update app.py

app.py

@@ -1,63 +1,110 @@
+import google.generativeai as genai
+from dotenv import load_dotenv
+import os
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+from PIL import Image
+import numpy as np
+
+load_dotenv()
+
+GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
+genai.configure(api_key=GOOGLE_API_KEY)
+
+def save_image(file_input, image_name):
+    # Convert the input to a PIL image
+    image_pil = Image.fromarray(np.uint8(file_input))
+    
+    # Define the directory where the image will be saved
+    save_directory = "images"
+    
+    # Check if the directory exists, create it if not
+    if not os.path.exists(save_directory):
+        os.makedirs(save_directory, exist_ok=True)
+    
+    # Define the full path to save the image
+    image_path = os.path.join(save_directory, image_name)
+    
+    # Save the image
+    image_pil.save(image_path)
+
+    return image_path
+
+def generate_response(text_input, file_inputs=None, chat_history=None):
+    # Upload the files (images) and print a confirmation.
+    image_paths = []
+    if file_inputs is not None:
+        for idx, file_input in enumerate(file_inputs):
+            image_name = f"image_{idx + 1}.jpg"
+            image_path = save_image(file_input, image_name)
+            image_paths.append(image_path)
+
+    # Choose a Gemini API model.
+    model = genai.GenerativeModel(model_name="gemini-1.5-flash")
+
+    # Initialize chat history if None
+    if chat_history is None:
+        chat_history = []
+
+    # Convert chat history into the required format for Gemini API
+    chat_history_content = []
+    for user_message, bot_response in chat_history:
+        chat_history_content.append({"role": "user", "parts": [{"text": user_message}]})
+        chat_history_content.append({"role": "model", "parts": [{"text": bot_response}]})
+
+    chat = model.start_chat(history=chat_history_content)
+
+    # Open images and pass them with text_input if available
+    images = [Image.open(image_path) for image_path in image_paths] if image_paths else None
+
+    # Prompt the model with text and the uploaded images if available
+    if images:
+        response = chat.send_message([*images, text_input])
+    else:
+        response = chat.send_message(text_input)
+
+    # Append the new message to chat history in Gradio format (user, bot)
+    chat_history.append((text_input, response.text))
+
+    return "", chat_history
+
+# Create a Gradio interface with Blocks
+with gr.Blocks(title="Gemini vision") as demo:
+    gr.Markdown("# Chat Bot M1N9")
+
+    # Define the Chatbot component
+    chatbot = gr.Chatbot([], elem_id="chatbot", height=700, show_share_button=True, show_copy_button=True)
+
+    # Define the Textbox and Image components
+    msg = gr.Textbox(show_copy_button=True, placeholder="Type your message here...")
+
+    # Row for multiple image inputs
+    with gr.Row():
+        img1 = gr.Image()
+        img2 = gr.Image()
+        img3 = gr.Image()
+        img4 = gr.Image()
+    
+    btn = gr.Button("Submit")
+
+    # Define the ClearButton component
+    clear = gr.ClearButton([msg, img1, img2, img3, img4, chatbot])
+
+    # Set the submit function for the Textbox and Image
+    def submit_message(msg, img1, img2, img3, img4, chat_history):
+        # Collect all images into a list
+        image_list = [img1, img2, img3, img4]
+        # Filter out None values in case fewer than 4 images are uploaded
+        image_list = [img for img in image_list if img is not None]
+        
+        # Call the generate_response with the list of images
+        response, chat_history = generate_response(msg, image_list, chat_history)
+        
+        # Return the updated chat history and clear input fields
+        return "", None, None, None, None, chat_history
+
+    # Bind the submit function to both the submit action of Textbox and the button click
+    msg.submit(submit_message, [msg, img1, img2, img3, img4, chatbot], [msg, img1, img2, img3, img4, chatbot])
+    btn.click(submit_message, [msg, img1, img2, img3, img4, chatbot], [msg, img1, img2, img3, img4, chatbot])
+
+# Launch the Gradio interface
+demo.launch(debug=True, share=True)