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app.py

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+#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 os
+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"))
+
+#Function that loads Gemini Model and returns the response
+def get_model_response(query, user_image):
+    model = genai.GenerativeModel('gemini-pro-vision')
+    if query != "":
+        model_response = model.generate_content([query, user_image])
+    else:
+        model_response = model.generate_content(user_image)
+    return model_response.text
+
+#Set up Streamlit application to visualize results
+st.set_page_config(page_title="Visualize Image-Text")
+st.header("Poulta App Image Analyses")
+
+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"])
+
+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 ✨")
+if submit:
+    model_response=get_model_response(user_input, image)
+    st.subheader("Results here 👇🏻")
+    st.write(model_response)

requirements.txt

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+streamlit
+google-generativeai
+python-dotenv