app.py

import os
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

# ---------------------------------------------------
# Hugging Face Token (from Space Secrets)
# ---------------------------------------------------
HF_TOKEN = os.environ.get("HF_TOKEN")

# ---------------------------------------------------
# IBM Granite Model (Correct & Public)
# ---------------------------------------------------
MODEL_ID = "ibm-granite/granite-3.0-2b-instruct"

tokenizer = AutoTokenizer.from_pretrained(
    MODEL_ID,
    token=HF_TOKEN
)

model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    device_map="auto",
    token=HF_TOKEN
)

generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=180,
    do_sample=False   # 🔥 prevents multiple summaries
)

# ---------------------------------------------------
# Core NLP Function (STRICT OUTPUT CONTROL)
# ---------------------------------------------------
def granite_nlp(task, text):
    if not text.strip():
        return "⚠️ Please enter some text."

    if task == "Summarization":
        prompt = f"""
You are an expert summarizer.

TASK:
Summarize the text below into EXACTLY 4 concise bullet points.

RULES:
- Do NOT repeat the input text
- Do NOT include explanations
- Output ONLY the bullet points

TEXT:
{text}

SUMMARY:
"""

    elif task == "Classification":
        prompt = f"""
You are a sentiment classification expert.

TASK:
Classify the sentiment of the text below.

RULES:
- Choose ONLY one word
- Options: Positive, Negative, Neutral
- Do NOT repeat the input text

TEXT:
{text}

ANSWER:
"""
    else:
        return "Invalid task selected."

    output = generator(prompt)[0]["generated_text"]

    # ---------------- Post-processing ----------------
    if "SUMMARY:" in output:
        output = output.split("SUMMARY:")[-1]
    elif "ANSWER:" in output:
        output = output.split("ANSWER:")[-1]

    return output.strip()

# ---------------------------------------------------
# Gradio UI
# ---------------------------------------------------
with gr.Blocks(title="IBM Granite – Summarization & Classification") as demo:
    gr.Markdown(
        """
        # 🧠 IBM Granite – Text Summarization & Classification  
        **Deployed on Hugging Face Spaces**

        Select a task, paste your text, and get clean AI output.
        """
    )

    task = gr.Radio(
        ["Summarization", "Classification"],
        label="Select Task",
        value="Summarization"
    )

    text_input = gr.Textbox(
        lines=12,
        label="Input Text",
        placeholder="Paste your text here..."
    )

    output = gr.Textbox(
        lines=12,
        label="Model Output",
        show_copy_button=True
    )

    btn = gr.Button("Run")

    btn.click(
        fn=granite_nlp,
        inputs=[task, text_input],
        outputs=output
    )

demo.launch()