Upload app.py

app.py

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+"""
+Gemini Pro demo.
+This example demonstrates how to use the Gemini protocol over a StreamLit app, and
+how to use the Gemini Pro API to generate text from images.
+
+About Developer:
+    LinkedIn: @pydev
+    Email: [email protected]
+    Github: @pydevpk
+    Twitter: @mrraosahab
+    Website: https://pydevpk.github.io
+"""
+
+from dotenv import load_dotenv
+
+# load environment variables 
+load_dotenv()
+
+import os
+import streamlit as st 
+import google.generativeai as genai 
+from PIL import Image
+
+genai.configure(api_key=os.getenv('API_KEY_GEMINI'))
+
+model = genai.GenerativeModel('gemini-pro-vision')
+
+
+def get_gemini_response(input, image):
+    if input != "":
+        response = model.generate_content([input, image])
+        return response
+    else:
+        response = model.generate_content(image)
+        return response
+    
+
+# setting webpage title 
+st.title("Gemini Pro")
+# setting webpage header 
+st.subheader("A generative AI that can create text from images")
+# take an input
+input = st.text_input("Enter a text prompt", "")
+# take an image
+uploader = st.file_uploader("Upload an image", type=['png', 'jpg', 'jpeg'])
+image = ""
+if not uploader is None:
+    image = uploader.read()
+    image = Image.open(uploader)
+    st.image(image, caption='Uploaded Image.', use_column_width=True)
+
+# creating button on webpage
+submit = st.button("know this image")
+if submit:
+    loader_text = st.empty()
+    loader_text.write('Classifying...')
+    # st.write("Classifying...")
+    # Classify the image
+    response = get_gemini_response(input, image)
+    # Display the results
+    st.write(response.text)
+    # replace loader_text 
+    loader_text.write('Response from Gemini Pro')