Update app.py

app.py

@@ -7,40 +7,13 @@ 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
 
 
 
 
 
 # Cell 1: Image Classification Model
-image_pipeline = pipeline(task="image-classification", model="guillen/vit-basura-test1")
+image_pipeline = pipeline(task="image-classification", model="rocioadlc/TrashNet_ResNet152V2")
 
 def predict_image(input_img):
     predictions = image_pipeline(input_img)
@@ -56,81 +29,11 @@ image_gradio_app = gr.Interface(
 
 # Cell 2: Chatbot Model
 
-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 AngryGreta 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
+def echo(message, history):
+    return message
 
 chatbot_gradio_app = gr.ChatInterface(
-    fn=chat_interface,
+    fn=echo,
     title='Green Greta'
 )