# app.py """ ========================================================= 1) IMPORTS & DEPENDENCIES ========================================================= """ import gradio as gr import torch import theme theme = theme.Theme() from huggingface_hub import from_pretrained_keras from tensorflow.keras.applications import EfficientNetB0 import tensorflow as tf from tensorflow import keras from PIL import Image import shutil import tenacity # for retrying failed requests from fake_useragent import UserAgent # LangChain from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.embeddings import HuggingFaceEmbeddings from langchain.prompts import PromptTemplate from langchain.schema import StrOutputParser from langchain.schema.runnable import Runnable from langchain.schema.runnable.config import RunnableConfig from langchain.chains import RetrievalQA, ConversationalRetrievalChain, LLMChain from langchain.prompts.chat import ChatPromptTemplate, SystemMessagePromptTemplate from langchain.prompts import SystemMessagePromptTemplate, HumanMessagePromptTemplate, ChatPromptTemplate, MessagesPlaceholder from langchain.output_parsers import PydanticOutputParser from langchain_community.llms import HuggingFaceHub from langchain_community.document_loaders import WebBaseLoader from langchain.vectorstores import Chroma from langchain.memory import ConversationBufferMemory from pydantic.v1 import BaseModel, Field # Import the separate file that contains our list of URLs from url_list import URLS """ ========================================================= 2) IMAGE CLASSIFICATION MODEL SETUP ========================================================= """ # Load a Keras model from HuggingFace Hub model1 = from_pretrained_keras("rocioadlc/efficientnetB0_trash") # Define class labels for the trash classification class_labels = ['cardboard', 'glass', 'metal', 'paper', 'plastic', 'trash'] def predict_image(input_image): """ Resize the user-uploaded image and preprocess it so that it can be fed into the EfficientNetB0 model. The model then returns a dictionary of class probabilities. """ # Resize the image (note the target dimensions) image_array = tf.keras.preprocessing.image.img_to_array( input_image.resize((244, 224)) ) # Normalize/prescale the image for EfficientNet image_array = tf.keras.applications.efficientnet.preprocess_input(image_array) # Expand the dimensions to create a batch of size 1 image_array = tf.expand_dims(image_array, 0) # Get predictions predictions = model1.predict(image_array) # Convert predictions into a dictionary {class_label: score} category_scores = {} for i, class_label in enumerate(class_labels): category_scores[class_label] = predictions[0][i].item() return category_scores # Gradio interface for image classification image_gradio_app = gr.Interface( fn=predict_image, inputs=gr.Image(label="Image", sources=['upload', 'webcam'], type="pil"), outputs=[gr.Label(label="Result")], title="Green Greta", theme=theme ) """ ========================================================= 3) CHATBOT MODEL SETUP ========================================================= """ # 3.1) Define user agent to avoid blocking, etc. user_agent = UserAgent().random header_template = {"User-Agent": user_agent} @tenacity.retry( wait=tenacity.wait_fixed(3), # wait 3 seconds between retries stop=tenacity.stop_after_attempt(3), # stop after 3 attempts reraise=True ) def load_url(url): """ Use the WebBaseLoader for a single URL. The function is retried if it fails due to connection issues. """ loader = WebBaseLoader( web_paths=[url], header_template=header_template ) return loader.load() def safe_load_all_urls(urls): """ Safely load documents from a list of URLs. Any URL that fails after the specified number of retries is skipped. """ all_docs = [] for link in urls: try: docs = load_url(link) all_docs.extend(docs) except Exception as e: # If load_url fails after all retries, skip that URL print(f"Skipping URL due to error: {link}\nError: {e}\n") return all_docs # 3.2) Actually load the data from all URLs (imported from url_list.py) all_loaded_docs = safe_load_all_urls(URLS) # 3.3) Split the documents into manageable chunks text_splitter = RecursiveCharacterTextSplitter( chunk_size=1024, chunk_overlap=150, length_function=len ) docs = text_splitter.split_documents(all_loaded_docs) # 3.4) Create embeddings embeddings = HuggingFaceEmbeddings(model_name='thenlper/gte-small') # 3.5) Create a persistent directory to store vector DB persist_directory = 'docs/chroma/' shutil.rmtree(persist_directory, ignore_errors=True) # remove old DB files # 3.6) Build Chroma vector store vectordb = Chroma.from_documents( documents=docs, embedding=embeddings, persist_directory=persist_directory ) # 3.7) Create a retriever retriever = vectordb.as_retriever( search_kwargs={"k": 2}, search_type="mmr" ) """ ========================================================= 4) PROMPT & CHAIN SETUP ========================================================= """ # 4.1) Define the schema for final chatbot answers class FinalAnswer(BaseModel): question: str = Field() answer: str = Field() parser = PydanticOutputParser(pydantic_object=FinalAnswer) # 4.2) Prompt template: system instructions template = """ Your name is Greta and you are a recycling chatbot with the objective to answer questions from user in English or Spanish / Has sido diseñado y creado por el Grupo 1 del Máster en Data Science & Big Data de la promoción 2023/2024 de la Universidad Complutense de Madrid. Este grupo está formado por Rocío, María Guillermo, Alejandra, Paloma y Álvaro / 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} """ sys_prompt = SystemMessagePromptTemplate.from_template(template) qa_prompt = ChatPromptTemplate( messages=[ sys_prompt, HumanMessagePromptTemplate.from_template("{question}") ], partial_variables={"format_instructions": parser.get_format_instructions()} ) # 4.3) Define the LLM from HuggingFace 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 }, ) # 4.4) Create a ConversationalRetrievalChain that uses the above LLM 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, # pass memory directly rephrase_question=False, output_key='output' ) def chat_interface(question, history): """ This function processes the user's question through the qa_chain, then parses out the final answer from the chain's output. """ 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":') 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('"') answer_value = answer_part[quote_index + 1:answer_part.find('"', quote_index + 1)] return answer_value # Gradio chat interface for the chatbot chatbot_gradio_app = gr.ChatInterface( fn=chat_interface, title="Green Greta" ) """ ========================================================= 5) BANNER / WELCOME TAB ========================================================= """ banner_tab_content = """
Banner Image

¡Bienvenido a nuestro clasificador de imágenes y chatbot para un reciclaje más inteligente!♻️

¿Alguna vez te has preguntado si puedes reciclar un objeto en particular? ¿O te has sentido abrumado por la cantidad de residuos que generas y no sabes cómo manejarlos de manera más sostenible? ¡Estás en el lugar correcto!

Nuestra plataforma combina la potencia de la inteligencia artificial con la comodidad de un chatbot para brindarte respuestas rápidas y precisas sobre qué objetos son reciclables y cómo hacerlo de la manera más eficiente.

¿Cómo usarlo?

Welcome to our image classifier and chatbot for smarter recycling!♻️

Have you ever wondered if you can recycle a particular object? Or felt overwhelmed by the amount of waste you generate and don't know how to handle it more sustainably? You're in the right place!

Our platform combines the power of artificial intelligence with the convenience of a chatbot to provide you with quick and accurate answers about which objects are recyclable and how to do it most efficiently.

How to use it?

""" banner_tab = gr.Markdown(banner_tab_content) """ ========================================================= 6) GRADIO FINAL APP: TABS ========================================================= """ app = gr.TabbedInterface( [banner_tab, image_gradio_app, chatbot_gradio_app], tab_names=["Welcome to Green Greta", "Green Greta Image Classification", "Green Greta Chat"], theme=theme ) # Enable queue() for concurrency and launch the Gradio app app.queue() app.launch()