Upload app.py

app.py

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+
+import os
+from langchain_community.document_loaders import TextLoader
+from langchain.vectorstores import Chroma
+from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
+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",
+    model_kwargs={
+        "max_new_tokens": 512,
+        "top_k": 5,
+        "temperature": 0.1,
+        "repetition_penalty": 1.03,
+    },
+    huggingfacehub_api_token=HF_TOKEN
+)
+template = """
+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:]
+gr.ChatInterface(predict).launch()