Update app.py

app.py

@@ -1,13 +1,25 @@
+# You can find this code for Chainlit python streaming here (https://docs.chainlit.io/concepts/streaming/python)
+
+# OpenAI Chat completion
 import os
-import chainlit as cl
-import textract  # For extracting text from documents
+from openai import AsyncOpenAI  # importing openai for API usage
+import chainlit as cl  # importing chainlit for our app
+from chainlit.prompt import Prompt, PromptMessage  # importing prompt tools
+from chainlit.playground.providers import ChatOpenAI  # importing ChatOpenAI tools
+from dotenv import load_dotenv
+
+api_key = os.getenv("OPENAI_API_KEY")
+
+# ChatOpenAI Templates
+system_template = """You are a helpful assistant who always speaks in a pleasant tone!
+"""
+
+user_template = """{input}
+Think through your response step by step.
+"""
 
-# Function to extract text from uploaded documents
-async def extract_text_from_file(file_path):
-    return textract.process(file_path).decode('utf-8')
 
-# Chat initialization
-@cl.on_chat_start
+@cl.on_chat_start  # marks a function that will be executed at the start of a user session
 async def start_chat():
     settings = {
         "model": "gpt-3.5-turbo",
@@ -17,51 +29,52 @@ async def start_chat():
         "frequency_penalty": 0,
         "presence_penalty": 0,
     }
+
     cl.user_session.set("settings", settings)
-    await cl.Message(content="Welcome! Please upload a document to begin.").send()
 
-# Handling file upload
-@cl.on_message
+
+@cl.on_message  # marks a function that should be run each time the chatbot receives a message from a user
 async def main(message: cl.Message):
-    # Checking if there is an uploaded file
-    if message.files:
-        uploaded_file = message.files[0]  # Accessing the uploaded file
-        file_path = uploaded_file['path']  # Getting the path of the uploaded file
-
-        # Extracting text from the uploaded file
-        file_content = await extract_text_from_file(file_path)
-
-        # Saving the content of the document in the user session
-        cl.user_session.set("document_content", file_content)
-
-        # Informing the user that the document was uploaded successfully
-        await cl.Message(content=f"Document '{uploaded_file['name']}' uploaded successfully! You can now ask questions based on the document content.").send()
-
-    else:
-        document_content = cl.user_session.get("document_content", "")
-        if not document_content:
-            await cl.Message(content="Please upload a document first.").send()
-            return
-
-        settings = cl.user_session.get("settings")
-        client = AsyncOpenAI()
-
-        # Creating the prompt for OpenAI based on the document content and user query
-        prompt = f"Document Content: {document_content}\n\nUser Query: {message.content}"
-        msg = cl.Message(content="")
-
-        # Sending prompt to OpenAI and streaming response
-        async for stream_resp in await client.chat.completions.create(
-            model=settings["model"],
-            messages=[{"role": "system", "content": "Answer based on the provided document."},
-                      {"role": "user", "content": prompt}],
-            stream=True,
-            **settings,
-        ):
-            token = stream_resp.choices[0].delta.content
-            if not token:
-                token = ""
-            await msg.stream_token(token)
-
-        # Sending the final response
-        await msg.send()
+    settings = cl.user_session.get("settings")
+
+    client = AsyncOpenAI()
+
+    print(message.content)
+
+    prompt = Prompt(
+        provider=ChatOpenAI.id,
+        messages=[
+            PromptMessage(
+                role="system",
+                template=system_template,
+                formatted=system_template,
+            ),
+            PromptMessage(
+                role="user",
+                template=user_template,
+                formatted=user_template.format(input=message.content),
+            ),
+        ],
+        inputs={"input": message.content},
+        settings=settings,
+    )
+
+    print([m.to_openai() for m in prompt.messages])
+
+    msg = cl.Message(content="")
+
+    # Call OpenAI
+    async for stream_resp in await client.chat.completions.create(
+        messages=[m.to_openai() for m in prompt.messages], stream=True, **settings
+    ):
+        token = stream_resp.choices[0].delta.content
+        if not token:
+            token = ""
+        await msg.stream_token(token)
+
+    # Update the prompt object with the completion
+    prompt.completion = msg.content
+    msg.prompt = prompt
+
+    # Send and close the message stream
+    await msg.send()