add files

app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("bigscience/bloom-1b3", use_cache=True)
+tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom-1b3")
+
+def post_process_sentence(input_sentence, generated_sentence):
+    new_sentence = generated_sentence.replace(input_sentence, "")
+    if "\n" not in new_sentence:
+        return generated_sentence.replace("  ", " ") + "\n- "
+    else:
+        return (input_sentence + new_sentence.split("\n")[0]).replace("  ", " ") + "\n- "
+
+def generate_single(model, tokenizer, input_sentence, max_length=50, top_k=0, temperature=0.7):
+    input_ids = tokenizer.encode(input_sentence, return_tensors="pt")
+    output = model.generate(
+        input_ids, do_sample=True, 
+        max_length=len(input_sentence)+max_length, 
+        top_k=top_k, 
+        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)
+    return post_processed_output.split("\n-")[-2]
+
+gr.Interface(
+    question_bloom,
+    [
+        gr.Textbox(lines=10, label="Input code"),
+        gr.inputs.Slider(
+            minimum=8,
+            maximum=256,
+            step=1,
+            default=8,
+            label="Number of tokens to generate",
+        ),
+        gr.inputs.Slider(
+            minimum=0,
+            maximum=2,
+            step=0.1,
+            default=0.6,
+            label="Temperature",
+        ),
+    ],
+    outputs=gr.Textbox(label="Predicted sentence", lines=10),
+).launch()

requirements.txt

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+transformers