Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+
+# only the real FLAN-T5 Small model repo
+MODEL_REPO = "google/flan-t5-small"
+
+print("Loading FLAN-T5 Small model...")
+tokenizer = AutoTokenizer.from_pretrained(MODEL_REPO)
+model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_REPO)
+model.eval()
+print("Model loaded")
+
+def chat(prompt):
+    if not prompt.strip():
+        return "Type a message first"
+    inputs = tokenizer(
+        prompt,
+        return_tensors="pt",
+        truncation=True,
+        padding=True,
+        max_length=256
+    )
+    with torch.no_grad():
+        output = model.generate(
+            **inputs,
+            max_length=128,
+            do_sample=True,
+            top_p=0.9,
+            temperature=0.7
+        )
+    return tokenizer.decode(output[0], skip_special_tokens=True)
+
+with gr.Blocks(title="SmallGPT CPU") as demo:
+    gr.Markdown("# SmallGPT CPU Chat \nPowered by google/flan-t5-small (CPU only)")
+
+    user_input = gr.Textbox(label="Your message", lines=2)
+    bot_output = gr.Textbox(label="SmallGPT says", lines=4)
+
+    send_btn = gr.Button("Send")
+    send_btn.click(chat, inputs=user_input, outputs=bot_output)
+    user_input.submit(chat, inputs=user_input, outputs=bot_output)
+
+demo.launch()