biogpt project codes are uploaded.

README.md

@@ -1,6 +1,6 @@
 ---
 title: BioGpt
-emoji: 💩
+emoji: 🌖
 colorFrom: red
 colorTo: purple
 sdk: gradio

app.py

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+from transformers import pipeline, set_seed
+from transformers import BioGptTokenizer, BioGptForCausalLM
+import gradio as gr
+
+model_list = [
+    "microsoft/biogpt",
+    "microsoft/BioGPT-Large-PubMedQA"
+]
+
+def biogpt(
+    prompt: str,
+    model_id: str,
+    max_length: int = 25,
+    num_return_sequences: int = 5
+):
+    
+    model = BioGptForCausalLM.from_pretrained(model_id)
+    tokenizer = BioGptTokenizer.from_pretrained(model_id)
+    generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
+    set_seed(42)
+    output = generator(prompt, max_length=max_length, num_return_sequences=num_return_sequences, do_sample=True)
+    output_dict = {
+        "1": output[0]['generated_text'],
+        "2": output[1]['generated_text'],
+        "3": output[2]['generated_text'],
+        "4": output[3]['generated_text'],
+        "5": output[4]['generated_text']
+    }
+    return f'{output_dict["1"]}\n\n{output_dict["2"]}\n\n{output_dict["3"]}\n\n{output_dict["4"]}\n\n{output_dict["5"]}'
+
+
+inputs = [
+    gr.inputs.Textbox(label="Prompt", lines=5, default="COVID-19 is"),
+    gr.Dropdown(model_list, value="microsoft/biogpt", label="Model ID"),
+    gr.inputs.Slider(5, 100, 25, default=25, label="Max Length"),
+    gr.inputs.Slider(1, 10, 5, default=5, label="Num Return Sequences")
+    ]
+
+outputs = gr.outputs.Textbox(label="Output")
+
+examples = [
+    ["COVID-19 is", "microsoft/biogpt"]
+]
+
+title = " BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining"
+demo_app = gr.Interface(
+    biogpt, 
+    inputs, 
+    outputs, 
+    title=title, 
+    examples=examples, 
+    cache_examples=True,
+)
+demo_app.launch(debug=True, enable_queue=True)

requirements.txt

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+transformers==4.26.0
+sacremoses