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Update web_demo_streamlit-minicpmv2_6.py
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1. Avoid using 'None' string when `user_text` is empty.
2. Added `st.spinner` to display a loading message during AI content generation.
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@JamePeng
JamePeng authored Aug 26, 2024
1 parent 48ed792 commit dc5f809
Showing 1 changed file with 91 additions and 85 deletions.
176 changes: 91 additions & 85 deletions web_demo_streamlit-minicpmv2_6.py
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Original file line number Diff line number Diff line change
Expand Up @@ -173,91 +173,97 @@ def uniform_sample(frame_indices, num_samples):
return frames



# User input box
user_text = st.chat_input("Enter your question")
if user_text:
# Display user input and save it to session history
with st.chat_message(U_NAME, avatar="user"):
st.session_state.chat_history.append({
"role": "user",
"content": user_text,
"image": None,
"video": None
})
st.markdown(f"{U_NAME}: {user_text}")

# Generate responses using the model
model = st.session_state.model
tokenizer = st.session_state.tokenizer
content_list = [] # Store the content (text or image) that will be passed into the model
imageFile = None

with st.chat_message(A_NAME, avatar="assistant"):
# Handle different inputs depending on the mode selected by the user
if selected_mode == "Single Image":
# Single image mode: pass in the last uploaded image
print("Single Images mode in use")
if len(st.session_state.chat_history) > 1 and len(st.session_state.uploaded_image_list) >= 1:
uploaded_image = st.session_state.uploaded_image_list[-1]
if uploaded_image:
imageFile = Image.open(uploaded_image).convert('RGB')
content_list.append(imageFile)
else:
print("Single Images mode: No image found")

elif selected_mode == "Multiple Images":
# Multi-image mode: pass in all the images uploaded last time
print("Multiple Images mode in use")
if len(st.session_state.chat_history) > 1 and st.session_state.uploaded_image_num >= 1:
for uploaded_image in st.session_state.uploaded_image_list:
imageFile = Image.open(uploaded_image).convert('RGB')
content_list.append(imageFile)
else:
print("Multiple Images mode: No image found")

elif selected_mode == "Video":
# Video mode: pass in slice frames of uploaded video
print("Video mode in use")
if len(st.session_state.chat_history) > 1 and st.session_state.uploaded_video_num == 1:
uploaded_video_path = st.session_state.uploaded_video_list[-1]
if uploaded_video_path:
frames = encode_video(uploaded_video_path)
if user_text is not None:
if user_text.strip() is "":
st.warning('Input message could not be empty!', icon="⚠️")
else:
# Display user input and save it to session history
with st.chat_message(U_NAME, avatar="user"):
st.session_state.chat_history.append({
"role": "user",
"content": user_text,
"image": None,
"video": None
})
st.markdown(f"{U_NAME}: {user_text}")

# Generate responses using the model
model = st.session_state.model
tokenizer = st.session_state.tokenizer
content_list = [] # Store the content (text or image) that will be passed into the model
imageFile = None

with st.chat_message(A_NAME, avatar="assistant"):
# Handle different inputs depending on the mode selected by the user
if selected_mode == "Single Image":
# Single image mode: pass in the last uploaded image
print("Single Images mode in use")
if len(st.session_state.chat_history) > 1 and len(st.session_state.uploaded_image_list) >= 1:
uploaded_image = st.session_state.uploaded_image_list[-1]
if uploaded_image:
imageFile = Image.open(uploaded_image).convert('RGB')
content_list.append(imageFile)
else:
print("Single Images mode: No image found")

elif selected_mode == "Multiple Images":
# Multi-image mode: pass in all the images uploaded last time
print("Multiple Images mode in use")
if len(st.session_state.chat_history) > 1 and st.session_state.uploaded_image_num >= 1:
for uploaded_image in st.session_state.uploaded_image_list:
imageFile = Image.open(uploaded_image).convert('RGB')
content_list.append(imageFile)
else:
print("Multiple Images mode: No image found")

elif selected_mode == "Video":
# Video mode: pass in slice frames of uploaded video
print("Video mode in use")
if len(st.session_state.chat_history) > 1 and st.session_state.uploaded_video_num == 1:
uploaded_video_path = st.session_state.uploaded_video_list[-1]
if uploaded_video_path:
frames = encode_video(uploaded_video_path)
else:
print("Video Mode: No video found")

# Defining model parameters
params = {
'sampling': True,
'top_p': top_p,
'top_k': top_k,
'temperature': temperature,
'repetition_penalty': repetition_penalty,
"max_new_tokens": max_length,
"stream": True
}

# Set different input parameters depending on whether to upload a video
if st.session_state.uploaded_video_num == 1 and selected_mode == "Video":
msgs = [{"role": "user", "content": frames + [user_text]}]
# Set decode params for video
params["max_inp_length"] = 4352 # Set the maximum input length of the video mode
params["use_image_id"] = False # Do not use image_id
params["max_slice_nums"] = 1 # # use 1 if cuda OOM and video resolution > 448*448
else:
print("Video Mode: No video found")

# Defining model parameters
params = {
'sampling': True,
'top_p': top_p,
'top_k': top_k,
'temperature': temperature,
'repetition_penalty': repetition_penalty,
"max_new_tokens": max_length,
"stream": True
}

# Set different input parameters depending on whether to upload a video
if st.session_state.uploaded_video_num == 1 and selected_mode == "Video":
msgs = [{"role": "user", "content": frames + [user_text]}]
# Set decode params for video
params["max_inp_length"] = 4352 # Set the maximum input length of the video mode
params["use_image_id"] = False # Do not use image_id
params["max_slice_nums"] = 1 # # use 1 if cuda OOM and video resolution > 448*448
else:
content_list.append(user_text)
msgs = [{"role": "user", "content": content_list}]

print("content_list:", content_list) # debug
print("params:", params) # debug

# Generate and display the model's responses
response = model.chat(image=None, msgs=msgs, context=None, tokenizer=tokenizer, **params)
st.session_state.response = st.write_stream(response)
st.session_state.chat_history.append({
"role": "model",
"content": st.session_state.response,
"image": None,
"video": None
})

st.divider() # Add separators to the interface
content_list.append(user_text)
msgs = [{"role": "user", "content": content_list}]

print("content_list:", content_list) # debug
print("params:", params) # debug

# Generate and display the model's responses
with st.spinner('AI is thinking...'):
response = model.chat(image=None, msgs=msgs, context=None, tokenizer=tokenizer, **params)
st.session_state.response = st.write_stream(response)
st.session_state.chat_history.append({
"role": "model",
"content": st.session_state.response,
"image": None,
"video": None
})

st.divider() # Add separators to the interface

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