Create app.py

app.py

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+from langchain.vectorstores import FAISS
+from langchain.chains import RetrievalQA
+from langchain.llms import HuggingFaceHub
+import gradio as gr
+import os
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain_experimental.agents.agent_toolkits.csv.base import create_csv_agent
+from langchain.document_loaders import PyPDFDirectoryLoader
+from langchain.document_loaders.csv_loader import CSVLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+import io
+import contextlib
+
+
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+vector_store= FAISS.load_local("vector_db/", embeddings)
+
+repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1"
+llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={"temperature": 0.01, "max_new_tokens": 2048})
+
+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)
+
+def echo(message, history):
+  try:
+    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])
+  except Exception as e:
+    error_message = f"An error occurred: {e}"+str(e.with_traceback) + str(e.args)
+
+def echo_agent(message, history):
+  message="There are 2 df's. If you find a KeyError check for the same in the other df." + "<br>" + message
+  try:
+    with io.StringIO() as buffer:
+      with contextlib.redirect_stdout(buffer):
+        result= agent.run(message)
+      verbose_output = buffer.getvalue()
+      verbose_output = verbose_output.replace("\x1b[36;1m\x1b[1;3m", "")
+      verbose_output = verbose_output.replace("> ", "")
+      verbose_output = verbose_output.replace("", "")
+      verbose_output = verbose_output.replace("", "")
+      result= "<b>" + verbose_output + "<br>" + result + "</b>"
+    return result
+  except Exception as e:
+    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),
+        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"))