Update app.py

app.py

@@ -21,14 +21,12 @@ st.set_page_config(layout="wide")
 GOOGLE_API_KEY=os.environ['GOOGLE_API_KEY']
 
 st.title('Stock Market Insights')
-st.subheader('Using [](https://www.gstatic.com/lamda/images/gemini_wordmark_landing_page_238102af073d0ae2763aa5.svg)')
+st.sidebar.image("https://myndroot.com/wp-content/uploads/2023/12/Gemini-Dext.jpg",width =100)
+st.sidebar.markdown("The App uses **Google Gemini API** for Text and Vision along with 🦜️🔗 LangChain")
 st.sidebar.info("Know more about [NSE Tickers](https://www.google.com/search?q=nse+tickers+list&sca_esv=a6c39f4d03c5324c&sca_upv=1&rlz=1C1GCEB_enIN1011IN1011&sxsrf=ADLYWILQPbew-0SrvUUWpI8Y29_uOOgbvA%3A1716470016765&ei=AEFPZp-zLvzHp84P_ZWtuA0&oq=NSE+Tickers+&gs_lp=Egxnd3Mtd2l6LXNlcnAiDE5TRSBUaWNrZXJzICoCCAAyBRAAGIAEMggQABgWGAoYHjIGEAAYFhgeMgYQABgWGB4yBhAAGBYYHjIGEAAYFhgeMgYQABgWGB4yBhAAGBYYHjILEAAYgAQYhgMYigUyCxAAGIAEGIYDGIoFSIIbUL0PWL0PcAF4AZABAJgB8QKgAfECqgEDMy0xuAEByAEA-AEBmAICoAKKA8ICChAAGLADGNYEGEeYAwCIBgGQBgiSBwUxLjMtMaAHtQU&sclient=gws-wiz-serp)")
 
 st.sidebar.info("Know more about [Charts](https://chart-img.com/)")
-st.sidebar.markdown("The App uses **Google Gemini API** for Text and Vision along with 🦜️🔗 LangChain")
-st.sidebar.image("https://myndroot.com/wp-content/uploads/2023/12/Gemini-Dext.jpg",width =40)
 ticker_user = st.text_input("Enter Ticker for NSE Stocks","")
-    
 gemini_embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
 
 llm = ChatGoogleGenerativeAI(model="gemini-1.5-pro-latest",google_api_key = GOOGLE_API_KEY)
@@ -116,26 +114,3 @@ if ticker_user!="":
     else:
         st.warning(f"Failed to retrieve image. Status code: {response.status_code}")
         st.warning("Response:", response.text)
-
-
-
-    
-
-
-
-# If there is no environment variable set for the API key, you can pass the API
-# key to the parameter `google_api_key` of the `GoogleGenerativeAIEmbeddings`
-# function: `google_api_key = "key"`.
-
-# gemini_embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
-
-# # Save to disk
-# vectorstore = Chroma.from_documents(
-#                      documents=docs,                 # Data
-#                      embedding=gemini_embeddings,    # Embedding model
-#                      persist_directory="./chroma_db" # Directory to save data
-#                      )
-
-# vectorstore_disk = Chroma(
-#                         persist_directory="./chroma_db",       # Directory of db
-#                         embedding_function=gemini_embeddings   # Embedding model