Update translator.py

translator.py

@@ -0,0 +1,35 @@
+import gradio as gr
+from pydantic import BaseModel, Field
+from langchain.prompts import HumanMessagePromptTemplate, ChatPromptTemplate
+from langchain.output_parsers import PydanticOutputParser
+from langchain_openai import ChatOpenAI
+
+chat = ChatOpenAI()
+
+# Define the Pydantic Model (updated for Pydantic v2)
+class TextTranslator(BaseModel):
+    output: str = Field(description="Python string containing the output text translated in the desired language")
+
+# Use PydanticOutputParser (no need for response_schemas)
+output_parser = PydanticOutputParser(pydantic_object=TextTranslator)
+
+def text_translator(input_text: str, language: str) -> str:
+    human_template = """Enter the text that you want to translate: 
+                {input_text}, and enter the language that you want it to translate to {language}."""
+    human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
+    chat_prompt = ChatPromptTemplate.from_messages([human_message_prompt])
+    prompt = chat_prompt.format_prompt(input_text=input_text, language=language)
+    messages = prompt.to_messages()
+    response = chat(messages=messages)
+    
+    # Use output_parser to parse the response
+    output = output_parser.parse(response.content)
+    return output.output
+
+def text_translator_ui():
+    gr.Markdown("### Text Translator\nTranslate text into any language using AI.")
+    input_text = gr.Textbox(label="Enter the text that you want to translate")
+    input_lang = gr.Textbox(label="Enter the language that you want it to translate to", placeholder="Example: Hindi, French, Bengali, etc.")
+    output_text = gr.Textbox(label="Translated text")
+    translate_button = gr.Button("Translate")
+    translate_button.click(fn=text_translator, inputs=[input_text, input_lang], outputs=output_text)