app.py

from pydantic import NoneStr
import os
from langchain.chains.question_answering import load_qa_chain
from langchain.document_loaders import UnstructuredFileLoader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.llms import OpenAI
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.vectorstores import Chroma
from langchain.chains import ConversationalRetrievalChain
import gradio as gr
import openai
from langchain import PromptTemplate, OpenAI, LLMChain
import validators
import requests
import mimetypes
import tempfile

class Chatbot:
    def __init__(self):
        openai.api_key = os.getenv("OPENAI_API_KEY")
    def get_empty_state(self):

        """ Create empty Knowledge base"""

        return {"knowledge_base": None}

    def create_knowledge_base(self,docs):

        """Create a knowledge base from the given documents.
        Args:
            docs (List[str]): List of documents.
        Returns:
            FAISS: Knowledge base built from the documents.
        """

        # Initialize a CharacterTextSplitter to split the documents into chunks
        # Each chunk has a maximum length of 500 characters
        # There is no overlap between the chunks
        text_splitter = CharacterTextSplitter(
            separator="\n", chunk_size=1000, chunk_overlap=200, length_function=len
        )

        # Split the documents into chunks using the text_splitter
        chunks = text_splitter.split_documents(docs)

        # Initialize an OpenAIEmbeddings model to compute embeddings of the chunks
        embeddings = OpenAIEmbeddings()

        # Build a knowledge base using FAISS from the chunks and their embeddings
        knowledge_base = Chroma.from_documents(chunks, embeddings)

        # Return the resulting knowledge base
        return knowledge_base


    def upload_file(self,file_paths):
        """Upload a file and create a knowledge base from its contents.
        Args:
            file_paths : The files to uploaded.
        Returns:
            tuple: A tuple containing the file name and the knowledge base.
        """

        file_paths = [i.name for i in file_paths]
        print(file_paths)


        loaders = [UnstructuredFileLoader(file_obj, strategy="fast") for file_obj in file_paths]

        # Load the contents of the file using the loader
        docs = []
        for loader in loaders:
            docs.extend(loader.load())

        # Create a knowledge base from the loaded documents using the create_knowledge_base() method
        knowledge_base = self.create_knowledge_base(docs)


        # Return a tuple containing the file name and the knowledge base
        return file_paths, {"knowledge_base": knowledge_base}

    def add_text(self,history, text):
        history = history + [(text, None)]
        return history, gr.update(value="", interactive=False)



    def upload_multiple_urls(self,urls):
        urlss = [url.strip() for url in urls.split(',')]
        all_docs = []
        file_paths = []
        for url in urlss:
            if validators.url(url):
                headers = {'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36',}
                r = requests.get(url,headers=headers)
                if r.status_code != 200:
                    raise ValueError(
                        "Check the url of your file; returned status code %s" % r.status_code
                    )
                content_type = r.headers.get("content-type")
                file_extension = mimetypes.guess_extension(content_type)
                temp_file = tempfile.NamedTemporaryFile(suffix=file_extension, delete=False)
                temp_file.write(r.content)
                file_path = temp_file.name
                file_paths.append(file_path)

        loaders = [UnstructuredFileLoader(file_obj, strategy="fast") for file_obj in file_paths]

        # Load the contents of the file using the loader
        docs = []
        for loader in loaders:
            docs.extend(loader.load())

        # Create a knowledge base from the loaded documents using the create_knowledge_base() method
        knowledge_base = self.create_knowledge_base(docs)

        return file_paths,{"knowledge_base":knowledge_base}

    def answer_question(self, question,history,state):
        """Answer a question based on the current knowledge base.
        Args:
            state (dict): The current state containing the knowledge base.
        Returns:
            str: The answer to the question.
        """


        # Retrieve the knowledge base from the state dictionary
        knowledge_base = state["knowledge_base"]
        retriever = knowledge_base.as_retriever()
        qa = ConversationalRetrievalChain.from_llm(
            llm=OpenAI(temperature=0.5),
            retriever=retriever,
            return_source_documents=False)
        # Set the question for which we want to find the answer
        res = []
        question = history[-1][0]
        for human, ai in history[:-1]:
            pair = (human, ai)
            res.append(pair)

        chat_history = res
        #print(chat_history)
        query = question
        result = qa({"question": query, "chat_history": chat_history})
        # Perform a similarity search on the knowledge base to retrieve relevant documents
        response = result["answer"]
        # Return the response as the answer to the question
        history[-1][1] = response
        return history


    def extract_excel_data(self,file_path):
        # Read the Excel file
        df = pd.read_excel(file_path)

        # Flatten the data to a single list
        data_list = []
        for _, row in df.iterrows():
            data_list.extend(row.tolist())

        return data_list

    def comparing_chemicals(self,excel_file_path,chemicals):
        chemistry_capability = self.extract_excel_data(excel_file_path.name)
        response = openai.Completion.create(
        engine="text-davinci-003",
        prompt= f"""Analyse the following text delimited by triple backticks to return the comman chemicals.
                  text : ```{chemicals}  {chemistry_capability}```.
                  result should be in bullet points format.
                 """,
        max_tokens=100,
        n=1,
        stop=None,
        temperature=0,
        top_p=1.0,
        frequency_penalty=0.0,
        presence_penalty=0.0
        )

        result = response.choices[0].text.strip()
        return result

    def clear_function(self,state):
      state.clear()
      # state = gr.State(self.get_empty_state())

    def gradio_interface(self):

        """Create the Gradio interface for the Chemical Identifier."""

        with gr.Blocks(css="style.css",theme=gr.themes.Soft()) as demo:
          state = gr.State(self.get_empty_state())
          with gr.Column(elem_id="col-container"):
              gr.HTML(
                  """<hr style="border-top: 5px solid white;">"""
                  )
              gr.HTML(
                  """<br>
                  <h1 style="text-align:center;">
                      Multi URL and Doc Chatbot Q&A
                    </h1> """
              )
              gr.HTML(
                  """<hr style="border-top: 5px solid white;">"""
                  )

              gr.Markdown("**Upload your file**")
              with gr.Row(elem_id="row-flex"):
                  with gr.Column(scale=1, min_width=0):
                      file_url = gr.Textbox(label='file url :',show_label=True, placeholder="")
              with gr.Row(elem_id="row-flex"):
                  with gr.Accordion("Upload Files", open = False):
                      with gr.Column(scale=0.90, min_width=160):
                          file_output = gr.File(elem_classes="heightfit")
                      with gr.Column(scale=0.10, min_width=160):
                          upload_button = gr.UploadButton(
                              "Browse File", file_types=[".txt", ".pdf", ".doc", ".docx"],
                              file_count = "multiple")
              with gr.Row():
                chatbot = gr.Chatbot([], elem_id="chatbot")
              with gr.Row():
                    txt = gr.Textbox(
                        label = "Question",
                        show_label=True,
                        placeholder="Enter text and press enter, or upload an image",
                    )
              with gr.Row():
                    clear_btn = gr.Button(value="Clear")

          txt_msg = txt.submit(self.add_text, [chatbot, txt], [chatbot, txt], queue=False).then(
              self.answer_question, [txt,chatbot,state], chatbot
          )
          txt_msg.then(lambda: gr.update(interactive=True), None, [txt], queue=False)

          file_url.submit(self.upload_multiple_urls, file_url, [file_output, state])
          clear_btn.click(self.clear_function,[state],[])
          clear_btn.click(lambda: None, None, chatbot, queue=False)
          upload_button.upload(self.upload_file, upload_button, [file_output,state])
        demo.queue().launch(debug=True)

if __name__=="__main__":
    chatbot = Chatbot()
    chatbot.gradio_interface()