Deploying PDF Version v19

app.py

@@ -105,33 +105,14 @@ async def start():
         return
 
     file = files[0]
-    msg = cl.Message(content=f"Processing `{file.name}`...")
-    await msg.send()
-
-    # Create a progress bar for upload
-    upload_progress = cl.Progress(total=100, description="Uploading")
-    await upload_progress.send()
-
-    # Simulate upload progress
-    for i in range(0, 101, 10):
-        await upload_progress.update(i)
-        await cl.sleep(0.5)
-
-    await cl.Message(content=f"Upload complete. Now processing `{file.name}`...").send()
-
-    # Create a progress bar for processing
-    process_progress = cl.Progress(total=100, description="Processing")
-    await process_progress.send()
+    await cl.Message(f"Starting to process `{file.name}`. This may take a few minutes...").send()
 
     try:
-        # Simulate processing progress
-        for i in range(0, 101, 10):
-            await process_progress.update(i)
-            await cl.sleep(1)
-            if i == 50:
-                # Actually process the file at the midpoint
-                chunks_with_metadata = process_file(file)
+        # Process the file
+        await cl.Message("Reading and splitting the document...").send()
+        chunks_with_metadata = process_file(file)
         
+        await cl.Message("Creating embeddings and setting up the retrieval system...").send()
         embeddings = OpenAIEmbeddings(model=APP_CONFIG["embedding_model"])
         docsearch = await cl.make_async(Chroma.from_documents)(
             [Document(page_content=chunk["content"], metadata=chunk["metadata"]) for chunk in chunks_with_metadata],
@@ -144,6 +125,7 @@ async def start():
             return_messages=True,
         )
 
+        await cl.Message("Finalizing the chat model...").send()
         chain = ConversationalRetrievalChain.from_llm(
             ChatOpenAI(
                 model_name=APP_CONFIG["model_name"],