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from dotenv import load_dotenv |
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load_dotenv() |
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import google.generativeai as genai |
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import streamlit as st |
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import os |
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from PIL import Image |
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genai.configure(api_key=os.getenv("GOOGLE_API_KEY")) |
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model=genai.GenerativeModel('gemini-pro-vision') |
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def get_res(input,image,prompt): |
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res=model.generate_content([input,image[0],prompt]) |
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return res.text |
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def input_image_setup(uploaded_file): |
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if uploaded_file is not None: |
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bytes_data=uploaded_file.getvalue() |
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image_parts =[ |
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{ |
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"mime_type": uploaded_file.type, |
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"data": bytes_data |
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} |
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] |
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return image_parts |
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else: |
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raise FileNotFoundError("No File Uploaded") |
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st.set_page_config("Multi-Language Invoice Extractor") |
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st.header("Multi-Language Invoice Extractor") |
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input=st.text_input("Input: ", key="input") |
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file = st.file_uploader("Choose an Image of the Invoice", type=["jpg","jpeg","png"]) |
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image="" |
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if file is not None: |
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image=Image.open(file) |
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st.image(image, caption="Uploaded Image: ", use_column_width=True) |
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submit=st.button("Tell me") |
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input_prompt=""" |
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ou are an expert in invoice analysis. I will upload an invoice image, and you need to answer any questions I ask based on the details in the image. |
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""" |
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if submit: |
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image_data= input_image_setup(file) |
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res = get_res(input_prompt,image_data,input) |
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st.subheader("Response: ") |
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st.write(res) |