Delete pages/😋_food_and_nutrition_chatbot.py

pages/😋_food_and_nutrition_chatbot.py

@@ -1,111 +0,0 @@
-#Import the necessary libraries
-import utils
-import streamlit as st
-from streaming import StreamHandler
-
-from langchain_openai import ChatOpenAI
-from langchain.chains.conversation.base import ConversationChain
-from langchain.memory.buffer import ConversationBufferMemory
-from langchain.memory import ConversationSummaryMemory
-from langchain.chains.conversational_retrieval.base import ConversationalRetrievalChain
-from langchain.prompts import PromptTemplate
-from langchain_community.document_loaders import TextLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_chroma import Chroma
-from langchain_openai import OpenAIEmbeddings
-
-st.set_page_config(page_title="Food and Nutrition chatbot", page_icon="😋")
-st.header('Sickle cell chatbot')
-st.write('Enhancing Chatbot Interactions through sickle cell training')
-
-class ContextChatbot:
-
-    def __init__(self):
-        self.openai_model = utils.configure_openai()
-
-    @st.cache_resource
-    def setup_chain(_self):
-        file_paths = [
-            './Docs/Prenatal Diagonosis.txt',
-            './Docs/Sickle Cell Anaemia Yoruba.txt',
-            './Docs/Sickle Cell Anemia.txt',
-            './Docs/Sickle cell in Africa.txt',
-            './Docs/sickle-cell-disease-report.txt',
-            './Docs/Blood Basics.txt',
-            './Docs/Sickle Cell Basic Info.txt',
-            './Docs/Manging Sicklecell.txt',
-            './Docs/SickleCell_nutrition.txt',
-            './Docs/Lifestyle Advice.txt'
-        ]
-
-        # Load documents
-        documents = []
-        for path in file_paths:
-            try:
-                loader = TextLoader(path)
-                documents.extend(loader.load())
-                print(f"Successfully loaded: {path}")
-            except Exception as e:
-                print(f"Error loading {path}: {e}")
-
-                # Check if any documents were loaded
-        if not documents:
-            raise RuntimeError("No documents were loaded. Please check the file paths and file contents.")
-        text_splitter = RecursiveCharacterTextSplitter(
-            chunk_size=650, chunk_overlap=60, add_start_index=True
-        )
-        openai_embedding_model = OpenAIEmbeddings(
-            model="text-embedding-ada-002",
-        )
-        all_splits = text_splitter.split_documents(documents)
-        vectorstore = Chroma.from_documents(documents=all_splits, embedding=openai_embedding_model, persist_directory='./Vectorstore/')
-        prompt_template = """
-        Use the following pieces of context to answer the user's question about sickle cell.
-        If you don't know the answer, just say that you don't know, don't try to make up an answer.
-        ----------------
-        {context}
-        Question: {question}
-        """
-
-        PROMPT = PromptTemplate(
-            template=prompt_template, input_variables=["context", "question"]
-        )
-        chain_type_kwargs = {"prompt": PROMPT}
-        summarizer = ChatOpenAI(model_name= "gpt-4o", temperature=0, streaming=True)
-        llm = ChatOpenAI(model_name=_self.openai_model, temperature=0, streaming=True)
-
-        chat_history = []
-
-        qa = ConversationalRetrievalChain.from_llm(
-            llm = llm,
-            chain_type = "stuff",
-            memory = ConversationSummaryMemory(llm = summarizer, memory_key='chat_history', input_key='question', output_key= 'answer', return_messages=True),
-            retriever = vectorstore.as_retriever(k = 5, search_type="mmr"),
-            return_source_documents=True,
-            combine_docs_chain_kwargs=chain_type_kwargs
-        )
-        return qa
-
-    @utils.enable_chat_history
-    def main(self):
-        chain = self.setup_chain()
-        user_query = st.chat_input(placeholder="Ask me anything!")
-        if user_query:
-            utils.display_msg(user_query, 'user')
-            with st.chat_message("assistant"):
-                st_cb = StreamHandler(st.empty())
-                result = chain.invoke(
-                    {"question":user_query},
-                    {"callbacks": [st_cb]}
-                )
-                #response = ""
-                #for source in result['source_documents']:
-                    #response += source.metadata['source']
-                    #response += "Content: " + source.page_content
-                response = result["answer"]
-                st.session_state.messages.append({"role": "assistant", "content": response})
-
-if __name__ == "__main__":
-    obj = ContextChatbot()
-    obj.main()
-#SickleCell