Create app.py

app.py

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+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: "#92b96a";'>Green Greta</span>"
+
+
+from huggingface_hub import from_pretrained_keras
+
+import tensorflow as tf
+from tensorflow import keras
+from PIL import Image
+
+# Cell 1: Image Classification Model
+model1 = from_pretrained_keras("rocioadlc/EfficientNetV2L")
+
+# Define class labels
+class_labels = ['battery',
+ 'biological',
+ 'brown-glass',
+ 'cardboard',
+ 'clothes',
+ 'green-glass',
+ 'metal',
+ 'paper',
+ 'plastic',
+ 'shoes',
+ 'trash',
+ 'white-glass']
+
+# Function to predict image label and score
+def predict_image(input):
+    # Resize the image to the size expected by the model
+    image = input.resize((244, 224))
+    # Convert the image to a NumPy array
+    image_array = tf.keras.preprocessing.image.img_to_array(image)
+    # Normalize the image
+    image_array /= 255.0
+    # Expand the dimensions to create a batch
+    image_array = tf.expand_dims(image_array, 0)
+    # Predict using the model
+    predictions = model1.predict(image_array)
+    
+    # Get the predicted class label
+    predicted_class_index = tf.argmax(predictions, axis=1).numpy()[0]
+    predicted_class_label = class_labels[predicted_class_index]
+    
+    # Get the confidence score of the predicted class
+    confidence_score = predictions[0][predicted_class_index]
+    
+    # Return predicted class label and confidence score
+    return {predicted_class_label: confidence_score}
+
+
+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
+)
+
+banner_tab_content = """
+<div style="background-color: #d3e3c3; text-align: center; padding: 20px; display: flex; flex-direction: column; align-items: center;">
+    <img src="https://huggingface.co/spaces/rocioadlc/test_4/resolve/main/front_4.jpg" alt="Banner Image" style="width: 50%; max-width: 500px; margin: 0 auto;">
+    <h1 style="font-size: 24px; color: "#92b96a"; margin-top: 20px;">¡Bienvenido a nuestro clasificador de imágenes y chatbot para un reciclaje más inteligente!♻️</h1>
+    <p style="font-size: 16px; color: "#92b96a"; text-align: justify;">¿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!</p>
+    <p style="font-size: 16px; color: "#92b96a"; text-align: justify;">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.</p>
+    <p style="font-size: 16px; text-align:center;"><strong><span style="color: "#92b96a";">¿Cómo usarlo?</span></strong>
+    <ul style="list-style-type: disc; text-align: justify; margin-top: 20px; padding-left: 20px;">
+        <li style="font-size: 16px; color: "#92b96a";"><strong><span style="color: "#92b96a";">Green Greta Image Classification:</span></strong> Ve a la pestaña Greta Image Classification y simplemente carga una foto del objeto que quieras reciclar, y nuestro modelo de identificará de qué se trata🕵️‍♂️ para que puedas desecharlo adecuadamente.</li>
+        <li style="font-size: 16px; color: "#92b96a";"><strong><span style="color: "#92b96a";">Green Greta Chat:</span></strong> ¿Tienes preguntas sobre reciclaje, materiales específicos o prácticas sostenibles? ¡Pregunta a nuestro chatbot en la pestaña Green Greta Chat!📝 Está aquí para responder todas tus preguntas y ayudarte a tomar decisiones más informadas sobre tu reciclaje.</li>
+    </ul>
+</div>
+"""
+banner_tab = gr.Markdown(banner_tab_content)
+
+# Combine both interfaces into a single app
+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
+)
+
+app.queue()
+app.launch()