Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load two chatbot models
+model_name1 = "microsoft/DialoGPT-small"
+model_name2 = "microsoft/DialoGPT-medium"
+
+tokenizer1 = AutoTokenizer.from_pretrained(model_name1)
+model1 = AutoModelForCausalLM.from_pretrained(model_name1)
+
+tokenizer2 = AutoTokenizer.from_pretrained(model_name2)
+model2 = AutoModelForCausalLM.from_pretrained(model_name2)
+
+# Chat history
+history1, history2 = "", ""
+
+def ai_conversation(prompt):
+    global history1, history2
+
+    # AI 1 generates a response
+    inputs = tokenizer1.encode(prompt + tokenizer1.eos_token + history1, return_tensors="pt")
+    response_ids = model1.generate(inputs, max_length=200, pad_token_id=tokenizer1.eos_token_id)
+    response1 = tokenizer1.decode(response_ids[:, inputs.shape[-1]:][0], skip_special_tokens=True)
+    history1 += f"{prompt}\n{response1}\n"
+
+    # AI 2 responds to AI 1
+    inputs = tokenizer2.encode(response1 + tokenizer2.eos_token + history2, return_tensors="pt")
+    response_ids = model2.generate(inputs, max_length=200, pad_token_id=tokenizer2.eos_token_id)
+    response2 = tokenizer2.decode(response_ids[:, inputs.shape[-1]:][0], skip_special_tokens=True)
+    history2 += f"{response1}\n{response2}\n"
+
+    return response1, response2
+
+# Gradio interface
+interface = gr.Interface(
+    fn=ai_conversation,
+    inputs="text",
+    outputs=["text", "text"],
+    title="AI-to-AI Conversation",
+    description="Type a prompt, and watch two AIs chat!"
+)
+
+interface.launch()