Delete app.py

app.py

@@ -1,107 +0,0 @@
-import os
-import chromadb
-
-import gradio as gr
-
-from dotenv import load_dotenv
-from openai import OpenAI
-
-from langchain_community.embeddings import AnyscaleEmbeddings
-from langchain_community.vectorstores import Chroma
-
-qna_system_message = """
-You are an assistant to an insurance firm who answers user queries on policy documents.
-User input will have the context required by you to answer user questions.
-This context will begin with the word: ###Context.
-The context contains references to specific portions of a document relevant to the user query.
-
-User questions will begin with the word: ###Question.
-
-Please answer user questions only using the context provided in the input.
-Do not mention anything about the context in your final answer. Your response should only contain the answer to the question.
-
-If the answer is not found in the context, respond "Sorry, I cannot answer your question. Please contact our representative on the hotline 1-800-AWESOMEINSURER".
-"""
-
-qna_user_message_template = """
-###Context
-Here are some documents that are relevant to the question mentioned below.
-{context}
-
-###Question
-{question}
-"""
-
-load_dotenv()
-
-anyscale_api_key = os.environ['ANYSCALE_API_KEY']
-
-client = OpenAI(
-    base_url="https://api.endpoints.anyscale.com/v1",
-    api_key=anyscale_api_key
-)
- 
-qna_model = 'mlabonne/NeuralHermes-2.5-Mistral-7B'
-
-embedding_model = AnyscaleEmbeddings(
-    client=client,
-    model='thenlper/gte-large'
-)
-
-chromadb_client = chromadb.PersistentClient(path='./policy_db')
-
-vectorstore_persisted = Chroma(
-    client=chromadb_client,
-    collection_name="policy-text",
-    embedding_function=embedding_model
-)
-
-retriever = vectorstore_persisted.as_retriever(
-    search_type='similarity',
-    search_kwargs={'k': 5}
-)
-
-def predict(question):
-
-    relevant_document_chunks = retriever.invoke(question)
-    context_list = [d.page_content for d in relevant_document_chunks]
-    context_for_query = "\n".join(context_list)
-
-    prompt = [
-        {'role':'system', 'content': qna_system_message},
-        {'role': 'user', 'content': qna_user_message_template.format(
-            context=context_for_query,
-            question=question
-        )
-        }
-    ]
-
-    try:
-        response = client.chat.completions.create(
-              model=qna_model,
-              messages=prompt,
-              temperature=0
-        )
-
-        prediction = response.choices[0].message.content.strip()
-    except Exception as e:
-        prediction = f'Sorry, I encountered the following error: \n {e}'
-
-    return prediction
-
-textbox = gr.Textbox(placeholder="Enter your query here", lines=6)
-
-demo = gr.Interface(
-    inputs=textbox, fn=predict, outputs="text",
-    title="AMA on your insurance policy document",
-    description="This web API presents an interface to ask questions on contents of your health insurance policy.",
-    article="Note that questions that are not relevant to the policy will not be answered.",
-    examples=[["My trip was delayed and I paid 45, how much am I covered for?", ""],
-              ["I just had a baby, is baby food covered?", ""],
-              ["How is the gauze used in my operation covered?", ""]
-             ],
-    concurrency_limit=16
-)
-
-demo.queue()
-demo.launch(auth=("demouser", os.getenv('PASSWD')))