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https://github.com/OpenBMB/MiniCPM-V.git
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Update web_demo_streamlit-minicpmv2_6.py
1. Avoid using 'None' string when `user_text` is empty. 2. Added `st.spinner` to display a loading message during AI content generation.
This commit is contained in:
JamePeng
GitHub
@@ -173,91 +173,97 @@ def encode_video(video_path):
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return frames
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# User input box
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user_text = st.chat_input("Enter your question")
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if user_text:
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# Display user input and save it to session history
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with st.chat_message(U_NAME, avatar="user"):
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st.session_state.chat_history.append({
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"role": "user",
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"content": user_text,
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"image": None,
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"video": None
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})
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st.markdown(f"{U_NAME}: {user_text}")
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if user_text is not None:
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if user_text.strip() is "":
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st.warning('Input message could not be empty!', icon="⚠️")
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else:
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# Display user input and save it to session history
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with st.chat_message(U_NAME, avatar="user"):
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st.session_state.chat_history.append({
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"role": "user",
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"content": user_text,
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"image": None,
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"video": None
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})
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st.markdown(f"{U_NAME}: {user_text}")
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# Generate responses using the model
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model = st.session_state.model
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tokenizer = st.session_state.tokenizer
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content_list = [] # Store the content (text or image) that will be passed into the model
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imageFile = None
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# Generate responses using the model
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model = st.session_state.model
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tokenizer = st.session_state.tokenizer
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content_list = [] # Store the content (text or image) that will be passed into the model
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imageFile = None
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with st.chat_message(A_NAME, avatar="assistant"):
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# Handle different inputs depending on the mode selected by the user
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if selected_mode == "Single Image":
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# Single image mode: pass in the last uploaded image
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print("Single Images mode in use")
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if len(st.session_state.chat_history) > 1 and len(st.session_state.uploaded_image_list) >= 1:
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uploaded_image = st.session_state.uploaded_image_list[-1]
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if uploaded_image:
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imageFile = Image.open(uploaded_image).convert('RGB')
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content_list.append(imageFile)
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with st.chat_message(A_NAME, avatar="assistant"):
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# Handle different inputs depending on the mode selected by the user
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if selected_mode == "Single Image":
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# Single image mode: pass in the last uploaded image
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print("Single Images mode in use")
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if len(st.session_state.chat_history) > 1 and len(st.session_state.uploaded_image_list) >= 1:
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uploaded_image = st.session_state.uploaded_image_list[-1]
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if uploaded_image:
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imageFile = Image.open(uploaded_image).convert('RGB')
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content_list.append(imageFile)
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else:
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print("Single Images mode: No image found")
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elif selected_mode == "Multiple Images":
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# Multi-image mode: pass in all the images uploaded last time
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print("Multiple Images mode in use")
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if len(st.session_state.chat_history) > 1 and st.session_state.uploaded_image_num >= 1:
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for uploaded_image in st.session_state.uploaded_image_list:
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imageFile = Image.open(uploaded_image).convert('RGB')
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content_list.append(imageFile)
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else:
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print("Multiple Images mode: No image found")
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elif selected_mode == "Video":
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# Video mode: pass in slice frames of uploaded video
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print("Video mode in use")
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if len(st.session_state.chat_history) > 1 and st.session_state.uploaded_video_num == 1:
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uploaded_video_path = st.session_state.uploaded_video_list[-1]
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if uploaded_video_path:
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frames = encode_video(uploaded_video_path)
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else:
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print("Video Mode: No video found")
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# Defining model parameters
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params = {
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'sampling': True,
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'top_p': top_p,
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'top_k': top_k,
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'temperature': temperature,
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'repetition_penalty': repetition_penalty,
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"max_new_tokens": max_length,
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"stream": True
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}
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# Set different input parameters depending on whether to upload a video
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if st.session_state.uploaded_video_num == 1 and selected_mode == "Video":
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msgs = [{"role": "user", "content": frames + [user_text]}]
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# Set decode params for video
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params["max_inp_length"] = 4352 # Set the maximum input length of the video mode
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params["use_image_id"] = False # Do not use image_id
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params["max_slice_nums"] = 1 # # use 1 if cuda OOM and video resolution > 448*448
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else:
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print("Single Images mode: No image found")
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content_list.append(user_text)
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msgs = [{"role": "user", "content": content_list}]
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elif selected_mode == "Multiple Images":
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# Multi-image mode: pass in all the images uploaded last time
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print("Multiple Images mode in use")
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if len(st.session_state.chat_history) > 1 and st.session_state.uploaded_image_num >= 1:
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for uploaded_image in st.session_state.uploaded_image_list:
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imageFile = Image.open(uploaded_image).convert('RGB')
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content_list.append(imageFile)
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else:
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print("Multiple Images mode: No image found")
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print("content_list:", content_list) # debug
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print("params:", params) # debug
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elif selected_mode == "Video":
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# Video mode: pass in slice frames of uploaded video
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print("Video mode in use")
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if len(st.session_state.chat_history) > 1 and st.session_state.uploaded_video_num == 1:
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uploaded_video_path = st.session_state.uploaded_video_list[-1]
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if uploaded_video_path:
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frames = encode_video(uploaded_video_path)
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else:
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print("Video Mode: No video found")
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# Generate and display the model's responses
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with st.spinner('AI is thinking...'):
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response = model.chat(image=None, msgs=msgs, context=None, tokenizer=tokenizer, **params)
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st.session_state.response = st.write_stream(response)
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st.session_state.chat_history.append({
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"role": "model",
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"content": st.session_state.response,
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"image": None,
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"video": None
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})
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# Defining model parameters
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params = {
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'sampling': True,
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'top_p': top_p,
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'top_k': top_k,
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'temperature': temperature,
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'repetition_penalty': repetition_penalty,
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"max_new_tokens": max_length,
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"stream": True
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}
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st.divider() # Add separators to the interface
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# Set different input parameters depending on whether to upload a video
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if st.session_state.uploaded_video_num == 1 and selected_mode == "Video":
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msgs = [{"role": "user", "content": frames + [user_text]}]
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# Set decode params for video
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params["max_inp_length"] = 4352 # Set the maximum input length of the video mode
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params["use_image_id"] = False # Do not use image_id
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params["max_slice_nums"] = 1 # # use 1 if cuda OOM and video resolution > 448*448
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else:
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content_list.append(user_text)
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msgs = [{"role": "user", "content": content_list}]
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print("content_list:", content_list) # debug
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print("params:", params) # debug
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# Generate and display the model's responses
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response = model.chat(image=None, msgs=msgs, context=None, tokenizer=tokenizer, **params)
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st.session_state.response = st.write_stream(response)
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st.session_state.chat_history.append({
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"role": "model",
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"content": st.session_state.response,
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"image": None,
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"video": None
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})
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st.divider() # Add separators to the interface
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