Update app.py

app.py

@@ -1,64 +1,85 @@
 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 import load_qa_chain
+from langchain.embeddings import SentenceTransformerEmbeddings
+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]' 
+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("Please give your I/P (e.g., Bug, Authentication issue):")
-    max_results = st.number_input("Enter number of results to retrieve:", min_value=1, max_value=100, value=5)
-    return summary_keyword, max_results
-
-# Streamlit app layout
-st.title("Trouble_Ticket_Finder")
-
-# Get user input through Streamlit widgets
-summary_keyword, max_results = get_user_input()
+    summary_keyword = st.text_input("Enter summary keyword or phrase (e.g., Bug, Authentication issue):")
+    return summary_keyword
 
-# If summary keyword is provided, make the Jira API request
-if 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}"'  # Search for issues where summary contains the keyword
-
-    # Construct the API request URL
+    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
 
-    # Set up the HTTP request headers
-    headers = {
-        'Accept': 'application/json',
-        'Content-Type': 'application/json',
-    }
+# 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
 
-    # Set up the authentication using basic auth (email and API token for Jira Cloud)
-    auth = HTTPBasicAuth(username, api_token)
+# Streamlit app layout
+st.title("Jira Issue Finder with Dynamic Explanations")
 
-    # Define the query parameters
-    params = {
-        'jql': jql_query,
-        'maxResults': max_results,  # Set max results based on user input
-    }
+# Get user input through Streamlit widgets
+summary_keyword = get_user_input()
 
-    # Send the GET request to Jira API
-    response = requests.get(url, headers=headers, auth=auth, params=params)
+# 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()
@@ -66,4 +87,6 @@ if summary_keyword:
         if error_message:
             st.write(f"Error: {', '.join(error_message)}")
         else:
-            st.write(f"Failed to fetch issues: {response.status_code} - {response.text}")
+            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.")