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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
from gtts import gTTS
import tempfile
from langdetect import detect
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
def respond(user_input):
if not user_input:
return "Please enter a question.", None
# Detect language
try:
detected_lang = detect(user_input)
except:
detected_lang = "en"
# Generate AI response
prompt = f"[INST] {user_input} [/INST]"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256, pad_token_id=tokenizer.eos_token_id)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Convert to speech
try:
tts = gTTS(text=response, lang='hi' if detected_lang == 'hi' else 'en')
with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as fp:
tts.save(fp.name)
audio_path = fp.name
except Exception as e:
audio_path = None
return response, audio_path
# UI
iface = gr.Interface(
fn=respond,
inputs=gr.Textbox(lines=2, placeholder="Ask anything...", label="user_input"),
outputs=[
gr.Textbox(label="TeachMe Says"),
gr.Audio(label="Voice", type="filepath", autoplay=True)
],
title="TeachMe - Your Smart Tutor",
description="Light AI bot with Hindi + English voice support."
)
iface.launch()
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