App

.env

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+GOOGLE_API_KEY="AIzaSyAVhmqKi7Tzc5RGSIdAI5LsVXu2W9NEU0A"

app.py

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+from dotenv import load_dotenv
+load_dotenv() 
+
+import streamlit as st
+import os 
+from PIL import Image
+import google.generativeai as genai
+
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+
+## function to load Gemini pro vision
+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_setup(upload_file):
+    if upload_file is not None:
+        bytes_data = upload_file.getvalue()
+        image_parts = [
+            {
+                "mime_type": upload_file.type,
+                "data": bytes_data
+            }
+        ]
+        return image_parts
+    else:
+        raise FileNotFoundError("No file uploaded")
+
+st.set_page_config(page_title="Multilanguage Invoice Extractor")
+
+st.header("Gemini Application")
+input = st.text_input("Input Prompt: ", key="input")
+upload_file = st.file_uploader("Choose an image of invoice ", type=["jpg", "jpeg", "png"])
+
+image=""
+if upload_file is not None:
+    image = Image.open(upload_file)
+    st.image(image, caption="uploaded Image", use_column_width=True)
+
+submit = st.button("Tell me about invoice")
+
+input_prompt ="""
+You are an export in understanding invoices. 
+we will upload an image as invoice and you will habe to answer any question based on uploaded invoice image
+"""
+
+if submit:
+    image_data = input_image_setup(upload_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
+google-generativeai
+python-dotenv
+langchain
+PyPDF2
+chromadb