Delete app.py

app.py

@@ -1,163 +0,0 @@
-import gradio as gr
-from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
-import torch
-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 pydantic import BaseModel, Field
-from langchain.output_parsers import PydanticOutputParser
-from langchain_community.llms import HuggingFaceHub
-from langchain_community.document_loaders import WebBaseLoader
-
-from pydantic import BaseModel
-import shutil
-
-
-
-custom_title = "<span style='color: rgb(243, 239, 224);'>Green Greta</span>"
-
-
-# Cell 1: Image Classification Model
-image_pipeline = pipeline(task="image-classification", model="guillen/vit-basura-test1")
-
-def predict_image(input_img):
-    predictions = image_pipeline(input_img)
-    return {p["label"]: p["score"] for p in predictions} 
-
-image_gradio_app = gr.Interface(
-    fn=predict_image,
-    inputs=gr.Image(label="Image", sources=['upload', 'webcam'], type="pil"),
-    outputs=[gr.Label(label="Result")],
-    title=custom_title,
-    theme=theme
-)
-
-loader = WebBaseLoader(["https://www.epa.gov/recycle/frequent-questions-recycling", "https://www.whitehorsedc.gov.uk/vale-of-white-horse-district-council/recycling-rubbish-and-waste/lets-get-real-about-recycling/", "https://www.teimas.com/blog/13-preguntas-y-respuestas-sobre-la-ley-de-residuos-07-2022", "https://www.molok.com/es/blog/gestion-de-residuos-solidos-urbanos-rsu-10-dudas-comunes"])
-data=loader.load()
-# split documents
-text_splitter = RecursiveCharacterTextSplitter(
-    chunk_size=1024,
-    chunk_overlap=150,
-    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_kwargs={"k": 2}, search_type="mmr")
-
-class FinalAnswer(BaseModel):
-    question: str = Field(description="the original question")
-    answer: str = Field(description="the extracted answer")
-
-# Assuming you have a parser for the FinalAnswer class
-parser = PydanticOutputParser(pydantic_object=FinalAnswer)
-
-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 English answer in English /
-If the question is Spanish answer in Spanish /
-Do not mention the word context when you answer a question /
-Answer the question fully and provide as much relevant detail as possible. Do not cut your response short /
-Context: {context}
-User: {question}
-{format_instructions}
-"""
-
-# Create the chat prompt templates
-sys_prompt = SystemMessagePromptTemplate.from_template(template)
-qa_prompt = ChatPromptTemplate(
-    messages=[
-        sys_prompt,
-        HumanMessagePromptTemplate.from_template("{question}")],
-    partial_variables={"format_instructions": parser.get_format_instructions()}
-)
-llm = HuggingFaceHub(
-    repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
-    task="text-generation",
-    model_kwargs={
-        "max_new_tokens": 2000,
-        "top_k": 30,
-        "temperature": 0.1,
-        "repetition_penalty": 1.03
-    },
-)
-
-qa_chain = ConversationalRetrievalChain.from_llm(
-    llm = llm,
-    memory = ConversationBufferMemory(llm=llm, memory_key="chat_history", input_key='question', output_key='output'),
-    retriever = retriever,
-    verbose = True,
-    combine_docs_chain_kwargs={'prompt': qa_prompt},
-    get_chat_history = lambda h : h,
-    rephrase_question = False,
-    output_key = 'output',
-)
-
-def chat_interface(question,history):
-    result = qa_chain.invoke({'question': question})
-    output_string = result['output']
-
-    # Find the index of the last occurrence of "answer": in the string
-    answer_index = output_string.rfind('"answer":')
-
-    # Extract the substring starting from the "answer": index
-    answer_part = output_string[answer_index + len('"answer":'):].strip()
-
-    # Find the next occurrence of a double quote to get the start of the answer value
-    quote_index = answer_part.find('"')
-
-    # Extract the answer value between double quotes
-    answer_value = answer_part[quote_index + 1:answer_part.find('"', quote_index + 1)]
-    
-    return answer_value
-
-
-chatbot_gradio_app = gr.ChatInterface(
-    fn=chat_interface,
-    title=custom_title
-)
-
-# Combine both interfaces into a single app
-app = gr.TabbedInterface(
-    [image_gradio_app, chatbot_gradio_app],
-    tab_names=["Green Greta Image Classification","Green Greta Chat"],
-    theme=theme
-)
-
-app.queue()
-app.launch()