%%writefile app.py
import os
import locale
import transformers
import torch
import json
import logging
from typing import List
from huggingface_hub import login
from huggingface_hub import InferenceClient
from langchain_openai import AzureChatOpenAI
import gradio as gr
from langchain.chains import LLMSummarizationCheckerChain
import os
import re
from typing import Set, List, Tuple
huggingface_key = os.getenv('HUGGINGFACE_KEY')
login(huggingface_key) # Huggingface api token

# Configure logging
logging.basicConfig(filename='factchecking.log', level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')
class FactChecking:

    def __init__(self):
        
        self.llm = AzureChatOpenAI(
          azure_deployment = "ChatGPT"
        )

        self.client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")

    def format_prompt(self, question: str) -> str:
        """
        Formats the input question into a specific structure for text generation.

        Args:
            question (str): The user's question to be formatted.

        Returns:
            str: The formatted prompt including instructions and the question.
        """
        # Combine the instruction template with the user's question
        prompt = f"[INST] you are the ai assitant your task is answr for the user question[/INST]"
        prompt1 = f"[INST] {question} [/INST]"
        return prompt+prompt1

    def mixtral_response(self,prompt, temperature=0.9, max_new_tokens=5000, top_p=0.95, repetition_penalty=1.0):
        """
        Generates a response to the given prompt using text generation parameters.

        Args:
            prompt (str): The user's question.
            temperature (float): Controls randomness in response generation.
            max_new_tokens (int): The maximum number of tokens to generate.
            top_p (float): Nucleus sampling parameter controlling diversity.
            repetition_penalty (float): Penalty for repeating tokens.

        Returns:
            str: The generated response to the input prompt.
        """

        # Adjust temperature and top_p values within acceptable ranges
        temperature = float(temperature)
        if temperature < 1e-2:
            temperature = 1e-2
        top_p = float(top_p)

        generate_kwargs = dict(
            temperature=temperature,
            max_new_tokens=max_new_tokens,
            top_p=top_p,
            repetition_penalty=repetition_penalty,
            do_sample=True,
            seed=42,
        )
        # Simulating a call to a client's text generation API
        formatted_prompt =self.format_prompt(prompt)
        stream =self.client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
        output = ""

        for response in stream:
            output += response.token.text
       
        return output.replace("</s>","")

    def extract_unique_sentences(self, text: str) -> Set[str]:
        """
        Extracts unique sentences from the given text.

        Args:
            text (str): The input text.

        Returns:
            Set[str]: A set containing unique sentences.
        """
        try:
          # Tokenize the text into sentences using regex
          sentences = re.split(r'(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?)\s', text)
          logging.info("Sentence extraction completed successfully.")
          # Return a list of sentences
          return sentences
        except Exception as e:
            logging.error(f"Error occurred in extract_unique_sentences: {e}")
            return set()

    def find_different_sentences(self, text1: str, text2: str) -> List[Tuple[str, str]]:
        """
        Finds sentences that are different between two texts.

        Args:
            text1 (str): The first text.
            text2 (str): The second text.

        Returns:
            List[Tuple[str, str]]: A list of tuples containing sentences and their labels.
        """
        try:
          sentences_text1 = self.extract_unique_sentences(text1)
          sentences_text2 = self.extract_unique_sentences(text2)
          # Initialize labels list
          labels = []
          # Iterate over sentences in text1
          for sentence in sentences_text1:
              if sentence in sentences_text2:
                  # If sentence is common to both texts, assign 'factual' label
                  labels.append((sentence, 'factual'))
              else:
                  # If sentence is unique to text1, assign 'hallucinated' label
                  labels.append((sentence, 'hallucinated'))
          logging.info("Sentence comparison completed successfully.")
          return labels
        except Exception as e:
            logging.error(f"Error occurred in find_different_sentences: {e}")
            return []

    def extract_words(self, text: str) -> List[str]:
        """
        Extracts words from the input text.

        Parameters:
            text (str): The input text.

        Returns:
            List[str]: A list containing the extracted words.
        """
        try:
          # Tokenize the text into words and non-word characters (including spaces) using regex
          chunks = re.findall(r'\b\w+\b|\W+', text)
          logging.info("Words extracted successfully.")
        except Exception as e:
            logging.error(f"An error occurred while extracting words: {str(e)}")
            return []
        else:
            return chunks

    def label_words(self, text1: str, text2: str) -> List[Tuple[str, str]]:
        """
        Labels words in text1 as 'factual' if they are present in text2, otherwise 'hallucinated'.

        Parameters:
            text1 (str): The first text.
            text2 (str): The second text.

        Returns:
            List[Tuple[str, str]]: A list of tuples containing words from text1 and their labels.
        """
        try:
          # Extract chunks from both texts
          chunks_text1 = self.extract_words(text1)
          chunks_text2 = self.extract_words(text2)
          # Convert chunks_text2 into a set for faster lookup
          chunks_set_text2 = set(chunks_text2)
          # Initialize labels list
          labels = []
          # Iterate over chunks in text1
          for chunk in chunks_text1:
              # Check if chunk is present in text2
              if chunk in chunks_set_text2:
                  labels.append((chunk, 'factual'))
              else:
                  labels.append((chunk, 'hallucinated'))
          logging.info("Words labeled successfully.")
          return labels
        except Exception as e:
            logging.error(f"An error occurred while labeling words: {str(e)}")
            return []

    def find_hallucinatted_sentence(self, question: str) -> Tuple[str, List[str]]:
        """
        Finds hallucinated sentences in response to a given question.

        Args:
            question (str): The input question.

        Returns:
            Tuple[str, List[str]]: A tuple containing the original llama_result and a list of hallucinated sentences.
        """
        try:

          # Generate initial response using contract generator
          mixtral_response = self.mixtral_response(question)

          # Create checker chain for summarization checking
          checker_chain = LLMSummarizationCheckerChain.from_llm(self.llm, verbose=True, max_checks=2)

          # Run fact checking on the generated result
          fact_checking_result = checker_chain.run(mixtral_response)

          # Find different sentences between original result and fact checking result
          prediction_list = self.find_different_sentences(mixtral_response, fact_checking_result)

          #word prediction list
          word_prediction_list = self.label_words(mixtral_response, fact_checking_result)

          logging.info("Sentences comparison completed successfully.")
          # Return the original result and list of hallucinated sentences
          return mixtral_response,fact_checking_result,prediction_list,word_prediction_list

        except Exception as e:
            logging.error(f"Error occurred in find_hallucinatted_sentence: {e}")
            return "", []

    def interface(self):
      css=""".gradio-container {background: rgb(157,228,255);
        background: radial-gradient(circle, rgba(157,228,255,1) 0%, rgba(18,115,106,1) 100%);}"""

      with gr.Blocks(css=css) as demo:
        gr.HTML("""
            <center><h1 style="color:#fff">Detect Hallucination</h1></center>""")
        with gr.Row():
          question = gr.Textbox(label="Question")
        with gr.Row():
          button = gr.Button(value="Submit")
        with gr.Row():
          with gr.Column(scale=0.50):
            mixtral_response = gr.Textbox(label="llm answer")
          with gr.Column(scale=0.50):
            fact_checking_result = gr.Textbox(label="Corrected Result")
        with gr.Row():
          with gr.Column(scale=0.50):
            highlighted_prediction = gr.HighlightedText(
                                  label="Sentence Hallucination detection",
                                  combine_adjacent=True,
                                  color_map={"hallucinated": "red", "factual": "green"},
                                  show_legend=True)
          with gr.Column(scale=0.50):
            word_highlighted_prediction = gr.HighlightedText(
                                  label="Word Hallucination detection",
                                  combine_adjacent=True,
                                  color_map={"hallucinated": "red", "factual": "green"},
                                  show_legend=True)
        button.click(self.find_hallucinatted_sentence,question,[mixtral_response,fact_checking_result,highlighted_prediction,word_highlighted_prediction])
      demo.launch(debug=True)


hallucination_detection = FactChecking()
hallucination_detection.interface()