Update app.py

app.py

@@ -1,15 +1,21 @@
 from langchain.vectorstores import FAISS
-from langchain.chains import RetrievalQA
+from langchain.chains import ConversationalRetrievalChain
 from langchain.llms import HuggingFaceHub
+from langchain.chains import RetrievalQA
+from getpass import getpass
 import gradio as gr
 import os
 from langchain.embeddings import HuggingFaceEmbeddings
-from langchain_experimental.agents.agent_toolkits.csv.base import create_csv_agent
+from pandasai import Agent
+from google.colab import drive
 from langchain.document_loaders import PyPDFDirectoryLoader
 from langchain.document_loaders.csv_loader import CSVLoader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.memory import ConversationSummaryBufferMemory
 import io
 import contextlib
+import pandas as pd
+import re
 
 
 embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
@@ -22,15 +28,128 @@ retriever = vector_store.as_retriever(
     search_type="similarity",
     search_kwargs={"k":3, "include_metadata": True})
 
-agent=create_csv_agent(llm,['data/Gretel_Data.csv','data/RAN_Data _T.csv'],verbose=True)
+df=pd.read_csv('data/Gretel_Data.csv')
+averages = df.mean().to_dict()
+agent = Agent([df], config={"llm": llm, 'raw_response':True, 'verbose':True})
+
+global unique_columns
+unique_columns = []
+
+global target_words
+target_words = ["Bandwidth", "Interference", "Call Quality", "Network", "Handover"]
+
+global result_dict
+result_dict = {}
+
+global network_features
+network_features  = {
+    'Bandwidth': [
+        'Avg_Connected_UEs',
+        'PRB Util%',
+        'CA Activation Rate',
+        'DLRLCLayerDataVolume MB',
+        'DRB UL Data Volume MB',
+        'UPTP_Mbps',
+        'UPTP Mbps Num',
+        'UPTP Mbps Den',
+        'UL MAC Vol Scell Pct',
+        'DL MAC Vol Scell Pct',
+        'DL MAC Vol Scell MB',
+        'DL Volume',
+        'DL Data Vol MAC in MB',
+        'UL Throughput',
+        'MB_per_connected_UE'
+    ],
+    'Handover': [
+        'Avg_Connected_UEs',
+        'PRB Util%',
+        'CA Activation Rate',
+        'HO Failures',
+        'HO_fail_InterFreq',
+        'HO_fail_PCT_InterFreq',
+        'HO Failure%',
+        'HO Attempts',
+        'HO_att_InterFreq'
+    ],
+    'Network': [
+        'Avg_Connected_UEs',
+        'PRB Util%',
+        'CA Activation Rate',
+        'SIP DC%',
+        'RRC Setup Attempts',
+        'RRC Setup Failures',
+        'RRC Setup Failure% 5G',
+        'Combined RACH Failure%',
+        'Combined RACH Preambles',
+        'Combined RACH Failures',
+        'Interference Pwr',
+    ],
+    'Call Quality': [
+        'Avg_Connected_UEs',
+        'PRB Util%',
+        'CA Activation Rate',
+        'Avg_PUCCH_SINR',
+        'Avg CQI',
+        'SIP Calls with a Leg',
+        'SIP_SC_Total_MOU',
+        'SIP Dropped Calls',
+        'Volte_MOU',
+        'QCI 1 Bearer Drops',
+        'QCI 1 Bearer Releases',
+        'QCI 1 Bearer Drop%',
+        'Peak UE',
+        'DL Packet Loss Pct',
+        'UL Resid BLER PCT',
+        'Bearer Drops Voice',
+        'Bearer Releases Voice',
+        'Bearer Drop%',
+        'Call_Drops_Credit'
+    ],
+    'Interference': [
+        'Avg_Connected_UEs',
+        'PRB Util%',
+        'CA Activation Rate',
+        'Combined RACH Failure%',
+        'Interference Pwr'
+    ]
+}
 
-def echo(message, history):
+
+def echo(message):
   try:
-    qa=RetrievalQA.from_chain_type(llm=llm, retriever=retriever,return_source_documents=True)
+    qa=RetrievalQA.from_chain_type(llm=llm, retriever=retriever, return_source_documents=True)
     message= "Your name is Clara. You are a senior telecom network engineer having access to troubleshooting tickets data and other technical and product documentation.Stick to the knowledge from these tickets. Ask clarification questions if needed.  "+message
     result=qa({"query":message})
     bold_answer= "<b>" + result['result'] + "</b>"
-    return bold_answer + "<br></br>" +'1. ' + str(result["source_documents"][0]) +"<br>" + '2. ' + str(result["source_documents"][1]) +  "<br>" + "3. " + str(result["source_documents"][2])
+    # return bold_answer + "<br></br>" +'1. ' + str(result["source_documents"][0]) +"<br>" + '2. ' + str(result["source_documents"][1]) +  "<br>" + "3. " + str(result["source_documents"][2])
+    for word in target_words:
+      if re.search(r'\b' + re.escape(word) + r'\b', bold_answer, flags=re.IGNORECASE):
+        columns.extend(network_features.get(word, []))
+    unique_columns = list(set(columns))
+
+    for column in unique_columns:
+      column_avgs.update({column:averages.get(column, [])})
+
+    result_df = df[unique_columns].iloc[:25]
+
+    def highlight_rows(val, threshold):
+        if val > threshold:
+            return 'color: red; font-weight: bold'
+        elif val < threshold:
+            return 'color: green'
+        else:
+            return ''
+
+    # Initialize styled_df outside the loop
+    styled_df = result_df.style
+
+    # Iterate through keys in result_dict
+    for key in column_avgs:
+      # Apply styling for the current key
+      styled_df = styled_df.applymap(lambda x, k=key: highlight_rows(x, column_avgs[k]), subset=[f'{key}'])
+
+    gr.Dataframe(styled_df)
+    return "<br></br>"+ bold_answer + "<br></br>" +'1. ' +  "<br>" + str(result["source_documents"][0]) +"<br>" + '2. ' + str(result["source_documents"][1]) +  "<br>" + "3. " + str(result["source_documents"][2]), styled_df
   except Exception as e:
     error_message = f"An error occurred: {e}"+str(e.with_traceback) + str(e.args)
 
@@ -39,7 +158,7 @@ def echo_agent(message, history):
   try:
     with io.StringIO() as buffer:
       with contextlib.redirect_stdout(buffer):
-        result= agent.run(message)
+        result= agent.chat(message)
       verbose_output = buffer.getvalue()
       verbose_output = verbose_output.replace("\x1b[36;1m\x1b[1;3m", "")
       verbose_output = verbose_output.replace("> ", "")
@@ -51,34 +170,46 @@ def echo_agent(message, history):
     error_message = f"An error occurred: {e}"+str(e.with_traceback) + str(e.args)
     return error_message
 
-demo=gr.ChatInterface(
-        fn=echo,
-        chatbot=gr.Chatbot(height=300, label="Hi I am Clara!", show_label=True),
-        textbox=gr.Textbox(placeholder="Ask me a question", container=True, autofocus=True, scale=7),
+demo_agent = gr.Blocks(
         title="Network Ticket Knowledge Management",
-        description="<span style='font-size: 16x;'>Welcome to Verizon Network Operations Center!! I am here to help the Verizon Field Operations team with technical queries & escalation. I am trained on 1000s of RAN, Backhaul, Core network & End user equipment trouble tickets. Ask me!!!&nbsp;☺</span>",
-        theme=gr.themes.Soft(),
-        examples=["wifi connected but no internet showing", "internet stopped working after primary link down", "internet stopped working link not shifted to secondary after primary link down"],
-        cache_examples=False,
-        retry_btn=None,
-        undo_btn="Delete Previous",
-        clear_btn="Clear",
-        stop_btn="Stop",
-    )
-
-
-demo1=gr.ChatInterface(
-        fn=echo_agent,
-        chatbot=gr.Chatbot(height=300, label="Hi I am Sam!", show_label=True),
-        textbox=gr.Textbox(placeholder="Ask me a question", container=True, autofocus=True, scale=7),
-        title="LLM Powered Agent",
-        description="<span style='font-size: 16x;'>Welcome to Verizon RAN Visualization & Analytics powered by GEN AI. I have access 100 of metrices generated by a RAN base station and can help in visualizing, correlating and generating insights, using power of Conversational AI &nbsp;☺</span>",
         theme=gr.themes.Soft(),
-        retry_btn=None,
-        undo_btn="Delete Previous",
-        clear_btn="Clear",
-        stop_btn="Stop",
-    )
-demo2=gr.TabbedInterface([demo,demo1],["RAG","AGENT"], title='INCEDO', theme=gr.themes.Soft())
-demo2.launch(share=True,debug=True,auth=("admin", "Sam&Clara"))
+)
+
+with demo_agent:
+
+  gr.Markdown(
+      '''
+
+      # <p style="text-align: center;">Network Ticket Knowledge Management</p>
+
+      Welcome to Verizon Network Operations Center!! I am here to help the Verizon Field Operations team with technical queries & escalation. I am trained on 1000s of RAN, Backhaul, Core network & End user equipment trouble tickets. Ask me!!!&nbsp;☺
+      '''
+      )
+
+  with gr.Tab('Clara'):
+    with gr.Row():
+      message = gr.Text(label="Input Query")
+    
+    btn = gr.Button("Submit")
+
+    with gr.Row():
+      reply = gr.Text(label="RCA and MoP")
+    
+    with gr.Accordion(label = "Metrics", open=False):
+          table = gr.Dataframe()
+
+    btn.click(echo, inputs=[message], outputs=[reply, table])
+    gr.Examples(["Wi-Fi connected but no internet showing", "internet stopped working after primary link down", "internet stopped working link not shifted to secondary after primary link down"], inputs=[message])
+
+  with gr.Tab('Sam'):
+    with gr.Row():
+      message_agent = gr.Text(label="Input Query")
+    with gr.Row():
+      reply_agent = gr.Text(label="Answer")
+
+    btn2 = gr.Button("Submit")
+    btn2.click(echo_agent, inputs=[message_agent], outputs=[reply_agent])
+
 
+if __name__ == "__main__":
+    demo_agent.launch(share=True,debug=True,auth=("admin", "Sam&Clara"))