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import anthropic
import base64
import json
import os
import pandas as pd
import pytz
import re
import streamlit as st
from datetime import datetime
from gradio_client import Client
from azure.cosmos import CosmosClient, exceptions
# App Configuration
title = "πŸ€– ArXiv and Claude AI Assistant"
st.set_page_config(page_title=title, layout="wide")
# Cosmos DB configuration
ENDPOINT = "https://acae-afd.documents.azure.com:443/"
Key = os.environ.get("Key")
DATABASE_NAME = os.environ.get("COSMOS_DATABASE_NAME")
CONTAINER_NAME = os.environ.get("COSMOS_CONTAINER_NAME")
# Initialize Anthropic client
anthropic_client = anthropic.Anthropic(api_key=os.environ.get("ANTHROPIC_API_KEY"))
# Initialize session state
if "chat_history" not in st.session_state:
st.session_state.chat_history = []
def generate_filename(prompt, file_type):
"""Generate a filename with timestamp and sanitized prompt"""
central = pytz.timezone('US/Central')
safe_date_time = datetime.now(central).strftime("%m%d_%H%M")
safe_prompt = re.sub(r'\W+', '', prompt)[:90]
return f"{safe_date_time}{safe_prompt}.{file_type}"
def create_file(filename, prompt, response, should_save=True):
"""Create and save a file with prompt and response"""
if not should_save:
return
with open(filename, 'w', encoding='utf-8') as file:
file.write(f"Prompt:\n{prompt}\n\nResponse:\n{response}")
def save_to_cosmos_db(container, query, response1, response2):
"""Save interaction to Cosmos DB"""
try:
if container:
timestamp = datetime.utcnow().strftime('%Y%m%d%H%M%S%f')
record = {
"id": timestamp,
"name": timestamp,
"query": query,
"response1": response1,
"response2": response2,
"timestamp": datetime.utcnow().isoformat(),
"type": "ai_response",
"version": "1.0"
}
container.create_item(body=record)
st.success(f"Record saved to Cosmos DB with ID: {record['id']}")
except Exception as e:
st.error(f"Error saving to Cosmos DB: {str(e)}")
def search_arxiv(query):
"""Search ArXiv using Gradio client"""
try:
client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
# Get response from Mixtral model
result_mixtral = client.predict(
query,
"mistralai/Mixtral-8x7B-Instruct-v0.1",
True,
api_name="/ask_llm"
)
# Get response from Mistral model
result_mistral = client.predict(
query,
"mistralai/Mistral-7B-Instruct-v0.2",
True,
api_name="/ask_llm"
)
# Get RAG-enhanced response
result_rag = client.predict(
query,
10, # llm_results_use
"Semantic Search",
"mistralai/Mistral-7B-Instruct-v0.2",
api_name="/update_with_rag_md"
)
return result_mixtral, result_mistral, result_rag
except Exception as e:
st.error(f"Error searching ArXiv: {str(e)}")
return None, None, None
def main():
st.title(title)
# Initialize Cosmos DB client if key is available
if Key:
cosmos_client = CosmosClient(ENDPOINT, credential=Key)
try:
database = cosmos_client.get_database_client(DATABASE_NAME)
container = database.get_container_client(CONTAINER_NAME)
except Exception as e:
st.error(f"Error connecting to Cosmos DB: {str(e)}")
container = None
else:
st.warning("Cosmos DB Key not found in environment variables")
container = None
# Create tabs for different functionalities
arxiv_tab, claude_tab, history_tab = st.tabs(["ArXiv Search", "Chat with Claude", "History"])
with arxiv_tab:
st.header("πŸ” ArXiv Search")
arxiv_query = st.text_area("Enter your research query:", height=100)
if st.button("Search ArXiv"):
if arxiv_query:
with st.spinner("Searching ArXiv..."):
result_mixtral, result_mistral, result_rag = search_arxiv(arxiv_query)
if result_mixtral:
st.subheader("Mixtral Model Response")
st.markdown(result_mixtral)
st.subheader("Mistral Model Response")
st.markdown(result_mistral)
st.subheader("RAG-Enhanced Response")
if isinstance(result_rag, (list, tuple)) and len(result_rag) > 0:
st.markdown(result_rag[0])
if len(result_rag) > 1:
st.markdown(result_rag[1])
# Save results
filename = generate_filename(arxiv_query, "md")
create_file(filename, arxiv_query, f"{result_mixtral}\n\n{result_mistral}")
if container:
save_to_cosmos_db(container, arxiv_query, result_mixtral, result_mistral)
with claude_tab:
st.header("πŸ’¬ Chat with Claude")
user_input = st.text_area("Your message:", height=100)
if st.button("Send"):
if user_input:
with st.spinner("Claude is thinking..."):
try:
response = anthropic_client.messages.create(
model="claude-3-sonnet-20240229",
max_tokens=1000,
messages=[{"role": "user", "content": user_input}]
)
claude_response = response.content[0].text
st.markdown("### Claude's Response:")
st.markdown(claude_response)
# Save chat history
st.session_state.chat_history.append({
"user": user_input,
"claude": claude_response,
"timestamp": datetime.now().isoformat()
})
# Save to file
filename = generate_filename(user_input, "md")
create_file(filename, user_input, claude_response)
# Save to Cosmos DB
if container:
save_to_cosmos_db(container, user_input, claude_response, "")
except Exception as e:
st.error(f"Error communicating with Claude: {str(e)}")
with history_tab:
st.header("πŸ“œ Chat History")
for chat in reversed(st.session_state.chat_history):
with st.expander(f"Conversation from {chat.get('timestamp', 'Unknown time')}"):
st.markdown("**Your message:**")
st.markdown(chat["user"])
st.markdown("**Claude's response:**")
st.markdown(chat["claude"])
if __name__ == "__main__":
main()