add upload file feature

apps/datascience_assistant/README.md

@@ -6,21 +6,27 @@ Detailed information can be found in the [documentation](../../docs/source/agent
 ## Quick Start
 Streamlit is a Python library that makes it easy to create and share beautiful, custom web apps for machine learning and data science.
 
-To run the DS Assistant in streamlit, you need to install the Streamlit library. You can install it using pip:
+To run the DS Assistant in streamlit, you need to install additional libraries. You can install it using pip:
 ```bash
-pip install streamlit streamlit-jupyter
+pip install streamlit mistune matplotlib nbconvert
 ```
-Then, you need to set
+
 Then, you can run the DS Assistant using the following command:
 ```bash
-streamlit run app.py
+cd ../../
+streamlit run ./apps/datascience_assistant/app.py
 ```
 
 After running the command, a new tab will open in your default web browser with the DS Assistant running.
-The following are screenshots of the DS Assistant running in the browser:
 
+you can upload your dataset and write your request.
 ![img_2.png](../../resources/data_science_assistant_streamlit_1.png)
-you can view all of the codes and  in streamlit
+
+After submitting your request, DS Assistant will automatically generate a plan for this request.
+![img_2.png](../../resources/data_science_assistant_streamlit_4.png)
+
+After that, DS Assistant will automatically excute every task, you can view all of the codes and details in streamlit
 ![img_3.png](../../resources/data_science_assistant_streamlit_2.png)
+
 After you have finished using the DS Assistant, you can directly convert the running process to a pdf
 ![img_5.png](../../resources/data_science_assistant_streamlit_3.png)

apps/datascience_assistant/app.py

@@ -1,10 +1,14 @@
 import os
+import sys
 
 import streamlit as st
 from modelscope_agent.agents.data_science_assistant import DataScienceAssistant
 from modelscope_agent.tools.metagpt_tools.tool_recommend import \
     TypeMatchToolRecommender
 
+current_dir = os.path.dirname(os.path.abspath(__file__))
+project_root_path = os.path.abspath(os.path.join(current_dir, '../../'))
+sys.path.append(project_root_path)
 llm_config = {
     'model': 'qwen2-72b-instruct',
     'model_server': 'dashscope',
@@ -17,7 +21,15 @@
 st.write(
     'This is a data science assistant that can help you with your data science tasks.'
 )
-st.write('Please input your request below and click the submit button.')
-user_request = st.text_input('User Request')
+st.write(
+    'Please input your request and upload files then click the submit button.')
+
+files = st.file_uploader(
+    'Please upload files that you need. ', accept_multiple_files=True)
+last_file_name = ''
+user_request = st.text_area('User Request')
 if st.button('submit'):
+    for file in files:
+        with open(file.name, 'wb') as f:
+            f.write(file.getbuffer())
     data_science_assistant.run(user_request=user_request, streamlit=True)