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import gradio as gr
from transformers import pipeline
from gtts import gTTS
# Load models
sentiment_pipeline = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
summarization_pipeline = pipeline("summarization", model="facebook/bart-large-cnn")
# Task logic
def perform_task(task, text):
if not text.strip():
return "⚠️ Please enter some text.", None
if task == "Sentiment Analysis":
result = sentiment_pipeline(text)[0]
label = result['label']
score = round(result['score'], 3)
return f"🧠 Sentiment: {label}\n📊 Confidence: {score}", None
elif task == "Summarization":
result = summarization_pipeline(text, max_length=100, min_length=30, do_sample=False)
return f"✂️ Summary:\n{result[0]['summary_text']}", None
elif task == "Text-to-Speech":
tts = gTTS(text)
filename = "tts_output.mp3"
tts.save(filename)
return "✅ Audio generated below:", filename
# UI with custom dark mode
with gr.Blocks(theme=gr.themes.Base()) as demo:
# Custom CSS for dark mode
gr.HTML("""
<style>
body, .gradio-container {
background-color: #111 !important;
color: #eee !important;
}
.gr-input, .gr-button, .gr-box {
background-color: #222 !important;
color: #eee !important;
border: 1px solid #444 !important;
}
input::placeholder, textarea::placeholder {
color: #888 !important;
}
</style>
""")
gr.Markdown("# 🤖 Multi-Task Chatbot", elem_id="title")
with gr.Row():
task_selector = gr.Dropdown(
["Sentiment Analysis", "Summarization", "Text-to-Speech"],
label="Select Task",
value="Sentiment Analysis"
)
textbox = gr.Textbox(lines=6, label="Enter your text here")
output_text = gr.Textbox(label="Output Message")
output_audio = gr.Audio(label="Generated Speech", type="filepath", visible=False)
run_button = gr.Button("Run")
def handle_all(task, text):
message, audio_path = perform_task(task, text)
return message, gr.update(value=audio_path, visible=audio_path is not None)
run_button.click(fn=handle_all, inputs=[task_selector, textbox], outputs=[output_text, output_audio])
demo.launch()
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