Spaces:
Running
on
T4
Running
on
T4
Update app.py
Browse files
app.py
CHANGED
@@ -3,19 +3,27 @@ from teapotai import TeapotAI, TeapotAISettings
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import hashlib
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import os
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import requests
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from langsmith import traceable
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default_documents = []
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API_KEY = os.environ.get("brave_api_key")
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def brave_search(query, count=3):
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url = "https://api.search.brave.com/res/v1/web/search"
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headers = {"Accept": "application/json", "X-Subscription-Token": API_KEY}
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params = {
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"q": query,
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"count": count
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}
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response = requests.get(url, headers=headers, params=params)
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@@ -28,92 +36,65 @@ def brave_search(query, count=3):
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return []
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@traceable
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def query_teapot(prompt, context, user_input, teapot_ai):
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response = teapot_ai.query(
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context=prompt+"\n"+context,
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query=user_input
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)
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return response
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def handle_chat(user_input, teapot_ai):
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results = brave_search(user_input)
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documents = []
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for i, (title, description, url) in enumerate(results, 1):
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documents.append(description.replace('<strong>','').replace('</strong>',''))
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print(documents)
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context="\n".join(documents)
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prompt = "You are Teapot, an open-source AI assistant optimized for low-end devices, providing short, accurate responses without hallucinating while excelling at information extraction and text summarization."
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response = query_teapot(prompt, context, user_input, teapot_ai)
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# response = teapot_ai.chat([
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# {
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# "role": "system",
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# "content": "You are Teapot, an open-source AI assistant optimized for running efficiently on low-end devices. You provide short, accurate responses without hallucinating and excel at extracting information and summarizing text."
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# },
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# {
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# "role": "user",
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# "content": user_input
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# }
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# ])
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return response
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def suggestion_button(suggestion_text, teapot_ai):
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if st.button(suggestion_text):
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handle_chat(suggestion_text, teapot_ai)
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def hash_documents(documents):
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return hashlib.sha256("\n".join(documents).encode("utf-8")).hexdigest()
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# Streamlit app
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def main():
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st.set_page_config(page_title="TeapotAI Chat", page_icon=":robot_face:", layout="wide")
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# Sidebar for document input
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st.sidebar.header("Document Input (for RAG)")
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user_documents = st.sidebar.text_area(
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"Enter documents, each on a new line",
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value="\n".join(default_documents)
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)
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# Parse the user input to get the documents (split by newline)
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documents = user_documents.split("\n")
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documents = [doc for doc in documents if doc.strip()]
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# Check if documents have changed
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new_documents_hash = hash_documents(documents)
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# Load model if documents have changed, otherwise reuse the model from session_state
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if "documents_hash" not in st.session_state or st.session_state.documents_hash != new_documents_hash:
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with st.spinner('Loading Model and Embeddings...'):
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teapot_ai = TeapotAI(documents=documents or default_documents, settings=TeapotAISettings(rag_num_results=3))
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# Store the new hash and model in session state
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st.session_state.documents_hash = new_documents_hash
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st.session_state.teapot_ai = teapot_ai
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else:
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# Reuse the existing model
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teapot_ai = st.session_state.teapot_ai
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# Initialize session state and display the welcome message
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if "messages" not in st.session_state:
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st.session_state.messages = [{"role": "assistant", "content": "Hi, I am Teapot AI, how can I help you?"}]
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# Display previous messages from chat history
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for message in st.session_state.messages:
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with st.chat_message(message["role"]):
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st.markdown(message["content"])
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# Accept user input
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user_input = st.chat_input("Ask me anything")
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s1, s2, s3 = st.columns([1, 2, 3])
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with s1:
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suggestion_button("Tell me about the varieties of tea", teapot_ai)
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@@ -123,26 +104,18 @@ def main():
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suggestion_button("Extract Google's stock price", teapot_ai)
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if user_input:
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# Display user message in chat message container
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with st.chat_message("user"):
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st.markdown(user_input)
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# Add user message to session state
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st.session_state.messages.append({"role": "user", "content": user_input})
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with st.spinner('Generating Response...'):
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# Get the answer from TeapotAI using chat functionality
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response = handle_chat(user_input, teapot_ai)
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# Display assistant response in chat message container
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with st.chat_message("assistant"):
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st.markdown(response)
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# Add assistant response to session state
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st.session_state.messages.append({"role": "assistant", "content": response})
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st.markdown("### Suggested Questions")
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# Run the app
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if __name__ == "__main__":
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main()
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import hashlib
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import os
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import requests
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import time
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from langsmith import traceable
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def log_time(func):
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def wrapper(*args, **kwargs):
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start_time = time.time()
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result = func(*args, **kwargs)
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end_time = time.time()
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print(f"{func.__name__} executed in {end_time - start_time:.4f} seconds")
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return result
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return wrapper
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default_documents = []
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API_KEY = os.environ.get("brave_api_key")
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@log_time
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def brave_search(query, count=3):
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url = "https://api.search.brave.com/res/v1/web/search"
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headers = {"Accept": "application/json", "X-Subscription-Token": API_KEY}
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params = {"q": query, "count": count}
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response = requests.get(url, headers=headers, params=params)
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return []
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@traceable
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@log_time
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def query_teapot(prompt, context, user_input, teapot_ai):
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response = teapot_ai.query(
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context=prompt+"\n"+context,
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query=user_input
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)
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return response
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@log_time
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def handle_chat(user_input, teapot_ai):
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results = brave_search(user_input)
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documents = [desc.replace('<strong>','').replace('</strong>','') for _, desc, _ in results]
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print(documents)
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context = "\n".join(documents)
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prompt = "You are Teapot, an open-source AI assistant optimized for low-end devices, providing short, accurate responses without hallucinating while excelling at information extraction and text summarization."
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response = query_teapot(prompt, context, user_input, teapot_ai)
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return response
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def suggestion_button(suggestion_text, teapot_ai):
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if st.button(suggestion_text):
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handle_chat(suggestion_text, teapot_ai)
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@log_time
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def hash_documents(documents):
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return hashlib.sha256("\n".join(documents).encode("utf-8")).hexdigest()
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def main():
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st.set_page_config(page_title="TeapotAI Chat", page_icon=":robot_face:", layout="wide")
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st.sidebar.header("Document Input (for RAG)")
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user_documents = st.sidebar.text_area("Enter documents, each on a new line", value="\n".join(default_documents))
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documents = [doc.strip() for doc in user_documents.split("\n") if doc.strip()]
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new_documents_hash = hash_documents(documents)
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if "documents_hash" not in st.session_state or st.session_state.documents_hash != new_documents_hash:
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with st.spinner('Loading Model and Embeddings...'):
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start_time = time.time()
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teapot_ai = TeapotAI(documents=documents or default_documents, settings=TeapotAISettings(rag_num_results=3))
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end_time = time.time()
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print(f"Model loaded in {end_time - start_time:.4f} seconds")
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st.session_state.documents_hash = new_documents_hash
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st.session_state.teapot_ai = teapot_ai
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else:
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teapot_ai = st.session_state.teapot_ai
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if "messages" not in st.session_state:
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st.session_state.messages = [{"role": "assistant", "content": "Hi, I am Teapot AI, how can I help you?"}]
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for message in st.session_state.messages:
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with st.chat_message(message["role"]):
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st.markdown(message["content"])
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user_input = st.chat_input("Ask me anything")
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s1, s2, s3 = st.columns([1, 2, 3])
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with s1:
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suggestion_button("Tell me about the varieties of tea", teapot_ai)
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suggestion_button("Extract Google's stock price", teapot_ai)
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if user_input:
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with st.chat_message("user"):
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st.markdown(user_input)
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st.session_state.messages.append({"role": "user", "content": user_input})
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with st.spinner('Generating Response...'):
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response = handle_chat(user_input, teapot_ai)
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with st.chat_message("assistant"):
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st.markdown(response)
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st.session_state.messages.append({"role": "assistant", "content": response})
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st.markdown("### Suggested Questions")
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if __name__ == "__main__":
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main()
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