File size: 7,396 Bytes
4b3ee30
 
 
df17f8f
4b3ee30
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
a0010c7
4b3ee30
 
 
 
 
 
 
 
 
 
 
 
 
 
a0010c7
4b3ee30
9e1ef69
4b3ee30
 
a0010c7
4b3ee30
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
a0010c7
4b3ee30
 
9e1ef69
4b3ee30
 
9e1ef69
4b3ee30
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
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()