Create app.py

app.py

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+from dotenv import load_dotenv
+load_dotenv()
+
+import google.generativeai as genai
+import streamlit as st
+import os
+from PIL import Image
+
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+
+model=genai.GenerativeModel('gemini-pro-vision')
+
+def get_res(input,image,prompt):
+    res=model.generate_content([input,image[0],prompt])
+    return res.text
+
+def input_image_setup(uploaded_file):
+    if uploaded_file is not None:
+        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
+
+st.set_page_config("Multi-Language Invoice Extractor")
+st.header("Multi-Language Invoice Extractor")
+input=st.text_input("Input: ", key="input")
+file = st.file_uploader("Choose an Image of the Invoice", type=["jpg","jpeg","png"])
+
+image=""
+if file is not None:
+    image=Image.open(file)
+    st.image(image, caption="Uploaded Image: ", use_column_width=True)
+    
+submit=st.button("Tell me")
+
+input_prompt="""
+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.
+"""
+
+if submit:
+    image_data= input_image_setup(file)
+    res = get_res(input_prompt,image_data,input)
+    st.subheader("Response: ")
+    st.write(res)