Added files

app.py

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+import gradio as gr
+import torch
+
+from peft import AutoPeftModelForCausalLM
+from transformers import AutoTokenizer
+
+#
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = AutoPeftModelForCausalLM.from_pretrained(
+    "Someman/bloomz-560m-fine-tuned-adapters_v1.0"
+).to(device)
+tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom-1b1")
+
+SUMMARIZATION = "Summarization"
+TITLE_GENERATION = "Title Generation"
+
+
+def generate_output(prompt, input, kwargs):
+    text = prompt + input
+    inputs = tokenizer(text, return_tensors="pt")
+    generate = model.generate(**inputs, **kwargs)
+    output = tokenizer.batch_decodee(
+        generate[:, inputs.input_ids.shape[1] :], skip_special_tokens=True
+    )
+    return output[0].split("\n\n")[0].strip()
+
+
+def summarization(input: str):
+    prompt = "\\nSummary in the same language as the doc:"
+    kwargs = {"max_new_tokens": 50, "penalty_alpha": 0.6, "top_k": 4}
+    return generate_output(prompt, input, kwargs)
+
+
+def title_generation(input: str):
+    prompt = " \\n\\nGive me a good title for the article above."
+    kwargs = {"max_new_tokens": 50, "penalty_alpha": 0.6, "top_k": 4}
+    return generate_output(prompt, input, kwargs)
+
+
+def generate(task: str, input: str):
+    if task == SUMMARIZATION:
+        return summarization(input)
+    elif task == TITLE_GENERATION:
+        return title_generation(input)
+    else:
+        return "Wow! Very Dangerous!"
+
+
+demo = gr.Interface(
+    generate,
+    [
+        gr.Dropdown(
+            [SUMMARIZATION, TITLE_GENERATION],
+            label="Task",
+            info="Will add more task later!",
+        ),
+        gr.TextArea(),
+    ],
+    outputs="text",
+)
+demo.launch()

requirements.txt

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+peft
+transformers