Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from huggingface_hub import login
+import os
+
+# -----------------------------
+# 1. Login using HF token (from environment variable)
+# -----------------------------
+hf_token = os.environ.get("HF_TOKEN")
+if hf_token:
+    login(token=hf_token)
+else:
+    print("⚠️ Warning: HF_TOKEN not set. Add it in the Space settings.")
+
+# -----------------------------
+# 2. Load Model
+# -----------------------------
+model_id = "ibm-granite/granite-3.3-2b-instruct"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id, use_auth_token=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    device_map="auto",
+    use_auth_token=True
+)
+
+# -----------------------------
+# 3. Functions
+# -----------------------------
+def classify_sentiment(text):
+
+    prompt = f"""
+Classify the sentiment of the following review as one word: Positive, Negative, or Neutral.
+Review: "{text}"
+Only respond with the sentiment label.
+"""
+
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+    output = model.generate(
+        **inputs,
+        max_new_tokens=5,
+        temperature=0.1,
+        repetition_penalty=1.5
+    )
+
+    result = tokenizer.decode(output[0], skip_special_tokens=True)
+
+    final = result.split()[-1].strip()
+
+    return final
+
+
+def summarize_text(text):
+
+    prompt = f"""
+Summarize the following text in 3–5 bullet points.
+Make the summary short, simple, and avoid repeating sentences.
+Text: {text}
+Summary:
+"""
+
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+    output = model.generate(
+        **inputs,
+        max_new_tokens=150,
+        temperature=0.7,
+        top_p=0.9,
+        repetition_penalty=2.0,
+    )
+
+    result = tokenizer.decode(output[0], skip_special_tokens=True)
+
+    summary = result.split("Summary:")[-1].strip()
+
+    return summary
+
+
+
+# -----------------------------
+# 4. Gradio Interface
+# -----------------------------
+with gr.Blocks() as demo:
+
+    gr.Markdown("## 🟦 Granite 3.3-2B — Sentiment + Summarization Demo")
+
+    with gr.Tab("Sentiment Classification"):
+        inp = gr.Textbox(label="Enter Review", lines=4)
+        out = gr.Textbox(label="Predicted Sentiment", lines=1)
+        btn = gr.Button("Classify")
+        btn.click(classify_sentiment, inp, out)
+
+    with gr.Tab("Summarization"):
+        inp2 = gr.Textbox(label="Enter Text to Summarize", lines=8)
+        out2 = gr.Textbox(label="Summary", lines=8)
+        btn2 = gr.Button("Summarize")
+        btn2.click(summarize_text, inp2, out2)
+
+demo.launch()