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import gradio as gr
from langchain_community.llms import OpenAI
from langchain.prompts import PromptTemplate
import os
from dotenv import load_dotenv
from langchain_huggingface import HuggingFaceEndpoint
load_dotenv()
system_prompt_1 = """
You are an advanced AI assistant tasked with helping to transcribes given texts into
simplified languages, specifically FALC (Facile à Lire et à Comprendre) and "Leichte Sprache" (Simple Language).
This system is intended to streamline the creation of accessible content for government websites.
Instructions for AI Development:
detect the language of text given then transcribes text into the same language which the guidelines of
FALC (Facile à Lire et à Comprendre) and "Leichte Sprache" (Simple Language) and
accurately transcribe complex texts into simplified language.
Ensure maintaining the context and meaning of the original text while simplifying its language.
text: {text}
transcribes text: """
def translate_text(file, text_input):
repo_id = "mistralai/Mistral-7B-Instruct-v0.2"
# Initialize the HuggingFace endpoint
llm = HuggingFaceEndpoint(
repo_id=repo_id,
max_length=128,
temperature=0.5,
huggingfacehub_api_token=api_token,
)
# Read the input file based on its type
if file.name.endswith('.txt'):
with open(file.name, 'r', encoding='utf-8') as f:
file_text = f.read()
elif file.name.endswith('.rtf'):
import pyth.plugins.rtf15.reader as rtf15_reader
import pyth.plugins.plaintext.writer as plaintext_writer
doc = rtf15_reader.read(f)
file_text = plaintext_writer.write(doc).getvalue()
else:
raise ValueError("Unsupported file type. Please upload a .txt or .rtf file.")
# Define the prompt template and create the LLM chain
prompt = PromptTemplate.from_template(system_prompt_1)
llm_chain = prompt | llm
file_translation = llm_chain.invoke({"text": file_text})
# Determine the output file path and type
if file.name.endswith('.txt'):
output_file_path = "translated_file.txt"
elif file.name.endswith('.rtf'):
output_file_path = "translated_file.rtf"
# Write the translated text to the output file
with open(output_file_path, 'w', encoding='utf-8') as f:
f.write(file_translation)
return file_translation, output_file_path
# Create the Gradio interface
iface = gr.Interface(
fn=translate_text,
inputs=[gr.File(label="Upload Text File")],
outputs=[
gr.Textbox(label="Transcribed Content"),
gr.File(label="Download Translated File")
],
title="Text Transcriber",
description="Upload a .txt or .rtf file to translate its content using LangChain and Mistral-7B-Instruct-v0.2 model with predefined system prompts.",
allow_flagging="never"
)
iface.launch(debug=True)
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