Update app.py

app.py

@@ -10,29 +10,30 @@ tinier = AutoModelForCausalLM.from_pretrained("afrizalha/Sasando-1-7M", token=hf
 
 desc = """Sasando-1 is a tiny, highly experimental text generator built using the Phi-3 architecture. It comes with two variations of microscopic sizes: 7M and 25M parameters. It is trained on a tightly-controlled Indo4B dataset filtered to only have 18000 unique words. The method is inspired by Microsoft's TinyStories paper which demonstrates that a tiny language model can produce fluent text when trained on tightly-controlled dataset.\n\nTry prompting with two simple words, and let the model continue. Fun examples provided below."""
 
-def generate(starting_text, choice, num_runs,temp,top_p):
-    if choice == '7M': 
+def generate(starting_text, choice, temp, top_p):
+    if choice == '7M':
         model = tinier
-    elif choice == '25M': 
+    elif choice == '25M':
         model = tiny
     elif choice == 'Info':
-        return desc
-        
+        yield desc
+        return
+
     results = []
     for i in range(5):
         inputs = tokenizer([starting_text], return_tensors="pt").to(model.device)
         outputs = model.generate(
             inputs=inputs.input_ids,
-            max_new_tokens=32-len(inputs.input_ids),
-            do_sample=True,        
+            max_new_tokens=32-len(inputs.input_ids[0]),
+            do_sample=True,
             temperature=temp,
             top_p=top_p
         )
-        outputs = tokenizer.batch_decode(outputs,skip_special_tokens=True)[0]
+        outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
         outputs = outputs[:outputs.find(".")]
         results.append(outputs)
-        yield "\n\n".join(results)
-
+    yield "\n\n".join(results)
+    
 with gr.Blocks(theme=gr.themes.Soft()) as app:
     starting_text = gr.Textbox(label="Starting text", value="cinta adalah")
     choice = gr.Radio(["7M", "25M", "Info"], label="Select model", info="Built with the Phi-3 architecture")