Create app.py

app.py

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+import gradio as gr
+from langchain import PromptTemplate, LLMChain
+from langchain_huggingface import HuggingFaceEndpoint
+import os
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.schema import Document
+from langchain_community.vectorstores import Qdrant
+from langchain.chains import RetrievalQA
+from langchain.llms import HuggingFaceHub
+
+# Set up the RetrievalQA model
+with open("brookline_data.txt", "r") as f:
+    data = f.read()
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=100, chunk_overlap=20)
+splitted_data = text_splitter.split_text(data)
+embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-mpnet-base-v2')
+retriever = Qdrant.as_retriever()
+
+llm = HuggingFaceHub(repo_id="ahmadmac/Trained-T5-large", model_kwargs={"temperature": 0.5, "max_length": 512},huggingfacehub_api_token=)
+qna = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever)
+prompt_template = """ you are a highly knowledgeable AI assistant. Engage in a conversation with the user. Your main goal is to provide clear and informative answers to the user's questions.
+User: {question}
+Assistant:"""
+prompt = PromptTemplate(template=prompt_template, input_variables=["question"])
+chain = LLMChain(llm=llm, prompt=prompt)
+
+def chatbot(question, chat_history):
+    result = qna(question)
+    if result['result']:
+        return result['result']
+
+    response = chain.run(question)
+    return response
+
+demo = gr.ChatInterface(
+    fn=chatbot,
+    title="Chatbot",
+    description="AI Assistant!!"
+)
+demo.launch()