Update app.py

app.py

@@ -1,69 +1,69 @@
-##Invoice Extractor
-
-from dotenv import load_dotenv
-
-load_dotenv() #it will take all env variables from .env file
-
-import streamlit as st
-import os
-from PIL import Image
-import google.generativeai as genai
-
-##congigure API Key
-
-genai.configure(api_key="AIzaSyBTfFJH5arruUT21R-kvloH0Xyzj8wAmn8")
-
-##function to load gemini pro vision model and get response
-
-def get_gemini_response(input,image,prompt):
-
-    ##loading the gemini model
-    model =genai.GenerativeModel('gemini-pro-vision')
-    response =model.generate_content([input,image[0],prompt])
-    return response.text
-
-def input_image_setup(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,
-                "data": bytes_data
-            }
-        ] 
-        return image_parts
-    else:
-        raise FileNotFoundError("No file uploaded")
-
-
-#streamlit app
-
-st.set_page_config(page_title="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 invoice")
-
-input_prompt ="""
-you are an expert in understanding invoices. you will 
-receive input images as invoices and you will have to
-answer questions based on the input image.
-"""
-
-## If asubmit button is clicked
-
-if submit:
-    image_data = input_image_setup(uploaded_file)
-    response=get_gemini_response(input_prompt,image_data,input)
-
-    st.subheader("The Response is")
-    st.write(response)
+##Invoice Extractor
+
+from dotenv import load_dotenv
+
+load_dotenv() #it will take all env variables from .env file
+
+import streamlit as st
+import os
+from PIL import Image
+import google.generativeai as genai
+
+##congigure API Key
+
+genai.configure(api_key="add your own created api key")
+
+##function to load gemini pro vision model and get response
+
+def get_gemini_response(input,image,prompt):
+
+    ##loading the gemini model
+    model =genai.GenerativeModel('gemini-pro-vision')
+    response =model.generate_content([input,image[0],prompt])
+    return response.text
+
+def input_image_setup(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,
+                "data": bytes_data
+            }
+        ] 
+        return image_parts
+    else:
+        raise FileNotFoundError("No file uploaded")
+
+
+#streamlit app
+
+st.set_page_config(page_title="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 invoice")
+
+input_prompt ="""
+you are an expert in understanding invoices. you will 
+receive input images as invoices and you will have to
+answer questions based on the input image.
+"""
+
+## If asubmit button is clicked
+
+if submit:
+    image_data = input_image_setup(uploaded_file)
+    response=get_gemini_response(input_prompt,image_data,input)
+
+    st.subheader("The Response is")
+    st.write(response)