Initial deploy

README.md

@@ -1,12 +1,17 @@
 ---
 title: Sentiment Analysis
-emoji: ⚡
-colorFrom: purple
-colorTo: red
+emoji: "\U0001F4CA"
+colorFrom: green
+colorTo: yellow
 sdk: gradio
-sdk_version: 6.11.0
+sdk_version: "5.29.0"
 app_file: app.py
 pinned: false
+license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Sentiment Analysis
+
+Analyze the sentiment of text input (positive / negative / neutral).
+
+**Course**: 100 Deep Learning ch4 — RNN & NLP

app.py

@@ -0,0 +1,89 @@
+"""
+Sentiment Analysis — Positive / Negative / Neutral
+Course: 100 Deep Learning ch4
+"""
+
+import gradio as gr
+from transformers import pipeline
+
+# Multi-language sentiment model
+classifier = pipeline(
+    "sentiment-analysis",
+    model="lxyuan/distilbert-base-multilingual-cased-sentiments-student",
+)
+
+LABEL_MAP = {
+    "positive": "Positive",
+    "negative": "Negative",
+    "neutral": "Neutral",
+}
+EMOJI_MAP = {
+    "positive": "😊",
+    "negative": "😞",
+    "neutral": "😐",
+}
+
+
+def analyze(text: str):
+    if not text.strip():
+        return {}, ""
+
+    results = classifier(text, top_k=3)
+    label_dict = {
+        f"{EMOJI_MAP.get(r['label'], '')} {LABEL_MAP.get(r['label'], r['label'])}": round(r["score"], 4)
+        for r in results
+    }
+
+    top = results[0]
+    emoji = EMOJI_MAP.get(top["label"], "")
+    summary = (
+        f"**Prediction: {emoji} {LABEL_MAP.get(top['label'], top['label'])}** "
+        f"({top['score']:.1%} confidence)\n\n"
+        f"| Label | Score |\n|---|---|\n"
+    )
+    for r in results:
+        bar_len = int(r["score"] * 30)
+        bar = "█" * bar_len + "░" * (30 - bar_len)
+        summary += f"| {LABEL_MAP.get(r['label'], r['label'])} | {bar} {r['score']:.1%} |\n"
+
+    return label_dict, summary
+
+
+with gr.Blocks(title="Sentiment Analysis") as demo:
+    gr.Markdown(
+        "# Sentiment Analysis\n"
+        "Enter text in any language to analyze its sentiment.\n"
+        "Uses a multilingual DistilBERT model.\n"
+        "*Course: 100 Deep Learning ch4 — RNN & Sequence Models*"
+    )
+
+    with gr.Row():
+        with gr.Column():
+            text_input = gr.Textbox(
+                label="Input Text",
+                placeholder="Type or paste text here...",
+                lines=4,
+            )
+            btn = gr.Button("Analyze Sentiment", variant="primary")
+        with gr.Column():
+            label_out = gr.Label(num_top_classes=3, label="Sentiment Scores")
+
+    detail_md = gr.Markdown()
+
+    btn.click(analyze, [text_input], [label_out, detail_md])
+    text_input.submit(analyze, [text_input], [label_out, detail_md])
+
+    gr.Examples(
+        examples=[
+            "I absolutely love this product! It exceeded all my expectations.",
+            "The movie was terrible. Worst 2 hours of my life.",
+            "The weather today is partly cloudy with temperatures around 72°F.",
+            "这个餐厅的食物非常好吃，服务也很棒！",
+            "I'm not sure how I feel about the new update. It has some good features but also some bugs.",
+            "Das Essen war hervorragend und die Bedienung sehr freundlich.",
+        ],
+        inputs=[text_input],
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,3 @@
+gradio>=5.0.0
+transformers>=4.30.0
+torch>=2.0.0