Create app.py

app.py

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+from transformers import AutoModelForCausalLM, AutoTokenizer
+import cradio as cr
+
+# Load the first model and tokenizer
+model_1 = AutoModelForCausalLM.from_pretrained("mosaicml/mpt-7b-chat")
+tokenizer_1 = AutoTokenizer.from_pretrained("mosaicml/mpt-7b-chat")
+
+# Load the second model and tokenizer
+model_2 = AutoModelForCausalLM.from_pretrained("nomic-ai/gpt4all-j")
+tokenizer_2 = AutoTokenizer.from_pretrained("nomic-ai/gpt4all-j")
+
+def generate_responses(prompt):
+    # Encode the prompt using both tokenizers
+    input_ids_1 = tokenizer_1.encode(prompt, return_tensors="pt")
+    input_ids_2 = tokenizer_2.encode(prompt, return_tensors="pt")
+
+    # Generate responses using both models
+    output_1 = model_1.generate(input_ids_1, max_length=50, num_return_sequences=1)
+    output_2 = model_2.generate(input_ids_2, max_length=50, num_return_sequences=1)
+
+    # Decode the responses using the corresponding tokenizers
+    response_1 = tokenizer_1.decode(output_1[0], skip_special_tokens=True)
+    response_2 = tokenizer_2.decode(output_2[0], skip_special_tokens=True)
+
+    return response_1, response_2
+
+iface = cr.Interface(
+    fn=generate_responses,
+    inputs=cr.TextInput("Enter your question:"),
+    outputs=[cr.Textbox(label="Response from model 1"), cr.Textbox(label="Response from model 2")],
+    title="AI Chatbot",
+    description="Get responses from two different AI models.",
+)
+
+iface.launch()