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.py

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+import gradio as gr
+
+def echo(message, history):
+    return message
+
+demo = gr.ChatInterface(fn=echo, type="messages", examples=["hello", "hola", "merhaba"], title="Echo Bot")
+demo.launch()

speech_to_text.py

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+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+import gradio as gr
+
+model_name = "mistralai/Mistral-7B-Instruct-v0.1"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
+
+chat_history = [{"role": "system", "content": "You are a helpful assistant."}]
+
+def chat(user_input):
+    # 사용자 입력을 채팅 기록에 추가
+    chat_history.append({"role": "user", "content": user_input})
+    
+    # 모델에 채팅 기록 전달
+    inputs = tokenizer([message['content'] for message in chat_history], return_tensors="pt", padding=True).to("cuda" if torch.cuda.is_available() else "cpu")
+    
+    # 모델로 응답 생성
+    outputs = model.generate(**inputs, max_length=200)
+    
+    # 생성된 응답
+    bot_reply = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    # 봇 응답을 채팅 기록에 추가
+    chat_history.append({"role": "assistant", "content": bot_reply})
+    
+    return bot_reply
+
+demo = gr.ChatInterface(fn=chat, type='messages', title='이우진의 챗봇')
+demo.launch(share=True)