Update app.py

app.py

@@ -1,11 +1,5 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("Hbigscience/bloom")
-
+import pandas as pd
 
 def respond(
     message,
@@ -14,6 +8,8 @@ def respond(
     max_tokens,
     temperature,
     top_p,
+    client,
+    prompts_df,
 ):
     messages = [{"role": "system", "content": system_message}]
 
@@ -39,25 +35,38 @@ def respond(
         response += token
         yield response
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+    # 데이터셋 업데이트
+    if len(prompts_df) > 0:
+        prompts_df = prompts_df.iloc[1:, :]
 
+# ...
 
 if __name__ == "__main__":
-    demo.launch()
+    # 데이터셋 로드
+    prompts_df = pd.read_csv("prompts.csv")
+
+    # ...
+
+    while True:
+        user_input = input("사용자: ")
+
+        # 데이터셋에서 프롬프트 추출
+        if len(prompts_df) > 0:
+            prompt = prompts_df["prompt"].values[0]
+            prompts_df = prompts_df.iloc[1:, :]
+        else:
+            prompt = None
+
+        # ...
+
+        # 데이터셋 프롬프트 사용
+        if prompt:
+            history.append((user_input, None))
+            user_input = prompt
+
+        # ...
+
+        # 대화 내용 기록
+        history.append((user_input, response))
+
+        # ...