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Delete app.py

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  1. app.py +0 -47
app.py DELETED
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- import os
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- import gradio as gr
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- from langchain_core.pydantic_v1 import BaseModel, Field
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- from langchain.prompts import HumanMessagePromptTemplate, ChatPromptTemplate
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- from langchain.output_parsers import PydanticOutputParser
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- from langchain_openai import ChatOpenAI
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-
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- # define the model instance
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- chat = ChatOpenAI()
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-
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- class TextTranslator(BaseModel):
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- output: str = Field(description="Python string containing the output text translated in the desired language")
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-
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- output_parser = PydanticOutputParser(pydantic_object=TextTranslator)
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- format_instructions = output_parser.get_format_instructions()
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-
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- def text_translator(input_text : str, language : str) -> str:
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- human_template = """Enter the text that you want to translate:
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- {input_text}, and enter the language that you want it to translate to {language}. {format_instructions}"""
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- human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
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-
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- chat_prompt = ChatPromptTemplate.from_messages([human_message_prompt])
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-
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- prompt = chat_prompt.format_prompt(input_text = input_text, language = language, format_instructions = format_instructions)
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-
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- messages = prompt.to_messages()
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-
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- response = chat(messages = messages)
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-
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- output = output_parser.parse(response.content)
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-
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- output_text = output.output
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-
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- return output_text
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-
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- # Interface
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- with gr.Blocks() as demo:
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- gr.HTML("<h1 align = 'center'> Text Translator </h1>")
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- gr.HTML("<h4 align = 'center'> Translate to any language </h4>")
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-
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- inputs = [gr.Textbox(label = "Enter the text that you want to translate"), gr.Textbox(label = "Enter the language that you want it to translate to", placeholder = "Example : Hindi,French,Bengali,etc")]
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- generate_btn = gr.Button(value = 'Translate')
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- outputs = [gr.Textbox(label = "Translated text")]
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- generate_btn.click(fn = text_translator, inputs= inputs, outputs = outputs)
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-
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- if __name__ == '__main__':
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- demo.launch()