Update app.py

app.py

@@ -7,20 +7,20 @@ import google.generativeai as genai
 from langchain.chains.question_answering import load_qa_chain
 import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
-from langsmith import LangSmithClient  # Hypothetical import for LangSmith
+# 使用 LangSmith 实际提供的类或方法
 
-# Configure Gemini API
+# 配置 Gemini API
 genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
 
-# Load Mistral model
+# 加载 Mistral 模型
 model_path = "nvidia/Mistral-NeMo-Minitron-8B-Base"
 mistral_tokenizer = AutoTokenizer.from_pretrained(model_path)
 device = 'cuda' if torch.cuda.is_available() else 'cpu'
 dtype = torch.bfloat16
 mistral_model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=dtype, device_map=device)
 
-# Initialize LangSmith client
-langsmith_client = LangSmithClient(api_key=os.getenv("LANGSMITH_API_KEY"))
+# 初始化 LangSmith 客户端或其他相关的工具
+# client = LangSmithClient(api_key=os.getenv("LANGSMITH_API_KEY")) # 请替换为实际的客户端或工具
 
 def initialize(file_path, question):
     try:
@@ -41,7 +41,7 @@ def initialize(file_path, question):
             stuff_answer = stuff_chain({"input_documents": pages, "question": question, "context": context}, return_only_outputs=True)
             gemini_answer = stuff_answer['output_text']
             
-            # Use Mistral model for additional text generation
+            # 使用 Mistral 模型进行文本生成
             mistral_prompt = f"Based on this answer: {gemini_answer}\nGenerate a follow-up question:"
             mistral_inputs = mistral_tokenizer.encode(mistral_prompt, return_tensors='pt').to(device)
             with torch.no_grad():
@@ -50,15 +50,16 @@ def initialize(file_path, question):
             
             combined_output = f"Gemini Answer: {gemini_answer}\n\nMistral Follow-up: {mistral_output}"
 
-            # Log and evaluate the results using LangSmith
-            evaluation = langsmith_client.evaluate(gemini_answer, mistral_output, question)
-            return combined_output + f"\n\nEvaluation: {evaluation}"
+            # 在这里使用 LangSmith 进行评估或日志记录
+            # evaluation = client.evaluate(gemini_answer, mistral_output, question) # 替换为实际评估方法
+            # return combined_output + f"\n\nEvaluation: {evaluation}"
+            return combined_output
         else:
             return "Error: Unable to process the document. Please ensure the PDF file is valid."
     except Exception as e:
         return f"An error occurred: {str(e)}"
 
-# Define Gradio Interface
+# 定义 Gradio 接口
 input_file = gr.File(label="Upload PDF File")
 input_question = gr.Textbox(label="Ask about the document")
 output_text = gr.Textbox(label="Answer - Combined Gemini and Mistral")
@@ -68,7 +69,7 @@ def pdf_qa(file, question):
         return "Please upload a PDF file first."
     return initialize(file.name, question)
 
-# Create Gradio Interface
+# 创建 Gradio 界面
 gr.Interface(
     fn=pdf_qa,
     inputs=[input_file, input_question],
@@ -76,3 +77,4 @@ gr.Interface(
     title="RAG Knowledge Retrieval using Gemini API and Mistral Model",
     description="Upload a PDF file and ask questions about the content."
 ).launch()
+