Merge pull request #76 from SillyLossy/main

Use BLIP to send a picture to model

modules/bot_picture.py

@@ -0,0 +1,14 @@
+import requests
+import torch
+from PIL import Image
+from transformers import BlipForConditionalGeneration
+from transformers import BlipProcessor
+
+processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
+model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base", torch_dtype=torch.float16).to("cuda")
+
+# raw_image = Image.open('/tmp/istockphoto-470604022-612x612.jpg').convert('RGB')
+def caption_image(raw_image):
+    inputs = processor(raw_image, return_tensors="pt").to("cuda", torch.float16)
+    out = model.generate(**inputs, max_new_tokens=100)
+    return processor.decode(out[0], skip_special_tokens=True)

modules/html_generator.py

@@ -217,6 +217,12 @@ def generate_chat_html(history, name1, name2, character):
 
     .body {
     }
+
+    .body img {
+      max-width: 300px;
+      max-height: 300px;
+      border-radius: 20px;
+    }
     """
 
     output = ''

server.py

@@ -23,6 +23,7 @@ from tqdm import tqdm
 from transformers import AutoConfig
 from transformers import AutoModelForCausalLM
 from transformers import AutoTokenizer
+from io import BytesIO
 
 from modules.html_generator import *
 from modules.stopping_criteria import _SentinelTokenStoppingCriteria
@@ -53,6 +54,7 @@ parser.add_argument('--listen', action='store_true', help='Make the web UI reach
 parser.add_argument('--listen-port', type=int, help='The listening port that the server will use.')
 parser.add_argument('--share', action='store_true', help='Create a public URL. This is useful for running the web UI on Google Colab or similar.')
 parser.add_argument('--verbose', action='store_true', help='Print the prompts to the terminal.')
+parser.add_argument('--picture', action='store_true', help='Adds an ability to send pictures in chat UI modes.')
 args = parser.parse_args()
 
 if (args.chat or args.cai_chat) and not args.no_stream:
@@ -97,6 +99,9 @@ if args.deepspeed:
     ds_config = generate_ds_config(args.bf16, 1 * world_size, args.nvme_offload_dir)
     dschf = HfDeepSpeedConfig(ds_config) # Keep this object alive for the Transformers integration
 
+if args.picture and (args.cai_chat or args.chat):
+    import modules.bot_picture as bot_picture
+
 def load_model(model_name):
     print(f"Loading {model_name}...")
     t0 = time.time()
@@ -561,8 +566,12 @@ def extract_message_from_reply(question, reply, current, other, check, extension
 
     return reply, next_character_found, substring_found
 
-def chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, history_size):
+def chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, history_size, picture=None):
-    original_text = text
+    if args.picture and picture is not None:
+        text, visible_text = generate_chat_picture(picture, name1, name2)
+    else:
+        visible_text = text
+
     text = apply_extensions(text, "input")
     question = generate_chat_prompt(text, tokens, name1, name2, context, history_size)
     history['internal'].append(['', ''])
@@ -571,14 +580,14 @@ def chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p,
     for reply in generate_reply(question, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, eos_token=eos_token, stopping_string=f"\n{name1}:"):
         reply, next_character_found, substring_found = extract_message_from_reply(question, reply, name2, name1, check, extensions=True)
         history['internal'][-1] = [text, reply]
-        history['visible'][-1] = [original_text, apply_extensions(reply, "output")]
+        history['visible'][-1] = [visible_text, apply_extensions(reply, "output")]
         if not substring_found:
             yield history['visible']
         if next_character_found:
             break
     yield history['visible']
 
-def impersonate_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, history_size):
+def impersonate_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, history_size, picture=None):
     question = generate_chat_prompt(text, tokens, name1, name2, context, history_size, impersonate=True)
     eos_token = '\n' if check else None
     for reply in generate_reply(question, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, eos_token=eos_token, stopping_string=f"\n{name2}:"):
@@ -589,19 +598,19 @@ def impersonate_wrapper(text, tokens, do_sample, max_new_tokens, temperature, to
             break
     yield apply_extensions(reply, "output")
 
-def cai_chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, history_size):
+def cai_chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, history_size, picture=None):
-    for _history in chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, history_size):
+    for _history in chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, history_size, picture):
         yield generate_chat_html(_history, name1, name2, character)
 
-def regenerate_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, history_size):
+def regenerate_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, history_size, picture=None):
     last = history['visible'].pop()
     history['internal'].pop()
     text = last[0]
     if args.cai_chat:
-        for i in cai_chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, history_size):
+        for i in cai_chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, history_size, picture):
             yield i
     else:
-        for i in chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, history_size):
+        for i in chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, history_size, picture):
             yield i
 
 def remove_last_message(name1, name2):
@@ -791,6 +800,14 @@ def upload_your_profile_picture(img):
     img.save(Path(f'img_me.png'))
     print(f'Profile picture saved to "img_me.png"')
 
+def generate_chat_picture(picture, name1, name2):
+    text = f'*{name1} sends {name2} a picture that contains the following: "{bot_picture.caption_image(picture)}"*'
+    buffer = BytesIO()
+    picture.save(buffer, format="JPEG")
+    img_str = base64.b64encode(buffer.getvalue()).decode('utf-8')
+    visible_text = f'<img src="data:image/jpeg;base64,{img_str}">'
+    return text, visible_text
+
 # Global variables
 available_models = get_available_models()
 available_presets = get_available_presets()
@@ -861,6 +878,9 @@ if args.chat or args.cai_chat:
         with gr.Row():
             buttons["Send last reply to input"] = gr.Button("Send last reply to input")
             buttons["Replace last reply"] = gr.Button("Replace last reply")
+        if args.picture:
+            with gr.Row():
+                picture_select = gr.Image(label="Send a picture", type='pil')
 
         with gr.Row():
             with gr.Column():
@@ -907,12 +927,18 @@ if args.chat or args.cai_chat:
             create_extensions_block()
 
         input_params = [textbox, max_new_tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, history_size_slider]
+        if args.picture:
+            input_params.append(picture_select)
         if args.cai_chat:
             gen_events.append(buttons["Generate"].click(cai_chatbot_wrapper, input_params, display, show_progress=args.no_stream, api_name="textgen"))
             gen_events.append(textbox.submit(cai_chatbot_wrapper, input_params, display, show_progress=args.no_stream))
+            if args.picture:
+                picture_select.upload(cai_chatbot_wrapper, input_params, display, show_progress=args.no_stream)
         else:
             gen_events.append(buttons["Generate"].click(chatbot_wrapper, input_params, display, show_progress=args.no_stream, api_name="textgen"))
             gen_events.append(textbox.submit(chatbot_wrapper, input_params, display, show_progress=args.no_stream))
+            if args.picture:
+                picture_select.upload(chatbot_wrapper, input_params, display, show_progress=args.no_stream)
         gen_events.append(buttons["Regenerate"].click(regenerate_wrapper, input_params, display, show_progress=args.no_stream))
         gen_events.append(buttons["Impersonate"].click(impersonate_wrapper, input_params, textbox, show_progress=args.no_stream))
 
@@ -925,11 +951,14 @@ if args.chat or args.cai_chat:
         buttons["Upload character"].click(upload_character, [upload_char, upload_img], [character_menu])
         for i in ["Generate", "Regenerate", "Replace last reply"]:
             buttons[i].click(lambda x: "", textbox, textbox, show_progress=False)
+
         textbox.submit(lambda x: "", textbox, textbox, show_progress=False)
         character_menu.change(load_character, [character_menu, name1, name2], [name2, context, display])
         upload_img_tavern.upload(upload_tavern_character, [upload_img_tavern, name1, name2], [character_menu])
         upload.upload(load_history, [upload, name1, name2], [])
         upload_img_me.upload(upload_your_profile_picture, [upload_img_me], [])
+        if args.picture:
+            picture_select.upload(lambda : None, [], [picture_select], show_progress=False)
 
         if args.cai_chat:
             upload.upload(redraw_html, [name1, name2], [display])