Update app.py

app.py

@@ -1,64 +1,64 @@
-import streamlit as st
-from dotenv import load_dotenv
-
-load_dotenv() ## load all env vriables from .env
-
-import os
-
-from PIL import Image
-from google.generativeai as genai 
-
-genai.configure(api_key = os.getenv('GOOGLE_API_KEY'))
-
-# create a function to load gemini pro vision
-model = genai.GenerativeMode('gemini-pro-vision')
-
-def get_gemini_response(input,image,prompt):
-    response = model.generate_content([input,image[0],prompt])
-    return response.text
-
-## create a function to return images bayets data
-
-def input_image_details(uploaded_file):
-    if uploaded_file is not None :
-        # read the file into a bytes
-        bytes_data = uploaded_file.getvalue()
-
-        image_parts = [
-            {
-                "mime_type" : uploaded_file.type, # get the mime 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("Gemini Application")
-
-input = st.text_input('Input Prompt: ',key = 'input')
-uploaded_file = st.file_uploader('Choose an image of the Invoice',type = ["jpg",'jpeg','png'])
-
-if uploaded_file is not None :
-    image = Image.open(uploaded_file)
-    st.image(image,caption='Uploaded Image', use_colume_width = True)
-
-submit = st.button("Tell me about the invoice")
-
-input_prompt = """
-you are a expert in understaning invoices. we will upload an image as invoice
-and you will have to answer any questions based on the uploaded invoice image
-"""
-
-# if submit button is clicked
-
-if sumbit :
-    image_data = input_image_details(uploaded_file)
-    response = get_gemini_response(input_prompt,image_data,input)
-    st.subheader("The Response is ")
-    st.write(response)
-    
-
+import streamlit as st
+from dotenv import load_dotenv
+
+load_dotenv() ## load all env vriables from .env
+
+import os
+
+from PIL import Image
+import google.generativeai as genai 
+
+genai.configure(api_key = os.getenv('GOOGLE_API_KEY'))
+
+# create a 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
+
+## create a function to return images bayets data
+
+def input_image_details(uploaded_file):
+    if uploaded_file is not None :
+        # read the file into a bytes
+        bytes_data = uploaded_file.getvalue()
+
+        image_parts = [
+            {
+                "mime_type" : uploaded_file.type, # get the mime 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("Gemini Application")
+
+input = st.text_input('Input Prompt: ',key = 'input')
+uploaded_file = st.file_uploader('Choose an image of the Invoice',type = ["jpg",'jpeg','png'])
+
+if uploaded_file is not None :
+    image = Image.open(uploaded_file)
+    st.image(image,caption='Uploaded Image', use_colume_width = True)
+
+submit = st.button("Tell me about the invoice")
+
+input_prompt = """
+you are a expert in understaning invoices. we will upload an image as invoice
+and you will have to answer any questions 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)
+    
+