Update app.py

app.py

@@ -6,9 +6,33 @@ import theme
 theme = theme.Theme()
 
 
+import os
+import sys
+sys.path.append('../..')
 
+#langchain
+from langchain.text_splitter import RecursiveCharacterTextSplitter, CharacterTextSplitter
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.prompts import PromptTemplate
+from langchain.chains import RetrievalQA
+from langchain.prompts import ChatPromptTemplate
+from langchain.schema import StrOutputParser
+from langchain.schema.runnable import Runnable
+from langchain.schema.runnable.config import RunnableConfig
+from langchain.chains import (
+    LLMChain, ConversationalRetrievalChain)
+from langchain.vectorstores import Chroma
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import LLMChain
+from langchain.prompts.prompt import PromptTemplate
+from langchain.prompts.chat import ChatPromptTemplate, SystemMessagePromptTemplate
+from langchain.prompts import SystemMessagePromptTemplate, HumanMessagePromptTemplate, ChatPromptTemplate,  MessagesPlaceholder
+from langchain.document_loaders import PyPDFDirectoryLoader
 
+from langchain_community.llms import HuggingFaceHub
 
+from pydantic import BaseModel
+import shutil
 
 
 
@@ -29,11 +53,81 @@ image_gradio_app = gr.Interface(
 
 # Cell 2: Chatbot Model
 
-def echo(message, history):
-    return message
+loader = PyPDFDirectoryLoader('pdfs')
+data=loader.load()
+# split documents
+text_splitter = RecursiveCharacterTextSplitter(
+    chunk_size=500,
+    chunk_overlap=70,
+    length_function=len
+)
+docs = text_splitter.split_documents(data)
+# define embedding
+embeddings = HuggingFaceEmbeddings(model_name='thenlper/gte-small')
+# create vector database from data
+persist_directory = 'docs/chroma/'
+
+# Remove old database files if any
+shutil.rmtree(persist_directory, ignore_errors=True)
+vectordb = Chroma.from_documents(
+    documents=docs,
+    embedding=embeddings,
+    persist_directory=persist_directory
+)
+# define retriever
+retriever = vectordb.as_retriever(search_type="mmr")
+template = """
+Your name is Greta and you are a recycling chatbot with the objective to anwer questions from user in English or Spanish /
+Use the following pieces of context to answer the question if the question is related with recycling /
+No more than two chunks of context /
+Answer in the same language of the question /
+Always say "thanks for asking!" at the end of the answer /
+If the context is not relevant, please answer the question by using your own knowledge about the topic.
+        
+context: {context}
+question: {question}
+"""
+
+# Create the chat prompt templates
+system_prompt = SystemMessagePromptTemplate.from_template(template)
+qa_prompt = ChatPromptTemplate(
+	messages=[
+		system_prompt,
+        MessagesPlaceholder(variable_name="chat_history"), 
+		HumanMessagePromptTemplate.from_template("{question}")
+    ]
+)
+llm = HuggingFaceHub(
+    repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
+    task="text-generation",
+    model_kwargs={
+        "max_new_tokens": 1024,
+        "top_k": 30,
+        "temperature": 0.1,
+        "repetition_penalty": 1.03,
+    },
+)
+
+memory = ConversationBufferMemory(llm=llm, memory_key="chat_history", input_key='question', output_key='answer', return_messages=True)
+
+qa_chain = ConversationalRetrievalChain.from_llm(
+    llm = llm,
+    memory = memory,
+    retriever = retriever,
+    verbose = True,
+    combine_docs_chain_kwargs={'prompt': qa_prompt},
+    get_chat_history = lambda h : h,
+    rephrase_question = False,
+    output_key = 'answer'
+)
+
+def chat_interface(question,history):
+
+    result = qa_chain.invoke({"question": question})
+    return result['answer']  # If the result is a string, return it directly
 
 chatbot_gradio_app = gr.ChatInterface(
-    fn=echo,
+    fn=chat_interface,
     title='Green Greta'
 )