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app.py

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+##Invoice Extractor
+
+from dotenv import load_dotenv
+
+load_dotenv() #it will take all env variables from .env file
+
+import streamlit as st
+import os
+from PIL import Image
+import google.generativeai as genai
+
+##congigure API Key
+
+genai.configure(api_key="AIzaSyBTfFJH5arruUT21R-kvloH0Xyzj8wAmn8")
+
+##function to load gemini pro vision model and get response
+
+def get_gemini_response(input,image,prompt):
+
+    ##loading the gemini model
+    model =genai.GenerativeModel('gemini-pro-vision')
+    response =model.generate_content([input,image[0],prompt])
+    return response.text
+
+def input_image_setup(uploaded_file):
+    if uploaded_file is not None:
+        #read the file into bytes
+        bytes_data = uploaded_file.getvalue()
+
+        image_parts = [
+            {
+                "mime_type": uploaded_file.type,
+                "data": bytes_data
+            }
+        ] 
+        return image_parts
+    else:
+        raise FileNotFoundError("No file uploaded")
+
+
+#streamlit app
+
+st.set_page_config(page_title="Invoice Extractor")
+
+st.header("Gemini Application")
+input=st.text_input("Input Prompt: ",key="input")
+uploaded_file = st.file_uploader("Choose an 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("Tell me about the invoice")
+
+input_prompt ="""
+you are an expert in understanding invoices. you will 
+receive input images as invoices and you will have to
+answer questions based on the input image.
+"""
+
+## If asubmit button is clicked
+
+if submit:
+    image_data = input_image_setup(uploaded_file)
+    response=get_gemini_response(input_prompt,image_data,input)
+
+    st.subheader("The Response is")
+    st.write(response)

requirements.txt

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+google-generativeai
+streamlit
+python-dotenv