Update app.py

app.py

@@ -1,26 +1,43 @@
 import gradio as gr
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
-# Load model and tokenizer
+# Load lightweight model
 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
+def generate_notes(topic):
+    """
+    Takes a topic string and generates study-style notes.
+    """
+    if not topic.strip():
+        return "⚠️ Please enter a topic first."
 
-# Gradio interface
+    prompt = (
+        f"Create clear, structured study notes about the topic '{topic}'. "
+        f"Include definitions, key points, and examples if possible. "
+        f"Keep it concise and easy to understand."
+    )
+
+    inputs = tokenizer(prompt, return_tensors="pt", truncation=True)
+    outputs = model.generate(**inputs, max_new_tokens=250, temperature=0.7, top_p=0.9)
+    notes = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return notes
+
+# Gradio UI
 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."
+    inputs=gr.Textbox(lines=2, placeholder="Enter a topic (e.g. Photosynthesis, Quantum Physics)"),
+    outputs=gr.Textbox(label="Generated Notes", lines=10),
+    title="📘 AI Note Generator",
+    description=(
+        "Enter any topic and get concise, well-structured notes generated by "
+        "a free open-source model (FLAN-T5-small)."
+    ),
+    examples=[
+        ["The water cycle"],
+        ["Machine learning"],
+        ["World War II"]
+    ]
 )
 
 iface.launch()