Commit

app.py

@@ -1,69 +1,70 @@
-###        Invoice extractor
-
-##Importing All the modules
-import streamlit as st
-import os
-from PIL import Image
-import google.generativeai as genai
-from dotenv import load_dotenv
-
-
-# Load all environment Variables
-load_dotenv()
-
-##Configuring the api key
-genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
-
-## Function to load Gemini Vison Pro Vision Model and Get response
-
-def get_gemini_response(input,image,prompt):
-
-    ##Loading the desired Model
-    model= genai.GenerativeModel("gemini-pro-vision")
-    response=model.generate_content([input,image[0],prompt])
-    return response.text
-
-## Function to extract data from Image Uploaded
-def input_image_setup(uploaded_file):
-    # Check if a file has been uploaded
-    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 mime type of the uploaded file
-                "data": bytes_data
-            }
-        ]
-        return image_parts
-    else:
-        raise FileNotFoundError("No file uploaded")
-    
-#       Initializing our Streamlit Prompt
-
-st.set_page_config(page_title="Gemini Image Demo")
-
-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 image")
-
-##  DEFINING A SYSTEM PROMPT
-
-input_prompt = "You are an expert in understanding invoices.You will receive invoices as input images and you will have to answer question based on the input image."
-
-if submit:
-    image_data = input_image_setup(uploaded_file)
-    response = get_gemini_response(input_prompt,image_data,input)
-
-    st.subheader("The Response Generated by your model Gemini Pro Vision is: ")
-    st.write(response)
-
-    st.balloons()
+###        Invoice extractor
+
+##Importing All the modules
+import streamlit as st
+import os
+from PIL import Image
+import google.generativeai as genai
+from dotenv import load_dotenv
+
+
+# Load all environment Variables
+load_dotenv()
+
+##Configuring the api key
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+
+## Function to load Gemini Vison Pro Vision Model and Get response
+
+def get_gemini_response(input,image,prompt):
+
+    ##Loading the desired Model
+    model= genai.GenerativeModel("gemini-pro-vision")
+    response=model.generate_content([input,image[0],prompt])
+    return response.text
+
+## Function to extract data from Image Uploaded
+def input_image_setup(uploaded_file):
+    # Check if a file has been uploaded
+    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 mime type of the uploaded file
+                "data": bytes_data
+            }
+        ]
+        return image_parts
+    else:
+        raise FileNotFoundError("No file uploaded")
+    
+#       Initializing our Streamlit Prompt
+
+st.set_page_config(page_title="Gemini Image Demo")
+
+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 image")
+
+##  DEFINING A SYSTEM PROMPT
+
+input_prompt = "You are an expert in understanding invoices.You will receive invoices as input images and you will have to answer question based on the input image."
+
+if submit:
+    image_data = input_image_setup(uploaded_file)
+    response = get_gemini_response(input_prompt,image_data,input)
+
+    st.subheader("The Response Generated by your model Gemini Pro Vision is: ")
+    st.write(response)
+        st.write("Follow me on LinkedIn:  https://www.linkedin.com/in/mohammed-musharraf-44a98a280/ ")
+
+    st.balloons()