app.py

import pandas as pd
import nltk
import os

import langchain
from langchain.schema import Document
from langchain.document_loaders import TextLoader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.text_splitter import CharacterTextSplitter
from langchain import OpenAI, VectorDBQA
from langchain.document_loaders import DirectoryLoader
from langchain.document_loaders import UnstructuredURLLoader
from langchain.document_loaders import UnstructuredFileLoader
from langchain.chains.conversation.memory import ConversationBufferMemory
from langchain.chains import RetrievalQA
from langchain.document_loaders import OnlinePDFLoader
from langchain.llms import OpenAIChat
from langchain.vectorstores import DeepLake

from langchain.document_loaders import SeleniumURLLoader

from langchain.chains import ConversationalRetrievalChain
from langchain.document_loaders.csv_loader import CSVLoader
from langchain.vectorstores import FAISS
import tempfile

from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import FAISS

from langchain.chains.question_answering import load_qa_chain
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.memory import ConversationBufferMemory


# Set your OpenAI API key
openai_api_key = os.environ['OPENAI_API_KEY']

# Set your organization key
organization_key = os.environ['OPENAI_ORG_KEY']

embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key, organization_key=organization_key)
docsearch = FAISS.load_local("faiss_index_yt", embeddings)


template = """You are a virtual assistant discussing Toyota vehicles.
Please respond to our clients in a good way, Intelligently generate a welcoming phrase. 
When discussing about toyota vehicles information or any recomendation, provide accurate information from this knowledge
base: {context}. If a question falls outside this document's scope, kindly reply with 'I'm sorry, but 
the available information is limited as I am an AI assistant.'
{chat_history} Human: {human_input} Virtual Assistant:"""




prompt = PromptTemplate(
    input_variables=["chat_history", "human_input", "context"], template=template
)
memory = ConversationBufferMemory(memory_key="chat_history", input_key="human_input", max_history=2)
chain = load_qa_chain(
    OpenAI(temperature=0.3), chain_type="stuff", memory=memory, prompt=prompt
)

# updated code 
import gradio as gr
import requests
import nest_asyncio
import re

# Function to get the image URL from an image search API
def get_image_url(query):
    query = re.sub(r'[^\w\s]', '', query)

    tundra_images = dict([('trd', 'https://www.toyota.com/imgix/content/dam/toyota/jellies/max/2023/tundra/8424.png?bg=fff&fm=webp&q=90&w=1764'), ('limited', 'https://www.toyota.com/imgix/content/dam/toyota/jellies/max/2023/tundra/8372.png?bg=fff&fm=webp&q=90&w=1764'), ('sr', 'https://www.toyota.com/imgix/content/dam/toyota/jellies/max/2023/tundra/8342.png?bg=fff&fm=webp&q=90&w=1764'),
                          ('sr5', 'https://www.toyota.com/imgix/content/dam/toyota/jellies/max/2023/tundra/8361.png?bg=fff&fm=webp&q=90&w=1764'), ('platinum', 'https://www.toyota.com/imgix/content/dam/toyota/jellies/max/2023/tundra/8375.png?bg=fff&fm=webp&q=90&w=1764'), ('1794', 'https://www.toyota.com/imgix/content/dam/toyota/jellies/max/2023/tundra/8386.png?bg=fff&fm=webp&q=90&w=1764'),
                          ('capstone', 'https://www.toyota.com/imgix/content/dam/toyota/jellies/max/2023/tundra/8425.png?bg=fff&fm=webp&q=90&w=1764')])
    model_names = ['sr','sr5','trd','platinum','limited','capstone','1794 edition']

    # Split the query into words
    words = query.lower().split()

    # Find the model names in the list using list comprehension
    found_models = [model for model in model_names if model in words ]

    # Get URLs and names of found models
    model_info = []
    for model in found_models:
        model_info.append((tundra_images[model], model))

    return model_info

with gr.Blocks() as demo:
    chatbot = gr.Chatbot()
    msg = gr.Textbox()
    clear = gr.Button("Clear")
    chat_history = []

    def user(user_message, history):
        # Get response from QA chain
        docs = docsearch.similarity_search(user_message)
        output = chain({"input_documents": docs, "human_input": user_message}, return_only_outputs=False)

        model_info = get_image_url(user_message)
        output_text = output['output_text']
        output_text = re.sub(r'^\W+', '', output_text)
        
        # Construct the HTML for displaying images
        images_html = ""
        for image_url, model_name in model_info:
            if image_url:
                image_html = f"<img src='{image_url}'><br><br>"
                images_html += f"Toyota Tundra {model_name.capitalize()} 2023: {image_html}"
        
        # Adding the source link
        if docs[0].page_content == 'nan' or docs[1].page_content == 'nan' or docs[0].page_content == '♪':
            video_link = ""
        else:
            user_query = user_message

            model_year = re.search(r'\d{4}', user_query)
            if model_year:
                model_year = model_year.group()

            
            video_link = f"Source: {output['input_documents'][0].metadata['video_link']}"
            if model_year is not None:
                for i in range(0,len(output['input_documents'])):
                    if model_year in output['input_documents'][i].metadata['title']:
                        video_link = f"Source: {output['input_documents'][i].metadata['video_link']}"
                        break
            
        
        output_text_with_images = f"{output_text}<br>{images_html}{video_link}"
        
        history.append((user_message, output_text_with_images))
        return gr.update(value=""), history

    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False)
    clear.click(lambda: None, None, chatbot, queue=False)

if __name__ == "__main__":

    nest_asyncio.apply()
    demo.launch(debug=True, share=True)