Update app.py

app.py

@@ -9,7 +9,6 @@ import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
 from PIL import Image
 import io
-from langsmith import LangSmith
 
 # Configure Gemini API
 genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
@@ -24,8 +23,10 @@ mistral_model = AutoModelForCausalLM.from_pretrained(model_path_mistral, torch_d
 openelm_270m_instruct = AutoModelForCausalLM.from_pretrained("apple/OpenELM-1_1B", trust_remote_code=True)
 tokenizer = AutoTokenizer.from_pretrained("NousResearch/Llama-2-7b-hf")
 
-# LangSmith setup
-langsmith = LangSmith(api_key=os.getenv("LANGSMITH_API_KEY"))
+# 替代的LangSmith評估函數
+def evaluate_with_langsmith(text):
+    score = len(text.split())  # 根據生成文本的字數評分
+    return f"Score: {score}"
 
 def process_pdf(file_path, question):
     model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3)
@@ -59,10 +60,6 @@ def generate(newQuestion, num):
     output_text = tokenizer.decode(output_ids[0].tolist(), skip_special_tokens=True)
     return output_text
 
-def evaluate_with_langsmith(text):
-    # Hypothetical evaluation logic using LangSmith
-    return langsmith.evaluate_text(text)
-
 def process_input(file, image, question, gen_length):
     try:
         if file is not None: