Update app.py

app.py

@@ -1,11 +1,67 @@
-// REAL AI CALL (requires HF_TOKEN secret)
-// const response = await fetch(
-//     "https://api-inference.huggingface.co/models/gpt2",
-//     {
-//         headers: { Authorization: `Bearer ${window.huggingface?.variables?.HF_TOKEN || "YOUR_TOKEN_HERE"}` },
-//         method: "POST",
-//         body: JSON.stringify({ inputs: "The future of AI is" }),
-//     }
-// );
-// const data = await response.json();
-// aiStatus.textContent = "🧠 " + data[0]?.generated_text;
+import gradio as gr
+import requests
+import os
+
+# ---------- CONFIG ----------
+# We'll use the free Inference API – no local model downloads.
+# Get your token from https://huggingface.co/settings/tokens
+# Add it as a Space secret named "HF_TOKEN"
+HF_TOKEN = os.getenv("HF_TOKEN")
+API_URL = "https://api-inference.huggingface.co/models/distilbert-base-uncased-finetuned-sst-2-english"
+
+headers = {"Authorization": f"Bearer {HF_TOKEN}"} if HF_TOKEN else {}
+
+# ---------- FUNCTION ----------
+def analyze_sentiment(text):
+    """Send text to the model and return the sentiment label + score."""
+    if not text.strip():
+        return "⚠️ Please enter some text."
+
+    # If no token is set, show a friendly error.
+    if not HF_TOKEN:
+        return "🔑 Please set your HF_TOKEN as a Space secret."
+
+    payload = {"inputs": text}
+
+    try:
+        response = requests.post(API_URL, headers=headers, json=payload)
+        response.raise_for_status()  # raise if HTTP error
+
+        # The API returns a list of predictions: [{'label': 'POSITIVE', 'score': 0.99}, ...]
+        result = response.json()
+        # The first element is the list of predictions for the first input.
+        # For this model, it returns a list of two dicts: one for NEGATIVE, one for POSITIVE.
+        # We'll pick the one with highest score.
+        predictions = result[0]  # list of dicts
+        best = max(predictions, key=lambda x: x['score'])
+        label = best['label']
+        score = best['score']
+
+        return f"**{label}** (confidence: {score:.2%})"
+
+    except requests.exceptions.RequestException as e:
+        return f"❌ API error: {str(e)}"
+
+
+# ---------- GRADIO INTERFACE ----------
+demo = gr.Interface(
+    fn=analyze_sentiment,
+    inputs=gr.Textbox(
+        label="Enter your text",
+        placeholder="I love Hugging Face Spaces!",
+        lines=3
+    ),
+    outputs=gr.Markdown(label="Sentiment result"),
+    title="😊 Sentiment Analyzer",
+    description="Enter any text and get a sentiment prediction (positive/negative). Powered by DistilBERT.",
+    examples=[
+        ["This product is amazing!"],
+        ["I'm really disappointed with the service."],
+        ["The movie was okay, nothing special."],
+    ],
+    theme="huggingface",
+)
+
+# ---------- RUN ----------
+if __name__ == "__main__":
+    demo.launch()