Upload app.py

app.py

@@ -0,0 +1,51 @@
+import gradio as gr
+
+num_sequences=4
+
+demo_mode = False
+if not demo_mode:
+    from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+    model = AutoModelForCausalLM.from_pretrained("d2weber/german-gpt2-finetuned-coldmirror-hpodcast1")
+    tokenizer = AutoTokenizer.from_pretrained("dbmdz/german-gpt2", use_fast=True)
+    lm = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+    def generate(*args, **kwargs):
+        return [o["generated_text"] for o in lm(*args, **kwargs, pad_token_id=tokenizer.eos_token_id)]
+
+with gr.Blocks() as app:
+    prompt = gr.TextArea(value="Hallo und herzlich willkommen", label="Input")
+    sequences = []
+    for _ in range(num_sequences):
+        seq = gr.Textbox("", visible=False)
+        box = gr.CheckboxGroup(choices=[], label="", interactive=True)
+        sequences.append(seq)
+
+        @seq.change(inputs=seq, outputs=box)
+        def split(seq):
+            return gr.CheckboxGroup(seq.split(), value=[])
+
+        @box.select(inputs=[prompt, seq], outputs=prompt)
+        def handle(prompt, sequence, selected: gr.SelectData):
+            to_append = " ".join(sequence.split()[:selected.index+1])
+            delimiter = " " if to_append[:1].isalnum() else ""
+            return prompt.rstrip() + delimiter + to_append
+    
+    max_new_tokens = gr.Slider(1, 100, value=18, step=1, label="How long should the generated sequences be:")
+
+    gr.Examples([
+        ["Hallo und herzlich willkommen"],
+    ], prompt)
+
+    @prompt.change(inputs=[prompt, max_new_tokens], outputs=sequences)
+    def handle(prompt, max_new_tokens):
+        prompt = prompt.rstrip()
+        texts = ["some new words"]*num_sequences if demo_mode else generate(
+            prompt,
+            return_full_text=False,
+            num_return_sequences=num_sequences,
+            max_new_tokens=int(max_new_tokens),
+        )
+        return texts
+
+app.launch()