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import gradio as gr
from transformers import pipeline
# ✅ Sentiment Analysis
sentiment_pipeline = pipeline("sentiment-analysis")
def analyze_sentiment(text):
result = sentiment_pipeline(text)[0]
return f"{result['label']} ({result['score']:.2f})"
# ✅ Toxic Comment Detection (uses a toxicity model from Hugging Face)
toxic_pipeline = pipeline("text-classification", model="unitary/toxic-bert")
def detect_toxic(text):
result = toxic_pipeline(text)[0]
return f"{result['label']} ({result['score']:.2f})"
# ✅ Image Captioning Model (BLIP)
image_captioner = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
def caption_image(image):
result = image_captioner(image)[0]['generated_text']
return result
# ✅ Speech-to-Text Model (whisper)
speech_pipeline = pipeline("automatic-speech-recognition", model="openai/whisper-tiny")
def speech_to_text(audio):
result = speech_pipeline(audio)
return result['text']
# 🟡 Placeholder for Style Transfer (optional upgrade later)
def style_transfer(image, style):
return image # Replace with real model later
# ✅ Gradio App Setup
with gr.Blocks() as demo:
gr.Markdown("# 🚀 ML Playground Dashboard")
with gr.Tab("Sentiment Analyzer"):
gr.Interface(fn=analyze_sentiment, inputs=gr.Textbox(), outputs=gr.Textbox())
with gr.Tab("Toxic Comment Detector"):
gr.Interface(fn=detect_toxic, inputs=gr.Textbox(), outputs=gr.Textbox())
with gr.Tab("Image Caption Generator"):
gr.Interface(fn=caption_image, inputs=gr.Image(type="pil"), outputs=gr.Textbox())
with gr.Tab("Speech-to-Text"):
gr.Interface(fn=speech_to_text, inputs=gr.Audio(type="filepath"), outputs=gr.Textbox())
with gr.Tab("Art Style Transfer"):
gr.Interface(
fn=style_transfer,
inputs=[gr.Image(), gr.Dropdown(["Van Gogh", "Monet", "Picasso"])],
outputs=gr.Image()
)
demo.launch()
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