Update app.py

app.py

@@ -1,25 +1,38 @@
-#load all environment variables
+# Load all environment variables
 from dotenv import load_dotenv
 load_dotenv()
 
-#Import required packages and libraries
-import streamlit as st #Streamlit to create web application
+# Import required packages and libraries
+import streamlit as st  # Streamlit to create a web application
 import os
-import google.generativeai as genai #The primary LLM model that resides inside genAI library
+import google.generativeai as genai  # The primary LLM model that resides inside genAI library
 from PIL import Image
 
-#Fetch the API key that is defined inside .env
-genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+# Fetch the API key that is defined inside .env
+google_api_key = os.getenv("GOOGLE_API_KEY")
 
-#Function that loads Gemini Model and returns the response
+if google_api_key is None:
+    st.error("API key not found. Please check your .env file.")
+    st.stop()
+
+# Configure genAI library with the fetched API key
+genai.configure(api_key=google_api_key)
+
+# Function that loads Gemini Model and returns the response
 def get_model_response(query, user_image):
+    # Create a GenerativeModel instance with the 'gemini-pro-vision' model
     model = genai.GenerativeModel('gemini-pro-vision')
+
+    # Check if a query is provided and generate content accordingly
     if query != "":
         model_response = model.generate_content([query, user_image])
     else:
         model_response = model.generate_content(user_image)
+
+    # Extract and return the text from the model response
     return model_response.text
 
+# Set page configuration
 st.set_page_config(page_title="Visualize Image-Text")
 st.header("Poulta App Image Analyses")
 
@@ -27,16 +40,27 @@ st.header("Poulta App Image Analyses")
 logo_image_url = "https://poulta.com/assets/site-images/poulta-logo.png"
 st.image(logo_image_url, width=200)  # Adjust width as needed
 
-user_input=st.text_input("What do you want to know about the image ? ",key="user_input")
-uploaded_file = st.file_uploader("Pick your image ...", type=["jpg", "jpeg", "png","pdf"])
+# User input for query
+user_input = st.text_input("What do you want to know about the image? ", key="user_input")
+
+# File uploader for image
+uploaded_file = st.file_uploader("Pick your image...", type=["jpg", "jpeg", "png", "pdf"])
 
-image=""   
+image = ""
 if uploaded_file is not None:
     image = Image.open(uploaded_file)
     st.image(image, caption="Uploaded Image.", use_column_width=True)
 
-submit=st.button("See the magic ✨")
+# Button to trigger image analysis
+submit = st.button("See the magic ✨")
 if submit:
-    model_response=get_model_response(user_input, image)
+    # Perform model analysis and display results
+    model_response = get_model_response(user_input, image)
     st.subheader("Results here 👇🏻")
-    st.write(model_response)
+    st.write(model_response)
+
+# Add some markdown text for instructions or information
+st.markdown("### Instructions:")
+st.markdown("1. Enter a query about the image.")
+st.markdown("2. Upload an image.")
+st.markdown("3. Click the 'See the magic ✨' button to get results.")