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import openai
import gradio as gr
from deep_translator import GoogleTranslator
openai.api_key = "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" # Replace with your OpenAI API key
language_codes = {
"English": "en",
"Tamil": "ta",
"Telugu": "te",
"Kannada": "kn",
"Malayalam": "ml",
"Hindi": "hi"
}
def translate_input(text, lang):
if lang != "English":
return GoogleTranslator(source=language_codes[lang], target="en").translate(text)
return text
def translate_output(text, lang):
if lang != "English":
return GoogleTranslator(source="en", target=language_codes[lang]).translate(text)
return text
def chatbot(user_input, language):
try:
translated_input = translate_input(user_input, language)
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": "You are a polite hospital receptionist."},
{"role": "user", "content": translated_input}
]
)
reply = response.choices[0].message.content
final_reply = translate_output(reply, language)
return final_reply
except Exception as e:
return f"❌ Error: {str(e)}"
iface = gr.Interface(
fn=chatbot,
inputs=[
gr.Textbox(label="Ask your hospital-related question"),
gr.Dropdown(choices=list(language_codes.keys()), label="Select Language", value="English")
],
outputs=gr.Textbox(label="Receptionist's Response"),
title="🏥 Multilingual Hospital Receptionist Chatbot"
)
iface.launch()
import gradio as gr
# your function and UI logic here...
demo = gr.Interface(fn=your_function, inputs=..., outputs=...)
demo.launch()
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