Create app.py

app.py

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+
+# import required packages
+import google.generativeai as genai
+import os
+import PIL.Image
+import gradio as gr
+from gradio_multimodalchatbot import MultimodalChatbot
+from gradio.data_classes import FileData
+
+# For better security practices, retrieve sensitive information like API keys from environment variables.
+
+# Fetch an environment variable.
+GOOGLE_API_KEY = os.environ.get('GOOGLE_API_KEY')
+genai.configure(api_key=GOOGLE_API_KEY)
+
+# These codelines are just to verify if your api key is correct or not
+# Use them when you clone the repo and build locally
+#!curl \
+#-H 'Content-Type: application/json' \
+#-d '{ "prompt": { "text": "Write a very short story about a magic backpack"} }' \
+#"https://generativelanguage.googleapis.com/v1beta3/models/text-bison-001:generateText?key=<enter-your-key-here>"
+
+# Initialize genai models
+model = genai.GenerativeModel('gemini-pro')
+modelvis = genai.GenerativeModel('gemini-pro-vision')
+
+def gemini(input, file, chatbot=[]):
+    """
+    Function to handle gemini model and gemini vision model interactions.
+
+    Parameters:
+    input (str): The input text.
+    file (File): An optional file object for image processing.
+    chatbot (list): A list to keep track of chatbot interactions.
+
+    Returns:
+    tuple: Updated chatbot interaction list, an empty string, and None.
+    """
+
+    messages = []
+    print(chatbot)
+
+    # Process previous chatbot messages if present
+    if len(chatbot) != 0:
+        for user, bot in chatbot:
+            user, bot = user.text, bot.text
+            messages.extend([
+                {'role': 'user', 'parts': [user]},
+                {'role': 'model', 'parts': [bot]}
+            ])
+        messages.append({'role': 'user', 'parts': [input]})
+    else:
+        messages.append({'role': 'user', 'parts': [input]})
+
+    try:
+        # Process image if file is provided
+        if file is not None:
+            with PIL.Image.open(file.name) as img:
+                message = [{'role': 'user', 'parts': [input, img]}]
+                response = modelvis.generate_content(message)
+                gemini_video_resp = response.text
+                messages.append({'role': 'model', 'parts': [gemini_video_resp]})
+
+                # Construct list of messages in the required format
+                user_msg = {"text": input, "files": [{"file": FileData(path=file.name)}]}
+                bot_msg = {"text": gemini_video_resp, "files": []}
+                chatbot.append([user_msg, bot_msg])
+        else:
+            response = model.generate_content(messages)
+            gemini_resp = response.text
+
+            # Construct list of messages in the required format
+            user_msg = {"text": input, "files": []}
+            bot_msg = {"text": gemini_resp, "files": []}
+            chatbot.append([user_msg, bot_msg])
+    except Exception as e:
+        # Handling exceptions and raising error to the modal
+        print(f"An error occurred: {e}")
+        raise gr.Error(e)
+
+    return chatbot, "", None
+
+# Define the Gradio Blocks interface
+with gr.Blocks() as demo:
+    # Add a centered header using HTML
+    gr.HTML("<center><h1>Gemini-PRO & Gemini-PRO-Vision API</h1></center>")
+
+    # Initialize the MultimodalChatbot component
+    multi = MultimodalChatbot(value=[], height=800)
+
+    with gr.Row():
+        # Textbox for user input with increased scale for better visibility
+        tb = gr.Textbox(scale=4)
+
+        # Upload button for image files
+        up = gr.UploadButton("Upload Image", file_types=["image"], scale=1)
+
+    # Define the behavior on text submission
+    tb.submit(gemini, [tb, up, multi], [multi, tb, up])
+
+    # Define the behavior on image upload
+    # Using chained then() calls to update the upload button's state
+    up.upload(lambda: gr.UploadButton("Uploading Image..."), [], up) \
+       .then(lambda: gr.UploadButton("Image Uploaded"), [], up) \
+       .then(lambda: gr.UploadButton("Upload Image"), [], up)
+
+# Launch the demo with a queue to handle multiple users
+demo.queue().launch()