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app.py

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+# importing libraries
+import streamlit as st
+import google.generativeai as genai
+from dotenv import load_dotenv
+from PIL import Image
+import os
+
+load_dotenv() # load all the environment variables from .env
+
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+
+# Function to load Gemini Pro Vision
+# In Gemini Pro, model takes it in a list
+model = genai.GenerativeModel('gemini-pro-vision')
+
+def get_gemini_response(input, image, prompt):
+    response = model.generate_content([input, image[0], prompt])
+    return response.text
+
+def input_image_details(uploaded_file):
+    # Check if a file has been uploaded
+    if uploaded_file is not None:
+        # Read the file into bytes
+        bytes_data = uploaded_file.getvalue()
+
+        image_parts = [
+            {
+                "mime_type": uploaded_file.type,  # Get the mime type of the uploaded file
+                "data": bytes_data
+            }
+        ]
+        return image_parts
+    else:
+        raise FileNotFoundError("No file uploaded")
+    
+
+# streamlit setup
+st.set_page_config(layout="wide", page_title="Multilanguage 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 image")
+
+input_prompt = """
+ You are an expert in understanding invoices.
+               You will receive input images as invoices &
+               you will have to answer questions based on the input image
+
+"""
+
+# if submit button is clicked
+if submit:
+    image_data = input_image_details(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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+streamlit
+langchain
+google.generativeai
+python-dotenv
+PyPDF2
+chromadb # vector embeddings/vector stores
+