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app.py

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+### CELLSTRAT HUB PACK - LANGCHAIN05  
+
+### This Streamlit web application, titled "MultiLanguage Invoice Extractor," leverages Google's Generative AI Gemini Pro Vision model to analyze and extract data from uploaded invoice images. It begins by setting up the environment and importing necessary libraries, including Streamlit for the web interface and Google's Generative AI library. Users can upload an invoice image in various formats, which is then displayed on the screen. The app allows users to input a prompt regarding the invoice, and upon submission, it processes this input alongside the image data through the Gemini Pro Vision model. The app then displays the AI-generated insights or information extracted from the invoice, making it a practical tool for understanding and processing invoice data in multiple languages.
+
+
+from dotenv import load_dotenv
+load_dotenv() ##load all the environment variables from .env  
+
+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_details(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, #Get the mmime type of the uploaded file
+                "data":bytes_data
+            }
+        ]
+        return image_parts
+    else:
+        raise FileNotFoundError("No file uploaded")
+
+
+###initialize our streamlit app
+
+st.set_page_config(page_title="MultiLanguage Invoice Extractor")
+
+st.header("MultiLanguage Invoice Extractor")
+input=st.text_input("Input Prompt: ",key="input")
+uploaded_file=st.file_uploader("Choose an image of the invoice....",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. We will upload an image as invoice and you will have to answer any question based on the uploaded invoice 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
+google-generativeai
+python-dotenv
+langchain
+PyPDF2
+chromadb