Create app.py

app.py

@@ -0,0 +1,41 @@
+from transformers import BloomForCausalLM, BloomTokenizerFast
+import gradio as gr
+
+tokenizer = BloomTokenizerFast.from_pretrained('nicholasKluge/Aira-Instruct-PT-560M',
+    use_auth_token="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")
+model = BloomForCausalLM.from_pretrained('nicholasKluge/Aira-Instruct-PT-560M',
+    use_auth_token="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") 
+
+import gradio as gr
+
+title = "AIRA Demo 🤓"
+
+
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox()
+    clear = gr.Button("Clear Conversation")
+
+    def respond(message, chat_history):
+        inputs = tokenizer(tokenizer.bos_token + message + tokenizer.eos_token, return_tensors="pt")
+
+        response = model.generate(**inputs,
+            bos_token_id=tokenizer.bos_token_id,
+            pad_token_id=tokenizer.pad_token_id,
+            eos_token_id=tokenizer.eos_token_id,
+            do_sample=True,
+            early_stopping=True,   
+            top_k=50, 
+            max_length=200,
+            top_p=0.95,
+            temperature=0.7, 
+            num_return_sequences=1)
+        
+        chat_history.append((f"👤 {message}", f"""🤖 {tokenizer.decode(response[0], skip_special_tokens=True).replace(message, "")}"""))
+
+        return "", chat_history
+
+    msg.submit(respond, [msg, chatbot], [msg, chatbot])
+    clear.click(lambda: None, None, chatbot, queue=False)
+
+demo.launch()