Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Load the SmolLM-135M model and tokenizer
+model_name = "HuggingFaceTB/SmolLM-135M-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+def generate_feedback(text):
+    # Generate a prompt for writing feedback
+    prompt = f"Provide constructive feedback on the following creative writing piece:\n\n{text}\n\nFeedback:"
+    
+    # Tokenize the input
+    inputs = tokenizer(prompt, return_tensors="pt")
+    
+    # Generate the response
+    with torch.no_grad():
+        outputs = model.generate(inputs.input_ids, max_length=150, do_sample=True, top_p=0.85, temperature=0.7)
+    
+    # Decode the response
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    feedback = response.split("Feedback:")[-1].strip()  # Extract feedback portion
+    return feedback
+
+# Set up the Gradio interface
+app = gr.Interface(
+    fn=generate_feedback,
+    inputs=gr.inputs.Textbox(lines=5, label="Your Writing", placeholder="Paste a short piece of creative writing here..."),
+    outputs="text",
+    title="Micro-Feedback Assistant for Creative Writing",
+    description="Get quick, constructive feedback on your creative writing. Ideal for short stories, poetry, or essays. "
+                "Feedback may cover tone, grammar, and word choice."
+)
+
+# Launch the app
+app.launch()