Delete app.py

app.py

@@ -1,93 +0,0 @@
-import gradio as gr
-import tiktoken
-
-import os
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.vectorstores import Chroma
-from langchain.document_loaders import TextLoader
-from langchain import PromptTemplate
-from langchain.chains import LLMChain
-from langchain.chains.qa_with_sources import load_qa_with_sources_chain
-from langchain.llms import OpenAI
-from langchain.vectorstores import FAISS
-from langchain_openai import OpenAIEmbeddings
-from langchain_openai import ChatOpenAI
-
-# Load the FAISS index from the .pkl file
-openai_api_key = os.getenv("OPENAI_API_KEY")
-if not openai_api_key:
-    raise ValueError("OPENAI_API_KEY environment variable is not set.")
-
-embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
-#with open("index.pkl", "rb") as f:
- #   db = faiss.read_index(f.read())
-#with open("index.pkl", "rb") as f:
-    #db = faiss.deserialize_index(f.read())
-
-db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
-
-def get_response_from_query(db, query, k=3):
-
-    docs = db.similarity_search(query, k=k)
-
-    docs_page_content = " ".join([d.page_content for d in docs])
-
-    # llm = BardLLM()
-    llm = ChatOpenAI(model_name="gpt-3.5-turbo-16k",temperature=0)
-
-    prompt = PromptTemplate(
-        input_variables=["question", "docs"],
-        template="""
-        A bot that is open to discussions about different cultural, philosophical and political exchanges. I will use do different analysis to the articles provided to me. Stay truthful and if you weren't provided any resources give your oppinion only.
-        Answer the following question: {question}
-        By searching the following articles: {docs}
-        Only use the factual information from the documents. Make sure to mention key phrases from the articles.
-        If you feel like you don't have enough information to answer the question, say "I don't know".
-        """,
-    )
-
-    chain = LLMChain(llm=llm, prompt=prompt)
-    response = chain.run(question=query, docs=docs_page_content,return_source_documents=True)
-    r_text = str(response)
-
-    ##evaluation part
-
-    prompt_eval = PromptTemplate(
-        input_variables=["answer", "docs"],
-        template="""
-       You job is to evaluate if the response to a given context is faithful.
-        for the following: {answer}
-        By searching the following article: {docs}
-       Give a reason why they are similar or not, start with a Yes or a No.
-        """,
-    )
-
-    chain_part_2 = LLMChain(llm=llm, prompt=prompt_eval)
-
-
-    evals = chain_part_2.run(answer=r_text, docs=docs_page_content)
-
-    return response,docs,evals
-
-
-
-def greet(query):
-
-    answer,sources,evals = get_response_from_query(db,query,2)
-    return answer,sources,evals
-examples = [
-    ["How to be happy"],
-    ["Climate Change Challenges in Europe"],
-    ["Philosophy in the world of Minimalism"],
-    ["Hate Speech  vs Freedom of Speech"],
-    ["Articles by Noam Chomsky on US Politics"],
-    ["The importance of values and reflection"]
-    ]
-demo = gr.Interface(fn=greet, title="cicero-semantic-search", inputs="text",
-                    outputs=[gr.components.Textbox(lines=3, label="Response"),
-                             gr.components.Textbox(lines=3, label="Source"),
-                             gr.components.Textbox(lines=3, label="Evaluation")],
-                   examples=examples)
-
-demo.launch(share=True)