Upload app.py

app.py

@@ -9,13 +9,24 @@ from langchain.memory import ConversationBufferMemory
 from langchain.chains import ConversationalRetrievalChain
 from langchain.llms import HuggingFaceHub
 from htmlTemplates import css, bot_template, user_template
+from streamlit_chat import message
+import os
+from docx import Document
+import requests
+from requests.auth import HTTPBasicAuth
 
-def get_pdf_text(pdf_docs):
+def get_uploaded_text(uploadedFiles):
     text = ""
-    for pdf in pdf_docs:
-        pdf_reader = PdfReader(pdf)
-        for page in pdf_reader.pages:
-            text += page.extract_text()
+    for uploadedFile in uploadedFiles:
+        file_extension = os.path.splitext(uploadedFile.name)[1]
+        if(file_extension == '.pdf'):
+            pdf_reader = PdfReader(uploadedFile)
+            for page in pdf_reader.pages:
+                text += page.extract_text()
+        elif(file_extension == '.docx'):
+            doc = Document(uploadedFile)
+            for para in doc.paragraphs:
+                text += para.text
     return text
 
 def get_text_chunks(text):
@@ -35,7 +46,7 @@ def get_vectorstore(text_chunks):
     return vectorstore
 
 def get_conversation_chain(vectorstore):
-    llm = ChatOpenAI()
+    llm = ChatOpenAI(temperature=0.3)
     # llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.5, "max_length":512})
 
     memory = ConversationBufferMemory(
@@ -50,47 +61,107 @@ def get_conversation_chain(vectorstore):
 def handle_userinput(user_question, myslot):
     response = st.session_state.conversation({'question': user_question})
     st.session_state.chat_history = response['chat_history']
+    indexed = response['answer'].find("don't have") != -1 or response['answer'].find("don't know") != -1
+    if response and response['answer'] and indexed:
+        st.session_state.sr = 0
+    else:
+        st.session_state.sr = 1
     with myslot.container():
-        for i, message in enumerate(st.session_state.chat_history):
+        for i, msg in enumerate(st.session_state.chat_history):
             if i % 2 == 0:
-                st.write(user_template.replace(
-                    "{{MSG}}", message.content), unsafe_allow_html=True)
+                message(msg.content, is_user=True) 
             else:
-                st.write(bot_template.replace(
-                    "{{MSG}}", message.content), unsafe_allow_html=True)
+                message(msg.content)
+
+def create_jira_ticket(summary, description, project_key, issuetype_name):
+    url = "https://tnq.atlassian.net/rest/api/3/issue"
+    token = "ATATT3xFfGF0EqZ--az1c1buBTXEebQfuxMIBv0jByz6tNHcXXrEV61esZS_ZIwOY15OJN7jHn23IBG1Fzw3lm7GKhtGqpetDZCaiis2BbGefP3dvS5aOfPubz1ZWlEjAbQr4s87J8hr7xMceFTbTMfXk4kk8TRN3jEes9SyMLyhnuux1ugykck=13D01E13"
+    auth = HTTPBasicAuth("harinandan.munagala@tnqtech.com", token)
+
+    headers = {
+       "Accept": "application/json",
+       "Content-Type": "application/json"
+    }
+
+    payload = {
+        "fields": {
+            "project":
+            {
+                "key": project_key
+            },
+            "summary": summary,
+            "customfield_10044": [{"value": "Edit Central All"}],
+            "description": {
+                "type": "doc",
+                "version": 1,
+                "content": [
+                    {
+                        "type": "paragraph",
+                        "content": [
+                            {
+                                "type": "text",
+                                "text": "Creating of an issue using project keys and issue type names using the REST API"
+                            }
+                        ]
+                    }
+                ]
+            },
+            "issuetype": {
+                "name": issuetype_name
+            }
+        }
+    }
+
+    response = requests.post(
+        url, json=payload, headers=headers, auth=auth
+    )
+
+    return response.json()
+
 
 def main():
     load_dotenv()
-    st.set_page_config(page_title="Chat with Support Documents and FAQs",
-                       page_icon=":books:")
+    st.set_page_config(page_title="AIusBOT", page_icon=":alien:")
 
     if "conversation" not in st.session_state:
         st.session_state.conversation = None
     if "chat_history" not in st.session_state:
         st.session_state.chat_history = None
+    if "sr" not in st.session_state:
+        st.session_state.sr = 1
 
-    st.header("Chat with Support Documents and FAQs :books:")
+    st.header("AIusBOT :alien:")
 
     myslot = st.empty()
-    user_question = st.text_input("Ask a question about your documents:writewritewrite")
+
+    user_question = st.text_input("Ask a question?")
     if user_question:
         handle_userinput(user_question, myslot)
 
+    if st.button("Create SR?", disabled=st.session_state.sr, type="primary"):
+        jira_response = create_jira_ticket(
+            summary=user_question,
+            description=f"User question that did not receive a satisfactory answer: {user_question}",
+            project_key="EC",
+            issuetype_name="Task"
+        )
+        st.write(f"Ticket created: {jira_response.get('key')}")
+
     with st.sidebar:
         st.subheader("Your support Documents")
         pdf_docs = st.file_uploader(
             "Upload your Documents here and click on 'Process'", accept_multiple_files=True)
         if st.button("Process"):
             with st.spinner("Uploading the docs"):
-                raw_text = get_pdf_text(pdf_docs)
+
+                raw_text = get_uploaded_text(pdf_docs)
 
                 text_chunks = get_text_chunks(raw_text)
                 
                 vectorstore = get_vectorstore(text_chunks)
 
                 st.session_state.conversation = get_conversation_chain(vectorstore)
-
-
+                st.toast("File Process Completed", icon='🎉')
 
 
 if __name__ == '__main__':