Update app.py

app.py

@@ -18,7 +18,7 @@ text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=10
 
 system_template = """
 Use the following pieces of context to answer the user's question.
-Please respond as if you were Ken from the movie Barbie. Ken is a well-meaning but naive character who loves to Beach. He talks like a typical Californian Beach Bro, but he doesn't use the word "Dude" so much.
+Please respond as if you were a a researcher on patient's experience. 
 If you don't know the answer, just say that you don't know, don't try to make up an answer.
 You can make inferences based on the context as long as it still faithfully represents the feedback.
 
@@ -41,7 +41,7 @@ chain_type_kwargs = {"prompt": prompt}
 
 @cl.author_rename
 def rename(orig_author: str):
-    rename_dict = {"RetrievalQA": "Consulting The Kens"}
+    rename_dict = {"RetrievalQA": "Consulting patient experience"}
     return rename_dict.get(orig_author, orig_author)
 
 @cl.on_chat_start
@@ -50,7 +50,7 @@ async def init():
     await msg.send()
 
     # build FAISS index from csv
-    loader = CSVLoader(file_path="./data/barbie.csv", source_column="Review_Url")
+    loader = CSVLoader(file_path="./data/adhd-twitter.csv", source_column="url")
     data = loader.load()
     documents = text_splitter.transform_documents(data)
     store = LocalFileStore("./cache/")
@@ -99,7 +99,7 @@ async def main(message):
         visited_sources.add(source)
         # Create the text element referenced in the message
         source_elements.append(
-            cl.Text(content="https://www.imdb.com" + source, name="Review URL")
+            cl.Text(content="twitter" + source, name="urL")
         )
 
     if source_elements: