app.py

import streamlit as st
import requests
from requests.auth import HTTPBasicAuth
from transformers import pipeline
import json
from sentence_transformers import SentenceTransformer, util
from langchain.chains.question_answering import load_qa_chain
from langchain_community.embeddings import SentenceTransformerEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.vectorstores import FAISS
from langchain.llms import HuggingFaceEndpoint
import numpy as np

# Jira instance details
jira_url = 'https://gen-ai-demo.atlassian.net/'
api_endpoint = '/rest/api/3/search'
username = '[email protected]'
api_token = 'ATATT3xFfGF09uFJnHFeDFU0_AKBZXP98fd2ZFLxzbZfTQ4Tr17IZxD6qoPbniEvLRWsxiO207EAYX77LBfa5NEXiK1J9_Crq7fF1lWPdH8MwY6Vp9GSLr-_0etnMcgqDPRn9cuLD9Lk1IxoxDY_Yh5nm36yp_Xg50RP5AN8mwJmMhC_uoad_A4=CBFBB200'

# Function to get user input for Jira query using Streamlit
def get_user_input():
    summary_keyword = st.text_input("You:")
    return summary_keyword

# Function to fetch Jira issues based on the keyword
def fetch_jira_issues(summary_keyword):
    # Construct the JQL query based on the user input
    jql_query = f'summary ~ "{summary_keyword}"'
    url = jira_url + api_endpoint
    headers = {'Accept': 'application/json', 'Content-Type': 'application/json'}
    auth = HTTPBasicAuth(username, api_token)
    params = {'jql': jql_query, 'maxResults': 5}
    
    response = requests.get(url, headers=headers, auth=auth, params=params)
    return response

# Function to load dynamic answer using embeddings-based search
def load_answer(question):
    embeddings = SentenceTransformerEmbeddings(model_name="nomic-ai/nomic-embed-text-v1", model_kwargs={"trust_remote_code":True})
    # Assuming 'finalData' is a list of documents or text snippets
    finalData = [
        "Authentication failure due to invalid credentials.",
        "Bug in login page causing a crash when incorrect input is provided.",
        "Database timeout issues during large data queries.",
        "Server error due to high request load.",
        "Page rendering issues when javascript fails to load.",
        "Error occurred while fetching data from the API.",
        "Network error caused by unstable connection to the server."
    ]
    documentSearch = FAISS.from_texts(finalData, embeddings)
    chain = load_qa_chain(HuggingFaceEndpoint(repo_id="mistralai/Mistral-7B-Instruct-v0.3"), chain_type="stuff")
    docs = documentSearch.similarity_search(question)
    answer = chain.invoke({"input_documents": docs, "question": question}, return_only_outputs=True)
    return answer

# Streamlit app layout
st.title("Trouble_Ticket_Finder")

# Get user input through Streamlit widgets
summary_keyword = get_user_input()

# If summary keyword is provided, make the Jira API request
if summary_keyword:
    response = fetch_jira_issues(summary_keyword)

    # Check for successful request
    if response.status_code == 200:
        data = response.json()
        issues = data.get('issues', [])
        
        if issues:
            # If issues are found, display them
            st.write("Found Jira issues matching your query:")
            for issue in issues:
                st.write(f"**Key:** {issue['key']} - **Summary:** {issue['fields']['summary']}")
                st.write(f"**Description:** {issue['fields'].get('description', 'No description available')}")
        else:
            st.write("No issues found matching your summary keyword.")
            
            # If no issues are found, provide dynamic explanation using embeddings
            st.write("Searching for a dynamic explanation using embeddings...")
            answer = load_answer(summary_keyword)
            st.write(f"**Dynamic Explanation:** {answer['output']}")
    else:
        # If the request failed, show the error details
        error_data = response.json()
        error_message = error_data.get("errorMessages", [])
        if error_message:
            st.write(f"Error: {', '.join(error_message)}")
        else:
            st.write(f"Failed to fetch issues: {response.status_code} - {response.text}")
else:
    st.write("Please enter a summary keyword to search for Jira issues.")