Delete app.py

app.py

@@ -1,80 +0,0 @@
-
-# import libraries
-import os
-from dotenv import load_dotenv
-
-import pinecone
-from langchain.document_loaders import PyPDFDirectoryLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain_pinecone import PineconeVectorStore
-from langchain.prompts import PromptTemplate
-from langchain.chains.question_answering import load_qa_chain
-from ctransformers import CTransformers
-
-load_dotenv()
-
-embeddings = HuggingFaceBgeEmbeddings(model_name = 'sentence-transformers/all-MiniLM-L6-v2',
-                                          model_kwargs = {'device':'cpu'})
-
-os.environ['PINECONE_API_KEY'] = 'afb0bb4d-3c15-461b-91a4-fb12fb1f25f2'
-index_name = 'harisonvecot'
-
-vectorstore = PineconeVectorStore(index_name=index_name,embedding=embeddings)
-
-# Create the vector index from documents
-def create_index(documents):
-    vectorstore.add_documents(documents)
-
-# Retrieve query from Pinecone
-def retrieve_query(query, k=2):
-    matching_results = vectorstore.similarity_search(query, k=k)
-    return matching_results
-
-# Custom prompt template
-custom_prompt_template = '''
-use the following pieces of information to answer the user's questions.
-If you don't know the answer, please just say that you don't know the answer, don't try to make up an answer.
-
-Content : {context}
-Question : {question}
-
-only return the helpful answer below and nothing else.
-'''
-
-def set_custom_prompt():
-    prompt = PromptTemplate(template=custom_prompt_template, input_variables=['context', 'question'])
-    return prompt
-
-# Load LLM model
-llm_model = CTransformers(model_name='TheBloke/Llama-2-7B-Chat-GGML',
-                          model_type = 'llama',
-                          max_new_token = 512,
-                          temperature=0.5)
-
-# Create retrieval QA chain
-def retrieval_qa_chain():
-    prompt = set_custom_prompt()
-    chain = load_qa_chain(llm_model, chain_type='stuff', prompt=prompt)
-    return chain
-
-# Search answers from Vector DB
-def retrieve_answer(query):
-    doc_search = retrieve_query(query)
-    chain = retrieval_qa_chain()
-    response = chain.run(input_documents=doc_search, question=query)
-    return response
-
-queries = st.text_input('write a medical questions ?')
-# Example usage
-submit = st.button('submit')
-    # Read and process documents
-#     doc = read_doc('documents/')
-#     documents = chunk_data(docs=doc)
-#     create_index(documents)
-if submit :
-    if queries :
-    # Query and get answer
-    #our_query = 'What is cause of Eczema?'
-        answer = retrieve_answer(queries)
-        st.write(answer)