Create app.py

app.py

@@ -0,0 +1,31 @@
+# app.py
+
+import gradio as gr
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
+
+def generate_sequences(model_name, prompt):
+    if model_name == "nferruz/ProtGPT2":
+        protgpt2 = pipeline('text-generation', model="nferruz/ProtGPT2")
+        sequences = protgpt2(prompt, max_length=100, do_sample=True, top_k=950, repetition_penalty=1.2, num_return_sequences=10, eos_token_id=0)
+        return "\n".join([seq['generated_text'] for seq in sequences])
+    elif model_name == "lightonai/RITA_xl":
+        model = AutoModelForCausalLM.from_pretrained("lightonai/RITA_xl", trust_remote_code=True)
+        tokenizer = AutoTokenizer.from_pretrained("lightonai/RITA_xl")
+        rita_gen = pipeline('text-generation', model=model, tokenizer=tokenizer)
+        sequences = rita_gen(prompt, max_length=20, do_sample=True, top_k=950, repetition_penalty=1.2, num_return_sequences=2, eos_token_id=2)
+        return "\n".join([seq['generated_text'].replace(' ', '') for seq in sequences])
+    else:
+        return "Model not supported"
+
+model_options = ["nferruz/ProtGPT2", "lightonai/RITA_xl"]
+
+gr.Interface(
+    fn=generate_sequences,
+    inputs=[
+        gr.Dropdown(model_options, label="Select Model"),
+        gr.Textbox(lines=2, placeholder="Enter your prompt here...", label="Prompt")
+    ],
+    outputs="text",
+    title="Sequence Generation with Transformers",
+    description="Generate sequences using selected transformer models."
+).launch()