Update app.py

app.py

@@ -10,16 +10,31 @@ HARD_CODED_MODEL = "CohereForAI/c4ai-command-r-plus"
 def create_client(model_name):
  return InferenceClient(model_name, token=os.getenv("HF_TOKEN"))
 
-def call_api(content, system_message, max_tokens, temperature, top_p):
+def call_api(model_input, system_message, max_tokens, temperature, top_p):
  client = create_client(HARD_CODED_MODEL)
- messages = [{"role": "system", "content": system_message}, {"role": "user", "content": content}]
+ messages = [
+ {"role": "system", "content": system_message},
+ {"role": "user", "content": model_input}
+ ]
  random_seed = random.randint(0, 1000000)
- response = client.chat_completion(messages=messages, max_tokens=max_tokens, temperature=temperature, top_p=top_p, seed=random_seed)
+ response = client.chat_completion(
+ messages=messages,
+ max_tokens=max_tokens,
+ temperature=temperature,
+ top_p=top_p,
+ seed=random_seed
+ )
  return response.choices[0].message.content
 
-def generate_text(user_message, system_message, max_tokens, temperature, top_p):
- return call_api(user_message, system_message, max_tokens, temperature, top_p)
+# 긍정리뷰 분석을 위한 LLM 호출
+def analyze_positive_reviews(positive_reviews, positive_prompt, max_tokens, temperature, top_p):
+ return call_api(positive_reviews, positive_prompt, max_tokens, temperature, top_p)
 
+# 부정리뷰 분석을 위한 LLM 호출
+def analyze_negative_reviews(negative_reviews, negative_prompt, max_tokens, temperature, top_p):
+ return call_api(negative_reviews, negative_prompt, max_tokens, temperature, top_p)
+
+# 엑셀 파일 처리 및 긍정/부정 리뷰 추출
 def process_excel(file):
  if file is not None:
  # 엑셀 파일을 데이터프레임으로 읽기
@@ -86,8 +101,13 @@ with gr.Blocks() as demo:
  inputs=upload_excel, 
  outputs=[user_message, input1]) # 분석 결과를 긍정리뷰와 부정리뷰 입력칸에 바로 넣기
 
- generate_btn.click(fn=generate_text, 
+ # 긍정리뷰분석 및 부정리뷰분석을 LLM으로 추론
+ generate_btn.click(fn=analyze_positive_reviews, 
  inputs=[user_message, system_message, max_tokens, temperature, top_p], 
  outputs=[output1])
 
+ generate_btn.click(fn=analyze_negative_reviews, 
+ inputs=[input1, input2, max_tokens, temperature, top_p], 
+ outputs=[output2])
+
 demo.launch()