gradio 5

app.py

@@ -1,28 +1,19 @@
 import argparse
 import os
-os.system("pip uninstall -y gradio")
-os.system("pip install gradio==3.47.1")
-
 import random
 from collections import defaultdict
-
 import cv2
 import re
-
 import numpy as np
 from PIL import Image
 import torch
 import html
 import gradio as gr
-
 import torchvision.transforms as T
 import torch.backends.cudnn as cudnn
-
 from minigpt4.common.config import Config
-
 from minigpt4.common.registry import registry
 from minigpt4.conversation.conversation import Conversation, SeparatorStyle, Chat
-
 # imports modules for registration
 from minigpt4.datasets.builders import *
 from minigpt4.models import *
@@ -32,6 +23,7 @@ from minigpt4.tasks import *
 
 import socket
 import os
+import spaces
 
 def find_free_port(start_port, end_port):
     for port in range(start_port, end_port + 1):
@@ -173,6 +165,10 @@ def escape_markdown(text):
 
 
 def reverse_escape(text):
+    # Add safety check for None values
+    if text is None:
+        return ""
+    
     md_chars = ['\\<', '\\>']
 
     for char in md_chars:
@@ -229,6 +225,8 @@ def visualize_all_bbox_together(image, generation):
     
     if isinstance(image, str):  # is a image path
         raw_image = get_first_frame(image)
+        if raw_image is None:
+            return None, ''
         frame_rgb = cv2.cvtColor(raw_image, cv2.COLOR_BGR2RGB)
         image = Image.fromarray(frame_rgb)
 
@@ -431,27 +429,20 @@ def gradio_reset(chat_state, img_list):
                                                                     interactive=True), chat_state, img_list
 
 
-def image_upload_trigger(upload_flag, replace_flag, img_list):
+def image_upload_trigger(gr_img, upload_flag, replace_flag, img_list):
     # set the upload flag to true when receive a new image.
     # if there is an old image (and old conversation), set the replace flag to true to reset the conv later.
+    print(f"Image upload triggered: {gr_img}")
     upload_flag = 1
     if img_list:
         replace_flag = 1
     return upload_flag, replace_flag
 
 
-def example_trigger(text_input, image, upload_flag, replace_flag, img_list):
-    # set the upload flag to true when receive a new image.
-    # if there is an old image (and old conversation), set the replace flag to true to reset the conv later.
-    upload_flag = 1
-    if img_list or replace_flag == 1:
-        replace_flag = 1
-
-    return upload_flag, replace_flag
-
-
 def gradio_ask(user_message, chatbot, chat_state, gr_img, img_list, upload_flag, replace_flag):    
     print("+++gradio_ask+++")
+    print(f"gr_img: {gr_img}, type: {type(gr_img)}")
+    print(f"upload_flag: {upload_flag}, replace_flag: {replace_flag}")
     
     if len(user_message) == 0:
         text_box_show = 'Input should not be empty!'
@@ -462,7 +453,6 @@ def gradio_ask(user_message, chatbot, chat_state, gr_img, img_list, upload_flag,
     print('chatbot:', chatbot)
     print('chat_state:', chat_state)
 
-
     if isinstance(gr_img, dict):
         gr_img, mask = gr_img['image'], gr_img['mask']
     else:
@@ -478,14 +468,22 @@ def gradio_ask(user_message, chatbot, chat_state, gr_img, img_list, upload_flag,
     if chat_state is None:
         chat_state = CONV_VISION.copy()
 
-    if upload_flag:
+    # Always process the image if it exists and upload_flag is set or img_list is empty
+    if gr_img is not None and (upload_flag or len(img_list) == 0):
         if replace_flag:
             chat_state = CONV_VISION.copy()  # new image, reset everything
             replace_flag = 0
             chatbot = []
         img_list = []
-        llm_message = chat.upload_img(gr_img, chat_state, img_list)
+        try:
+            llm_message = chat.upload_img(gr_img, chat_state, img_list)
+            print(f"Image uploaded successfully. img_list length: {len(img_list)}")
+        except Exception as e:
+            print(f"Error uploading image: {e}")
+            return "Error uploading image. Please try again.", chatbot, chat_state, img_list, 0, replace_flag
         upload_flag = 0
+    elif gr_img is None:
+        return "Please upload a video first.", chatbot, chat_state, img_list, upload_flag, replace_flag
 
     chat.ask(user_message, chat_state)
     print('user_message: ', user_message)
@@ -531,32 +529,56 @@ def process_english_text(text):
 
     return text
 
-
+@spaces.GPU
 def gradio_stream_answer(chatbot, chat_state, img_list, temperature):
     print('---gradio_stream_answer---')
+    print(f"img_list length: {len(img_list)}")
+    
+    # Check if img_list is empty
+    if len(img_list) == 0:
+        error_msg = "No image/video uploaded. Please upload a video first."
+        print(error_msg)
+        if len(chatbot) > 0:
+            chatbot[-1][1] = error_msg
+        yield chatbot, chat_state
+        return
+    
     if len(img_list) > 0:
         if not isinstance(img_list[0], torch.Tensor):
             chat.encode_img(img_list)
     print(chat)
-    streamer = chat.stream_answer(conv=chat_state,
-                                  img_list=img_list,
-                                  temperature=temperature,
-                                  max_new_tokens=500,
-                                  max_length=2000)
-    output = ''
-    print('streamer:', streamer)
-    for new_output in streamer:
-        escapped = escape_markdown(new_output)
-        output += escapped
-        chatbot[-1][1] = output
-        chatbot[-1][1] = process_english_text(chatbot[-1][1])
+    
+    try:
+        streamer = chat.stream_answer(conv=chat_state,
+                                      img_list=img_list,
+                                      temperature=temperature,
+                                      max_new_tokens=500,
+                                      max_length=2000)
+        output = ''
+        print('streamer:', streamer)
+        for new_output in streamer:
+            escapped = escape_markdown(new_output)
+            output += escapped
+            chatbot[-1][1] = output
+            chatbot[-1][1] = process_english_text(chatbot[-1][1])
+            yield chatbot, chat_state
+        chat_state.messages[-1][1] = '</s>'
+        print('output:', output)
+    except Exception as e:
+        error_msg = f"Error generating response: {str(e)}"
+        print(error_msg)
+        if len(chatbot) > 0:
+            chatbot[-1][1] = error_msg
         yield chatbot, chat_state
-    chat_state.messages[-1][1] = '</s>'
-    print('output:', output)
+    
     return chatbot, chat_state
 
 
 def gradio_visualize(chatbot, gr_img):
+    # Safety check for empty chatbot or None response
+    if len(chatbot) == 0 or chatbot[-1][1] is None:
+        return chatbot
+        
     if isinstance(gr_img, dict):
         gr_img, mask = gr_img['image'], gr_img['mask']
 
@@ -589,8 +611,6 @@ def gradio_taskselect(idx):
     return prompt_list[idx], instruct_list[idx]
 
 
-
-
 chat = Chat(model, vis_processor, device=device)
 
 title = """<h1 align="center">Emotion-LLaMA Demo</h1>"""
@@ -604,11 +624,11 @@ For Abilities Involging Multimodal Emotion Understanding:
 3. Visual: Click **Send** to generate a visual description.
 4. Audio: Click **Send** to generate an audio description.
 5. No Tag: Input whatever you want and click **Send** without any tagging.
-
 You can also simply chat in free form!
 '''
 
 text_input = gr.Textbox(placeholder='Upload your image and chat', interactive=True, show_label=False, container=False, scale=8)
+
 with gr.Blocks() as demo:
     gr.Markdown(title)
     # gr.Markdown(description)
@@ -637,6 +657,7 @@ with gr.Blocks() as demo:
             img_list = gr.State(value=[])
             chatbot = gr.Chatbot(label='Emotion-LLaMA')
 
+            # Updated Dataset component for Gradio 5
             dataset = gr.Dataset(
                 components=[gr.Textbox(visible=False)],
                 samples=[['No Tag'], ['reason'], ['emotion'], ['visual'], ['audio']],
@@ -650,36 +671,44 @@ with gr.Blocks() as demo:
 
     upload_flag = gr.State(value=0)
     replace_flag = gr.State(value=0)
-    image.upload(image_upload_trigger, [upload_flag, replace_flag, img_list], [upload_flag, replace_flag])
+    
+    # Updated upload trigger for Gradio 5 - fixed parameter order
+    image.upload(image_upload_trigger, [image, upload_flag, replace_flag, img_list], [upload_flag, replace_flag])
 
+    # Updated Examples component for Gradio 5 - this is the key fix!
     with gr.Row():
         with gr.Column():
-            gr.Examples(examples=[
-                ["examples/samplenew_00004251.mp4", "[detection] face", upload_flag, replace_flag, img_list],
-                ["examples/sample_00000338.mp4", "The person in video says: Oh no, my phone and wallet are all in my bag. [emotion] Please determine which emotion label in the video represents: happy, sad, neutral, angry, worried, surprise.", upload_flag, replace_flag, img_list],
-                ["examples/sample_00000669.mp4", "The person in video says: Why are you looking at me like this? It's just a woman, so you have to have something to do with me.  [emotion] Determine the emotional state shown in the video, choosing from happy, sad, neutral, angry, worried, or surprise.", upload_flag, replace_flag, img_list],
-                ["examples/sample_00003462.mp4", "The person in video says: Do you believe that you push me around?  [emotion] Assess and label the emotion evident in the video: could it be happy, sad, neutral, angry, worried, surprise?", upload_flag, replace_flag, img_list],
-                ["examples/sample_00000727.mp4", "The person in video says: No, this, I have to get up! You, I'm sorry, everyone. I'm sorry, it's from the German side.  [emotion] Identify the displayed emotion in the video: is it happy, sad, neutral, angry, worried, or surprise?", upload_flag, replace_flag, img_list],
-                ["examples/samplenew_00061200.mp4", "The person in video says: Me: I'm not going in anymore, scared.  [emotion] Identify the displayed emotion in the video: is it happy, sad, neutral, angry, fear, contempt, doubt, worried, or surprise?", upload_flag, replace_flag, img_list],
-            ], inputs=[image, text_input, upload_flag, replace_flag, img_list], fn=example_trigger,
-                outputs=[upload_flag, replace_flag])
+            examples1 = gr.Examples(
+                examples=[
+                    ["examples/samplenew_00004251.mp4", "[detection] face"],
+                    ["examples/sample_00000338.mp4", "The person in video says: Oh no, my phone and wallet are all in my bag. [emotion] Please determine which emotion label in the video represents: happy, sad, neutral, angry, worried, surprise."],
+                    ["examples/sample_00000669.mp4", "The person in video says: Why are you looking at me like this? It's just a woman, so you have to have something to do with me.  [emotion] Determine the emotional state shown in the video, choosing from happy, sad, neutral, angry, worried, or surprise."],
+                    ["examples/sample_00003462.mp4", "The person in video says: Do you believe that you push me around?  [emotion] Assess and label the emotion evident in the video: could it be happy, sad, neutral, angry, worried, surprise?"],
+                    ["examples/sample_00000727.mp4", "The person in video says: No, this, I have to get up! You, I'm sorry, everyone. I'm sorry, it's from the German side.  [emotion] Identify the displayed emotion in the video: is it happy, sad, neutral, angry, worried, or surprise?"],
+                    ["examples/samplenew_00061200.mp4", "The person in video says: Me: I'm not going in anymore, scared.  [emotion] Identify the displayed emotion in the video: is it happy, sad, neutral, angry, fear, contempt, doubt, worried, or surprise?"],
+                ],
+                inputs=[image, text_input],
+                # Remove fn and outputs - let Examples handle this automatically in Gradio 5
+            )
         with gr.Column():
-            gr.Examples(examples=[
-                ["examples/samplenew_00051251.mp4", "In what state is the person in the video, say the following: \"Do you really think so?\"", upload_flag, replace_flag, img_list],
-                ["examples/sample_00004735.mp4", "[visual] What are the emotions of the woman in the video?", upload_flag, replace_flag, img_list],
-                ["examples/sample_00002422.mp4", "[audio] Analyze the speaker's voice in the video.", upload_flag, replace_flag, img_list],
-                ["examples/sample_00001073.mp4", "The person in video says: Make him different from before. I like the way you are now.  [reason] Please analyze all the clues in the video and reason out the emotional label of the person in the video.", upload_flag, replace_flag, img_list],
-                ["examples/sample_00004671.mp4", "The person in video says: Won't you? Impossible! Fan Xiaomei is not such a person.  [reason] What are the facial expressions and vocal tone used in the video? What is the intended meaning behind his words? Which emotion does this reflect?", upload_flag, replace_flag, img_list],
-                ["examples/sample_00005854.mp4", "The person in video says: Bastard! Boss, you don't choose, you prefer.  [reason] Please integrate information from various modalities to infer the emotional category of the person in the video.", upload_flag, replace_flag, img_list],
-            ], inputs=[image, text_input, upload_flag, replace_flag, img_list], fn=example_trigger,
-                outputs=[upload_flag, replace_flag])
+            examples2 = gr.Examples(
+                examples=[
+                    ["examples/samplenew_00051251.mp4", "In what state is the person in the video, say the following: \"Do you really think so?\""],
+                    ["examples/sample_00004735.mp4", "[visual] What are the emotions of the woman in the video?"],
+                    ["examples/sample_00002422.mp4", "[audio] Analyze the speaker's voice in the video."],
+                    ["examples/sample_00001073.mp4", "The person in video says: Make him different from before. I like the way you are now.  [reason] Please analyze all the clues in the video and reason out the emotional label of the person in the video."],
+                    ["examples/sample_00004671.mp4", "The person in video says: Won't you? Impossible! Fan Xiaomei is not such a person.  [reason] What are the facial expressions and vocal tone used in the video? What is the intended meaning behind his words? Which emotion does this reflect?"],
+                    ["examples/sample_00005854.mp4", "The person in video says: Bastard! Boss, you don't choose, you prefer.  [reason] Please integrate information from various modalities to infer the emotional category of the person in the video."],
+                ],
+                inputs=[image, text_input],
+                # Remove fn and outputs - let Examples handle this automatically in Gradio 5
+            )
 
     dataset.click(
         gradio_taskselect,
         inputs=[dataset],
         outputs=[text_input, task_inst],
         show_progress="hidden",
-        postprocess=False,
         queue=False,
     )
 
@@ -687,11 +716,11 @@ with gr.Blocks() as demo:
         gradio_ask,
         [text_input, chatbot, chat_state, image, img_list, upload_flag, replace_flag],
         [text_input, chatbot, chat_state, img_list, upload_flag, replace_flag], queue=False
-    ).success(
+    ).then(
         gradio_stream_answer,
         [chatbot, chat_state, img_list, temperature],
         [chatbot, chat_state]
-    ).success(
+    ).then(
         gradio_visualize,
         [chatbot, image],
         [chatbot],
@@ -702,11 +731,11 @@ with gr.Blocks() as demo:
         gradio_ask,
         [text_input, chatbot, chat_state, image, img_list, upload_flag, replace_flag],
         [text_input, chatbot, chat_state, img_list, upload_flag, replace_flag], queue=False
-    ).success(
+    ).then(
         gradio_stream_answer,
         [chatbot, chat_state, img_list, temperature],
         [chatbot, chat_state]
-    ).success(
+    ).then(
         gradio_visualize,
         [chatbot, image],
         [chatbot],
@@ -715,5 +744,5 @@ with gr.Blocks() as demo:
 
     clear.click(gradio_reset, [chat_state, img_list], [chatbot, image, text_input, chat_state, img_list], queue=False)
 
-demo.launch(share=True, enable_queue=True)
-# demo.launch(share=True)
+demo.queue()
+demo.launch(share=True)

requirements.txt

@@ -1,5 +1,3 @@
-gradio==3.47.1
-gradio_client==0.6.0
 decorator==4.4.2
 moviepy==1.0.3
 decord==0.6.0
@@ -15,6 +13,9 @@ bitsandbytes==0.41.0
 scipy
 huggingface_hub
 torch==2.1.2
-torchvision==0.15.1
+torchvision
 timm==0.6.13
 transformers==4.30.0
+gradio
+gradio_client
+numpy<2.0