate_response(query: str, context: str) -> str:
+    """Generates a response using the Gemini model with provided context."""
+    prompt = f"""
+    You are a helpful and friendly campus assistant chatbot named Campus Helper Bot.
+    Use the following piece of context to answer the user's question.
+    If the context doesn't contain the answer, state that you don't have information on that topic. Do not make up information.
+    Keep your answer concise and clear.
+
+    Context: "{context or 'No context available.'}"
+    Question: "{query}"
+    Answer:
+    """
+    try:
+        response = model.generate_content(prompt)
+        return response.text
+    except Exception as e:
+        print(f"Error generating response: {e}")
+        return "Sorry, I'm having trouble connecting right now. Please try again later."
+
+# --- 3. STREAMLIT UI AND CHAT LOGIC ---
+
+# Set page title and icon
+st.set_page_config(page_title="Campus Helper Bot", page_icon="🤖")
+
+# Display header
+st.title("🤖 Campus Helper Bot")
+st.caption("Your AI-powered guide to campus information")
+
+# Initialize chat history in session state if it doesn't exist
+if "messages" not in st.session_state:
+    st.session_state.messages = [
+        {"role": "assistant", "content": "Hello! How can I help you with campus information today?"}
+    ]
+
+# Display past messages from session state
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# Main chat input logic
+if prompt := st.chat_input("Ask about fee deadlines, scholarships, etc."):
+    # Add user message to session state and display it
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    # Get and display bot response
+    with st.chat_message("assistant"):
+        with st.spinner("Thinking..."):
+            # 1. Retrieve context
+            context = get_context(prompt)
+            # 2. Generate response
+            response = generate_response(prompt, context)
+            # 3. Display response and add to session state
+            st.markdown(response)
+            st.session_state.messages.append({"role": "assistant", "content": response})