Update app.py

app.py

@@ -1 +1,59 @@
-# Paste your final gradio code here (the one I just sent you)
+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()