app.py

app.py

@@ -0,0 +1,26 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-small")
+model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-small")
+
+# Note generator function
+def generate_notes(prompt):
+    # Add task prefix for FLAN-T5
+    input_text = f"Summarize or make concise notes: {prompt}"
+    inputs = tokenizer(input_text, return_tensors="pt", truncation=True)
+    outputs = model.generate(**inputs, max_new_tokens=200)
+    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return result
+
+# Gradio interface
+iface = gr.Interface(
+    fn=generate_notes,
+    inputs=gr.Textbox(lines=5, placeholder="Enter your topic or text here..."),
+    outputs="text",
+    title="🧠 Free Note Generator (FLAN-T5-small)",
+    description="Generate summarized notes using Google's FLAN-T5-small model — runs 100% free on CPU in Hugging Face Spaces."
+)
+
+iface.launch()