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import gradio as gr
from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM
from gtts import gTTS
import tempfile
import torch
# ======= Sentiment Analysis =======
sentiment_model = pipeline("sentiment-analysis")
# ======= Summarization with tokenizer/model =======
sum_model_name = "sshleifer/distilbart-xsum-12-6"
sum_tokenizer = AutoTokenizer.from_pretrained(sum_model_name)
sum_model = AutoModelForSeq2SeqLM.from_pretrained(sum_model_name)
def summarize_text(text):
inputs = sum_tokenizer(text, return_tensors="pt", max_length=1024, truncation=True)
summary_ids = sum_model.generate(inputs["input_ids"], max_length=60, min_length=15, do_sample=False)
return sum_tokenizer.decode(summary_ids[0], skip_special_tokens=True)
# ======= Text to Speech =======
def text_to_speech(text):
tts = gTTS(text)
with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as fp:
tts.save(fp.name)
return fp.name
# ======= Chatbot Tab =======
chat_model_name = "microsoft/DialoGPT-medium"
chat_tokenizer = AutoTokenizer.from_pretrained(chat_model_name)
chat_model = AutoModelForCausalLM.from_pretrained(chat_model_name)
chat_history = []
def chat_with_bot(user_input):
global chat_history
new_input_ids = chat_tokenizer.encode(user_input + chat_tokenizer.eos_token, return_tensors='pt')
bot_input_ids = torch.cat(chat_history + [new_input_ids], dim=-1) if chat_history else new_input_ids
chat_history.append(new_input_ids)
response_ids = chat_model.generate(bot_input_ids, max_length=1000, pad_token_id=chat_tokenizer.eos_token_id)
response = chat_tokenizer.decode(response_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
return response
# ======= Gradio UI =======
with gr.Blocks() as demo:
gr.Markdown("## 📘 Homework - Tuwaiq Academy")
with gr.Tab("🔍 Sentiment Analysis"):
gr.Markdown("### Analyze the sentiment of your text")
input_sent = gr.Textbox(label="Enter your text", lines=6, placeholder="Type something...")
output_sent = gr.Textbox(label="Sentiment Result")
btn_sent = gr.Button("Analyze")
btn_sent.click(lambda text: sentiment_model(text)[0]['label'] + ", Confidence: " + str(round(sentiment_model(text)[0]['score'], 2)),
inputs=input_sent, outputs=output_sent)
with gr.Tab("📝 Summarization"):
gr.Markdown("### Summarize your text")
input_sum = gr.Textbox(label="Enter your text", lines=6, placeholder="Paste a paragraph...")
output_sum = gr.Textbox(label="Summary")
btn_sum = gr.Button("Summarize")
btn_sum.click(summarize_text, inputs=input_sum, outputs=output_sum)
with gr.Tab("🔊 Text to Speech"):
gr.Markdown("### Convert text to speech")
input_tts = gr.Textbox(label="Enter your text", lines=6, placeholder="Text for audio...")
output_audio = gr.Audio(label="Speech Output", type="filepath")
btn_tts = gr.Button("Convert")
btn_tts.click(text_to_speech, inputs=input_tts, outputs=output_audio)
with gr.Tab("🤖 Chatbot"):
gr.Markdown("### Chat with an AI Bot")
chat_input = gr.Textbox(label="You:", placeholder="Ask me anything...", lines=2)
chat_output = gr.Textbox(label="Bot:", lines=4)
btn_chat = gr.Button("Send")
btn_chat.click(chat_with_bot, inputs=chat_input, outputs=chat_output)
demo.launch()
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