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app.py

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+import gradio as gr
+from transformers import pipeline
+import torch
+
+# Load models
+device = 0 if torch.cuda.is_available() else -1
+classifier = pipeline("sentiment-analysis", device=device)
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn", device=device)
+
+def analyze_sentiment(text):
+    if not text.strip():
+        return {"Error": 1.0}
+    result = classifier(text)[0]
+    return {
+        f"{result['label']} {'😊' if result['label'] == 'POSITIVE' else '😠'}": result['score'],
+        "Other": 1 - result['score']
+    }
+
+def summarize(text, max_len, min_len):
+    if len(text) < 50:
+        return "Please enter at least 50 characters."
+    result = summarizer(text, max_length=max_len, min_length=min_len)
+    return result[0]['summary_text']
+
+with gr.Blocks(title="NLP Toolkit") as app:
+    gr.Markdown("# 🛠️ NLP Toolkit")
+    
+    with gr.Tabs():
+        with gr.TabItem("Sentiment"):
+            gr.Interface(
+                fn=analyze_sentiment,
+                inputs=gr.Textbox(lines=3),
+                outputs=gr.Label(),
+                examples=[["I love this!"], ["This is terrible."]]
+            )
+        
+        with gr.TabItem("Summarize"):
+            gr.Interface(
+                fn=summarize,
+                inputs=[
+                    gr.Textbox(lines=6),
+                    gr.Slider(50, 200, value=100),
+                    gr.Slider(20, 80, value=30)
+                ],
+                outputs=gr.Textbox()
+            )
+
+app.launch()

requirements.txt

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+transformers
+torch
+gradio