Add app file

app.py

@@ -0,0 +1,38 @@
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+def load_model(model_name):
+    model = AutoModelForCausalLM.from_pretrained(model_name)
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    return model, tokenizer
+    
+def converse(prompt, model1_name, model2_name, temperature1, temperature2):
+    model1, tokenizer1 = load_model(model1_name)
+    model2, tokenizer2 = load_model(model2_name)
+    
+    inputs = tokenizer1(prompt, return_tensors="pt")
+    outputs = model1.generate(inputs.input_ids, max_new_tokens=50, temperature=temperature1, do_sample=True, pad_token_id=tokenizer1.eos_token_id)
+    response1 = tokenizer1.decode(outputs[0], skip_special_tokens=True)
+
+    inputs = tokenizer2(response1, return_tensors="pt")
+    outputs = model2.generate(inputs.input_ids, max_new_tokens=50, temperature=temperature2, do_sample=True, pad_token_id=tokenizer2.eos_token_id)
+    response2 = tokenizer2.decode(outputs[0], skip_special_tokens=True)
+
+    return response1, response2
+
+iface = gr.Interface(
+    fn=converse,
+    inputs=[
+        gr.Textbox(label="Input Prompt"),
+        gr.Dropdown(["gpt2", "distilgpt2", "EleutherAI/gpt-neo-2.7B"], label="Model 1"),
+        gr.Dropdown(["gpt2", "distilgpt2", "EleutherAI/gpt-neo-2.7B"], label="Model 2"),
+        gr.Slider(0.1, 1.0, step=0.1, value=0.7, label="Temperature for Model 1"),
+        gr.Slider(0.1, 1.0, step=0.1, value=0.7, label="Temperature for Model 2")
+    ],
+    outputs=["text", "text"],
+    title="Multi-Model Conversation",
+    description="Input a prompt to start a conversation between two models. Adjust temperatures for more diverse outputs."
+)
+
+if __name__ == "__main__":
+    iface.launch()