Update app.py

app.py

@@ -1,3 +1,175 @@
+# import openai
+# import streamlit as st
+# from openai import OpenAI
+# import io
+# import time
+# import os
+# from dotenv import load_dotenv
+
+# # Initialize the OpenAI client with your API key
+# # Load environment variables from the .env file
+# load_dotenv()
+
+# # Get the OpenAI API key
+# OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+
+# client = OpenAI(api_key=OPENAI_API_KEY)
+# vector_store_id = "vs_sHLBqA88ng66RDZYMgaBmDuX"  # Vector Store ID to use
+# # vector_store_id = os.getenv("VECTOR_STORE_ID")  # Vector Store ID to use
+# # all_files = list(client.beta.vector_stores.files.list(vector_store_id))
+# # for file in all_files:
+# #   # print(file)
+# #   file_id = file.id
+# #   st.write(file_id)
+
+# # Set the assistant ID
+# assistant_id = "asst_UeIHpkpVejAgrIz3RnisxSLm"  # Replace with your own assistant ID
+# # assistant_id = os.getenv("ASSISTANT_ID")  # Replace with your own assistant ID
+
+# def ensure_single_thread_id():
+#     if "thread_id" not in st.session_state:
+#         thread = client.beta.threads.create()
+#         st.session_state.thread_id = thread.id
+#     return st.session_state.thread_id
+
+# def safe_message_send(prompt, thread_id):
+#     try:
+#         message = client.beta.threads.messages.create(
+#             thread_id=thread_id,
+#             role="user",
+#             content=prompt
+#         )
+#         return message
+#     except Exception as e:
+#         if "active" in str(e):
+#             print("Waiting for the current run to finish...")
+#             time.sleep(1)  # wait a bit before retrying
+#             return safe_message_send(prompt, thread_id)  # retry sending the message
+#         else:
+#             raise e
+
+# def stream_generator(prompt, thread_id):
+#     # print(f'First time thread in the function {thread_id}')
+#     message = safe_message_send(prompt, thread_id)  # use the new safe send function
+    
+#     with st.spinner("Wait... Generating response..."):
+#         try:
+#             stream = client.beta.threads.runs.create(
+#                 thread_id=thread_id,
+#                 assistant_id=assistant_id,
+#                 stream=True
+#             )
+
+#             for event in stream:
+#                 if event.data.object == "thread.message.delta":
+#                     for content in event.data.delta.content:
+#                         if content.type == 'text':
+#                             yield content.text.value
+#                             time.sleep(0.01)
+#                 elif event.data.object == "thread.run.stop":
+#                     break  # Break if the run stops
+#         except Exception as e:
+#             print(f"Error during streaming: {str(e)}")
+
+# def upload_and_add_to_vector_store(uploaded_file):
+#     """Upload a file to OpenAI and add it to the specified vector store."""
+#     try:
+#         # Convert the uploaded file to a BytesIO stream for uploading
+#         file_stream = io.BytesIO(uploaded_file.getvalue())
+#         file_stream.name = uploaded_file.name  # Preserve the file name    
+#         # Upload the file to the vector store
+#         file_batch = client.beta.vector_stores.file_batches.upload_and_poll(
+#             vector_store_id=vector_store_id,
+#             files=[file_stream]
+#         )
+
+
+#         st.success(f"File '{uploaded_file.name}' processed and added to vector store. Status: {file_batch.status}")
+#     except Exception as e:
+#         st.error(f"Failed to process file: {str(e)}")
+    
+
+
+# def list_all_files_in_vector_store():
+#     """List all files in the specified vector store."""
+#     try:
+#         all_files = list(client.beta.vector_stores.files.list(vector_store_id=vector_store_id))
+#         # st.write(all_files)
+#         for file in all_files:
+#             file_id = file.id
+#             st.write(file_id)   
+#     except Exception as e:
+#         st.error(f"Failed to list files: {str(e)}")
+#         return {}
+
+# def delete_file_from_vector_store(vector_store_id, file_id):
+#     """Delete a file from the specified vector store."""
+#     try:
+#         client.beta.vector_stores.files.delete(
+#             vector_store_id=vector_store_id,
+#             file_id=file_id
+#         )
+#         st.success(f"File with ID '{file_id}' deleted from vector store '{vector_store_id}'.")
+#     except Exception as e:
+#         st.error(f"Failed to delete file. File id is not Found.")
+
+# # Interface to delete files from vector store
+# st.sidebar.subheader("Delete File from Vector Store")
+# file_id_to_delete = st.sidebar.text_input("Enter File ID to Delete", "")
+# if st.sidebar.button("Delete File"):
+#     delete_file_from_vector_store(vector_store_id, file_id_to_delete)
+
+# # Streamlit interface setup
+# st.title("💬Chatbot")
+# st.caption("🚀 A Streamlit Custom Chatbot")
+
+
+# with st.sidebar:
+#     st.write("Upload PDF File")
+#     uploaded_file = st.file_uploader("Choose a file", type=['pdf', 'docx'], key='file_uploader')
+
+#     if st.button('Upload File', key='process_file'):
+#         if uploaded_file is not None:
+#             upload_and_add_to_vector_store(uploaded_file)
+#             st.success("File successfully uploaded and processed.")
+#         else:
+#             st.error("Please upload a file to process.")
+
+#     # List all uploaded files
+#     st.write("### Uploaded Files")
+#     if 'uploaded_files' in st.session_state and st.session_state.uploaded_files:
+#         for file_name, file_id in st.session_state.uploaded_files.items():
+#             st.write(f"{file_name}: {file_id}")
+    
+#     # List all files in the vector store
+#     st.write("## All Files in Vector Store")
+#     all_files = list_all_files_in_vector_store() 
+
+# # Initialize session state for chat
+# st.session_state.start_chat = True
+# if 'start_chat' not in st.session_state:
+#     st.session_state.start_chat = False
+
+# # Main chat interface
+# if st.session_state.start_chat:
+#     if "messages" not in st.session_state:
+#         st.session_state.messages = []
+
+#     for message in st.session_state.messages:
+#         with st.chat_message(message["role"]):
+#             st.markdown(message["content"])
+
+#     prompt = st.chat_input("Enter your message")
+#     if prompt:
+#         thread_id = ensure_single_thread_id()
+#         with st.chat_message("user"):
+#             st.markdown(prompt)
+#         st.session_state.messages.append({"role": "user", "content": prompt})
+        
+#         with st.chat_message("assistant"):
+#             response = st.write_stream(stream_generator(prompt, thread_id))
+#         st.session_state.messages.append({"role": "assistant", "content": response})
+
 import openai
 import streamlit as st
 from openai import OpenAI
@@ -15,7 +187,7 @@ OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
 
 client = OpenAI(api_key=OPENAI_API_KEY)
 vector_store_id = "vs_sHLBqA88ng66RDZYMgaBmDuX"  # Vector Store ID to use
-# vector_store_id = os.getenv("VECTOR_STORE_ID")  # Vector Store ID to use
+
 # all_files = list(client.beta.vector_stores.files.list(vector_store_id))
 # for file in all_files:
 #   # print(file)
@@ -24,7 +196,6 @@ vector_store_id = "vs_sHLBqA88ng66RDZYMgaBmDuX"  # Vector Store ID to use
 
 # Set the assistant ID
 assistant_id = "asst_UeIHpkpVejAgrIz3RnisxSLm"  # Replace with your own assistant ID
-# assistant_id = os.getenv("ASSISTANT_ID")  # Replace with your own assistant ID
 
 def ensure_single_thread_id():
     if "thread_id" not in st.session_state:
@@ -168,4 +339,4 @@ if st.session_state.start_chat:
         
         with st.chat_message("assistant"):
             response = st.write_stream(stream_generator(prompt, thread_id))
-        st.session_state.messages.append({"role": "assistant", "content": response})
+        st.session_state.messages.append({"role": "assistant", "content": response})