few updates

app.py

@@ -1,5 +1,5 @@
 import gradio as gr
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
 
 model = AutoModelForCausalLM.from_pretrained("bigscience/bloom-1b3", use_cache=True)
 tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom-1b3")
@@ -9,21 +9,22 @@ def post_process_sentence(input_sentence, generated_sentence):
     if "\n" not in new_sentence:
         return generated_sentence.replace("  ", " ") + "\n- "
     else:
-        return (input_sentence + new_sentence.split("\n")[0]).replace("  ", " ") + "\n- "
+        return (new_sentence.split("\n")[0]).replace("  ", " ") + "\n- "
 
-def generate_single(model, tokenizer, input_sentence, max_length=50, top_k=0, temperature=0.7):
+def generate_single(model, tokenizer, input_sentence, max_length=50, top_k=0, temperature=0.7, do_sample=True, seed=42):
+    set_seed(seed)
     input_ids = tokenizer.encode(input_sentence, return_tensors="pt")
     output = model.generate(
-        input_ids, do_sample=True, 
+        input_ids, do_sample=do_sample, 
         max_length=len(input_sentence)+max_length, 
         top_k=top_k, 
-        temperature=temperature
+        temperature=temperature,
     )
     generated_sentence = tokenizer.decode(output[0], skip_special_tokens=True)
     return post_process_sentence(input_sentence, generated_sentence)
 
-def question_bloom(input_sentence, max_length, temperature):
-    post_processed_output = generate_single(model, tokenizer, input_sentence, temperature=temperature, max_length=max_length)
+def question_bloom(input_sentence, max_length, temperature, do_sample=True, seed=42):
+    post_processed_output = generate_single(model, tokenizer, input_sentence, temperature=temperature, max_length=max_length, do_sample=do_sample, seed=seed)
     return post_processed_output.split("\n-")[-2]
 
 gr.Interface(
@@ -44,6 +45,14 @@ gr.Interface(
             default=0.6,
             label="Temperature",
         ),
+        gr.inputs.Checkbox(True, label="Do Sample"),
+        gr.inputs.Slider(
+            minimum=0,
+            maximum=256,
+            step=1,
+            default=42,
+            label="Random seed for generation",
+        ),
     ],
     outputs=gr.Textbox(label="Predicted sentence", lines=10),
 ).launch()