Update app.py

app.py

@@ -1,7 +1,58 @@
+# app.py
+import os
+from transformers import pipeline
 import gradio as gr
 
-def greet(name):
-    return f"Hello {name}!"
+# Load the sentiment-analysis pipeline once (cached in memory).
+# Model: distilBERT fine-tuned on SST-2. Swap this string with another HF model if you want.
+MODEL_NAME = "distilbert-base-uncased-finetuned-sst-2-english"
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+# Instantiate the pipeline (this downloads weights the first time).
+sentiment_pipe = pipeline("sentiment-analysis", model=MODEL_NAME, tokenizer=MODEL_NAME)
+
+def analyze_sentiment(text: str):
+    """
+    Analyze sentiment for `text` and return:
+      - a dict of label probabilities (for gr.Label component)
+      - a human-readable label + score string
+    """
+    if not text or not text.strip():
+        return {"POSITIVE": 0.0, "NEGATIVE": 0.0}, "No input provided."
+
+    raw = sentiment_pipe(text[:1000])[0]  # truncate long text to 1000 chars to keep latency reasonable
+    label = raw["label"]  # "POSITIVE" or "NEGATIVE"
+    score = float(raw["score"])
+
+    # Provide both label probabilities so the Label component can show a nice bar
+    if label.upper() == "POSITIVE":
+        probs = {"POSITIVE": score, "NEGATIVE": 1.0 - score}
+    else:
+        probs = {"POSITIVE": 1.0 - score, "NEGATIVE": score}
+
+    pretty = f"{label} (confidence: {score:.2f})"
+    return probs, pretty
+
+# Build Gradio UI
+title = "Simple Sentiment Classifier (Transformers → Gradio)"
+description = "Type some text and the model will predict sentiment (positive/negative). Uses a Hugging Face transformers sentiment model in the backend."
+
+with gr.Blocks() as demo:
+    gr.Markdown(f"# {title}")
+    gr.Markdown(description)
+
+    with gr.Row():
+        txt = gr.Textbox(lines=6, placeholder="Enter text to analyze...", label="Input text")
+        # Left: probabilities shown as bars. Right: human readable label
+        out_probs = gr.Label(label="Predicted probabilities")
+        out_pretty = gr.Textbox(label="Predicted label", interactive=False)
+
+    submit = gr.Button("Analyze")
+
+    # Wire inputs -> function
+    submit.click(fn=analyze_sentiment, inputs=txt, outputs=[out_probs, out_pretty])
+
+# If you deploy on Hugging Face Spaces, they run the app automatically; otherwise run locally.
+if __name__ == "__main__":
+    # Use environment variable PORT for cloud hosts (Spaces sets it automatically)
+    port = int(os.environ.get("PORT", 7860))
+    demo.launch(server_name="0.0.0.0", server_port=port)