Update app.py

app.py

@@ -1,13 +1,36 @@
 import gradio as gr
+import transformers
+from transformers import pipeline
+from transformers import BloomTokenizerFast
 
-def generate(text):
-    return "Hello " + name + "!"
 
-with gr.Blocks() as demo:
-    name = gr.Textbox(lines=10, label="Input paragraph")
+
+
+def generate(checkpoint, input_prompt):
+
+    tokenizer = BloomTokenizerFast.from_pretrained("bigscience/bloom")
+    generator = pipeline("text-generation", model='simonosgoode/bloom-560m-finetuned-cdn_law', tokenizer=tokenizer)
+    
+    generated_judgement = generator(input_prompt
+                              , max_length = 100
+                              , num_return_sequences = 1
+                              , return_full_text = True
+                              , verbose = 0
+                              #, num_beams = 1
+                              #, early_stopping = True    
+                              , temperature = 0.7
+                              #, top_k = 50     # Default 50
+                              , top_p = 1    # Default 1.0
+                              , no_repeat_ngram_size = 3   # Default = 0
+                              , repetition_penalty = 1.0   # Default = 1.0
+                              #, do_sample = True     # Default = False
+                              )[0]["generated_text"]
+    return generated_judgement
+
+with gr.Blocks() as judgements:
+    inputs = gr.Textbox(lines=10, label="Input paragraph")
     output = gr.Textbox(lines=10, label="Output paragraph")
-    greet_btn = gr.Button("Generate the next paragraph of a judgement")
-    greet_btn.click(fn=generate, inputs=name, outputs=output)
+    btn = gr.Button("Generate the next paragraph of a judgement")
+    btn.click(fn=generate, inputs=inputs, outputs=output)
 
 
-demo.launch()