Update app.py

app.py

@@ -67,7 +67,7 @@ model = AutoModel.from_pretrained(
 
 tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False)
 
-# Define the function for Gradio interface
+# Define the function for Gradio image interface
 def process_image(image):
     try:
         pixel_values = load_image(image, max_num=12).to(torch.bfloat16).cuda()
@@ -78,14 +78,31 @@ def process_image(image):
     except Exception as e:
         return f"Error: {str(e)}"
 
-# Create Gradio Interface
-demo = gr.Interface(
-    fn=process_image,
-    inputs=gr.Image(type="pil"),
-    outputs="text",
-    title="Dynamic Image Processing with InternVL",
-    description="Upload an image and get detailed responses using the InternVL model."
-)
+# Define the function for text-based chatbot interface
+def chatbot(input_text, history=[]):
+    try:
+        generation_config = dict(max_new_tokens=1024, do_sample=True)
+        response, updated_history = model.chat(tokenizer, None, input_text, generation_config, history=history, return_history=True)
+        return response, updated_history
+    except Exception as e:
+        return f"Error: {str(e)}", history
+
+# Create Gradio Tabs
+with gr.Blocks() as demo:
+    with gr.Tab("Image Processing"):
+        gr.Markdown("Upload an image and get detailed responses using the InternVL model.")
+        image_input = gr.Image(type="pil")
+        image_output = gr.Textbox(label="Response")
+        image_btn = gr.Button("Process")
+        image_btn.click(process_image, inputs=image_input, outputs=image_output)
+
+    with gr.Tab("Chatbot"):
+        gr.Markdown("Chat with the model.")
+        chatbot_input = gr.Textbox(label="Your Message")
+        chatbot_output = gr.Textbox(label="Response")
+        chatbot_history = gr.State([])
+        chatbot_btn = gr.Button("Send")
+        chatbot_btn.click(chatbot, inputs=[chatbot_input, chatbot_history], outputs=[chatbot_output, chatbot_history])
 
 # Launch the Gradio app
 if __name__ == "__main__":