Update app.py

app.py

@@ -2,6 +2,40 @@ import gradio as gr
 from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
 import torch
 
+import os
+import sys
+sys.path.append('../..')
+
+import panel as pn  # GUI
+pn.extension()
+
+from dotenv import load_dotenv, find_dotenv
+_ = load_dotenv(find_dotenv()) # read local .env file
+
+#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
+from langchain.document_loaders import PyPDFDirectoryLoader
+
+from langchain_community.llms import HuggingFaceHub
+
+from pydantic import BaseModel
+import shutil
+
 # Cell 1: Image Classification Model
 image_pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
 
@@ -17,26 +51,77 @@ image_gradio_app = gr.Interface(
 )
 
 # Cell 2: Chatbot Model
-tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
-chatbot_model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
-
-def predict_chatbot(input, history=[]):
-    new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')
-    bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
-    history = chatbot_model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist()
-    response = tokenizer.decode(history[0]).split("")
-
-    response_tuples = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)]
-    return response_tuples, history
-
-chatbot_gradio_app = gr.Interface(
-    fn=predict_chatbot,
-    inputs=gr.Textbox(show_label=False, placeholder="Enter text and press enter"),
-    outputs=gr.Textbox(),
-    live=True,
-    title="Chatbot",
+
+loader = PyPDFDirectoryLoader(r"TFM_DataScience_APP\pdfs")
+data=loader.load()
+# split documents
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=150)
+docs = text_splitter.split_documents(data)
+# define embedding
+embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-l6-v2')
+# 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 AngryGreta and you are a recycling chatbot created to help people. Use the following pieces of context to answer the question at the end. Answer in the same language of the question. Keep the answer as concise as possible. Always say "thanks for asking!" at the end of the answer. 
+CONTEXT: {context}
+CHAT HISTORY:
+{chat_history}
+Question: {question}
+Helpful Answer:"""
+
+# Create the chat prompt templates
+system_prompt = SystemMessagePromptTemplate.from_template(prompt_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": 512,
+    "top_k": 30,
+    "temperature": 0.1,
+    "repetition_penalty": 1.03,
+},
 )
+llm_chain = LLMChain(llm=llm, prompt=qa_prompt)
+
+memory = ConversationBufferMemory(llm=llm, memory_key="chat_history", 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
+)
+
+def qa_response(question, chat_history):
+    # Add user's question to chat history
+    chat_history.append(("User", question))
+
+    # Get response from the conversational retrieval chain
+    response = qa_chain.run(chat_history)
+
+    # Extract and return the assistant's answer from the response
+    assistant_answer = response.get("answer", "I don't know.")
+    return assistant_answer
 
+chatbot_gradio_app = gr.ChatInterface(qa_response)
 
 # Combine both interfaces into a single app
 gr.TabbedInterface(