Update app.py

app.py

@@ -1,36 +1,11 @@
 
-import os
-from langchain_community.document_loaders import TextLoader
-from langchain.vectorstores import Chroma
-from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
+
+import gradio as gr
 from langchain_community.llms import HuggingFaceHub
 from langchain.prompts import PromptTemplate
-from langchain.memory import ConversationBufferMemory
-from langchain.chains import ConversationalRetrievalChain
-from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-from langchain_core.output_parsers import StrOutputParser
-from langchain_core.runnables import RunnablePassthrough
-import gradio as gr
-import wandb
 
 # Initialize the chatbot
-loaders = []
-folder_path = "Data"
-for i in range(12):
-    file_path = os.path.join(folder_path,"{}.txt".format(i))
-    loaders.append(TextLoader(file_path))
-docs = []
-for loader in loaders:
-    docs.extend(loader.load())
 HF_TOKEN = os.getenv("HF_TOKEN")
-embeddings = HuggingFaceInferenceAPIEmbeddings(
-    api_key=HF_TOKEN,
-    model_name="sentence-transformers/all-mpnet-base-v2"
-)
-vectordb = Chroma.from_documents(
-    documents=docs,
-    embedding=embeddings
-)
 llm = HuggingFaceHub(
     repo_id="google/gemma-1.1-7b-it",
     task="text-generation",
@@ -47,52 +22,12 @@ You are a Mental Health Chatbot. Help the user with their mental health concerns
 Use the context below to answer the questions {context} 
 Question: {question} 
 Helpful Answer:"""
-        
 QA_CHAIN_PROMPT = PromptTemplate(input_variables=["context", "question"],template=template)
-memory = ConversationBufferMemory(
-    memory_key="chat_history",
-    return_messages=True
-)
-retriever = vectordb.as_retriever()
-qa = ConversationalRetrievalChain.from_llm(
-    llm,
-    retriever=retriever,
-    memory=memory,
-)
-contextualize_q_system_prompt = """
-Given a chat history and the latest user question 
-which might reference context in the chat history, 
-formulate a standalone question 
-which can be understood without the chat history. 
-Do NOT answer the question, just reformulate it if needed and otherwise return it as is."""
-contextualize_q_prompt = ChatPromptTemplate.from_messages(
-    [
-        ("system", contextualize_q_system_prompt),
-        MessagesPlaceholder(variable_name="chat_history"),
-        ("human", "{question}"),
-    ]
-)
-contextualize_q_chain = contextualize_q_prompt | llm | StrOutputParser()
-def contextualized_question(input: dict):
-    if input.get("chat_history"):
-        return contextualize_q_chain
-    else:
-        return input["question"]
-rag_chain = (
-    RunnablePassthrough.assign(
-        context=contextualized_question | retriever
-    )
-    | QA_CHAIN_PROMPT
-    | llm
-)
-wandb.login(key=os.getenv("key"))
-os.environ["LANGCHAIN_WANDB_TRACING"] = "true"
-os.environ["WANDB_PROJECT"] = "Mental_Health_ChatBot"
-print("Welcome to the Mental Health Chatbot. How can I help you today?")
-chat_history = []
+
 def predict(message, history):
-    ai_msg = rag_chain.invoke({"question": message, "chat_history": chat_history})
-    idx = ai_msg.find("Answer")
-    chat_history.extend([HumanMessage(content=message), ai_msg])
-    return ai_msg[idx:]
+    ai_msg = QA_CHAIN_PROMPT.apply({"question": message, "context": history})
+    return ai_msg
+
 gr.ChatInterface(predict).launch()
+
+