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from langchain.retrievers.multi_query import MultiQueryRetriever
from langchain_groq import ChatGroq
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate, PromptTemplate
from langchain_core.pydantic_v1 import BaseModel
from langchain_core.runnables import RunnableParallel, RunnablePassthrough
import getpass
import os
model = ChatGroq(
model_name="llama-3.1-70b-versatile",
temperature=0.5,
max_tokens=None,
timeout=None,
max_retries=2,
)
from PyPDF2 import PdfReader
import docx
def read_text_from_file(filepath):
if filepath.endswith('.pdf'):
return read_pdf(filepath)
elif filepath.endswith('.docx'):
return read_docx(filepath)
elif filepath.endswith('.txt'):
return read_txt(filepath)
else:
raise ValueError("Unsupported file format. Please provide a .pdf, .docx, or .txt file.")
def read_pdf(filepath):
paragraphs = []
with open(filepath, 'rb') as file:
reader = PdfReader(file)
for page in reader.pages:
text = page.extract_text()
if text:
paragraphs.extend(text.split('\n\n')) # Splitting by double newlines to get paragraphs
return paragraphs
def read_docx(filepath):
doc = docx.Document(filepath)
paragraphs = [paragraph.text for paragraph in doc.paragraphs if paragraph.text.strip() != ""]
return paragraphs
def read_txt(filepath):
with open(filepath, 'r', encoding='utf-8') as file:
text = file.read()
paragraphs = text.split('\n\n') # Splitting by double newlines to get paragraphs
return paragraphs
template = ChatPromptTemplate.from_template("""
###TASK###
You are a native Urdu speaker and an expert translator.
Your task is to translate the given text into Urdu, ensuring the use of vocabulary and expressions that a native speaker would use. No preamble and nothing else should be output.
No matter what the original tone was, make sure to translate in the given tone.
###TONE TO FOLLOW###
{tone}
###TEXT TO TRANSLATE###
{paragraph}
""")
def get_translation(filepath, tone):
paragraphs = read_text_from_file(filepath)
output_text = ""
for paragraph in paragraphs:
prompt = template.invoke({"tone": tone, 'paragraph': paragraph})
translation = model.invoke(prompt)
output_text += translation.content + '\n\n'
print(translation.content)
return output_text
import gradio as gr
# Gradio interface function
def gradio_interface(file, tone):
# Save the uploaded file to a temporary location
filepath = file.name
# Call the translation function with the file path and tone
result = get_translation(filepath, tone)
return result
# Define the Gradio interface
iface = gr.Interface(
fn=gradio_interface,
inputs=[gr.File(label="Upload a Text File"), gr.Textbox(label="Enter Tone")],
outputs=gr.Textbox(label="Translated Text Output"),
title="Translate your documents"
)
# Launch the interface
iface.launch() |