Create main.py

main.py

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+import argparse
+import os
+import random
+import io
+from PIL import Image
+import numpy as np
+import torch
+import torch.backends.cudnn as cudnn
+import gradio as gr
+
+from minigpt4.common.config import Config
+from minigpt4.common.dist_utils import get_rank
+from minigpt4.common.registry import registry
+from minigpt4.conversation.conversation import Chat, CONV_VISION
+from fastapi import FastAPI, HTTPException, File, UploadFile,Form
+from fastapi.responses import RedirectResponse
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from PIL import Image
+import io
+import uvicorn
+# imports modules for registration
+from minigpt4.datasets.builders import *
+from minigpt4.models import *
+from minigpt4.processors import *
+from minigpt4.runners import *
+from minigpt4.tasks import *
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Demo")
+    parser.add_argument("--cfg-path", type=str, default='eval_configs/minigpt4.yaml',
+                        help="path to configuration file.")
+    parser.add_argument(
+        "--options",
+        nargs="+",
+        help="override some settings in the used config, the key-value pair "
+             "in xxx=yyy format will be merged into config file (deprecate), "
+             "change to --cfg-options instead.",
+    )
+    args = parser.parse_args()
+    return args
+
+
+def setup_seeds(config):
+    seed = config.run_cfg.seed + get_rank()
+
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+
+    cudnn.benchmark = False
+    cudnn.deterministic = True
+
+
+# ========================================
+#             Model Initialization
+# ========================================
+
+SHARED_UI_WARNING = f'''### [NOTE] It is possible that you are waiting in a lengthy queue.
+
+You can duplicate and use it with a paid private GPU.
+
+<a class="duplicate-button" style="display:inline-block" target="_blank" href="https://huggingface.co/spaces/Vision-CAIR/minigpt4?duplicate=true"><img style="margin-top:0;margin-bottom:0" src="https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-xl-dark.svg" alt="Duplicate Space"></a>
+
+Alternatively, you can also use the demo on our [project page](https://minigpt-4.github.io).
+'''
+
+print('Initializing Chat')
+cfg = Config(parse_args())
+
+model_config = cfg.model_cfg
+model_cls = registry.get_model_class(model_config.arch)
+model = model_cls.from_config(model_config).to('cuda:0')
+
+vis_processor_cfg = cfg.datasets_cfg.cc_align.vis_processor.train
+vis_processor = registry.get_processor_class(vis_processor_cfg.name).from_config(vis_processor_cfg)
+chat = Chat(model, vis_processor)
+print('Initialization Finished')
+
+# ========================================
+#             Gradio Setting
+# ========================================
+
+app = FastAPI()
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # Replace "*" with your frontend domain
+    allow_credentials=True,
+    allow_methods=["GET", "POST"],
+    allow_headers=["*"],
+)
+
+
+class Item(BaseModel):
+    gr_img: UploadFile = File(..., description="Image file")
+    text_input: str = None
+
+@app.get("/")
+async def root():
+    return RedirectResponse(url="/docs")
+
+@app.post("/process/")
+async def process_item(
+        file: UploadFile = File(...),
+        prompt: str = Form(...),
+):
+    chat_state = CONV_VISION.copy()
+    img_list = []
+    chatbot=[]
+    pil_image = Image.open(io.BytesIO(await file.read()))
+    chat.upload_img(pil_image, chat_state, img_list)
+    chat.ask(prompt, chat_state)
+    chatbot = chatbot + [[prompt, None]]
+    llm_message = chat.answer(conv=chat_state, img_list=img_list, max_new_tokens=300, num_beams=1, temperature=temperature,
+                max_length=2000)[0]
+    chatbot[-1][1] = llm_message
+    return chatbot, chat_state, img_list
+
+
+# if __name__ == "__main__":
+#     # Run the FastAPI app with Uvicorn
+#     uvicorn.run("app:app", host="0.0.0.0", port=7860, reload=True)
+
+
+
+
+# def gradio_reset(chat_state, img_list):
+#     if chat_state is not None:
+#         chat_state.messages = []
+#     if img_list is not None:
+#         img_list = []
+#     return None, gr.update(value=None, interactive=True), gr.update(placeholder='Please upload your image first',
+#                                                                     interactive=False), gr.update(
+#         value="Upload & Start Chat", interactive=True), chat_state, img_list
+#
+#
+# def upload_img(gr_img, text_input, chat_state):
+#     if gr_img is None:
+#         return None, None, gr.update(interactive=True), chat_state, None
+#     chat_state = CONV_VISION.copy()
+#     img_list = []
+#     llm_message = chat.upload_img(gr_img, chat_state, img_list)
+#     return gr.update(interactive=False), gr.update(interactive=True, placeholder='Type and press Enter'), gr.update(
+#         value="Start Chatting", interactive=False), chat_state, img_list
+#
+#
+# def gradio_ask(user_message, chatbot, chat_state):
+#     if len(user_message) == 0:
+#         return gr.update(interactive=True, placeholder='Input should not be empty!'), chatbot, chat_state
+#     chat.ask(user_message, chat_state)
+#     chatbot = chatbot + [[user_message, None]]
+#     return '', chatbot, chat_state
+#
+#
+# def gradio_answer(chatbot, chat_state, img_list, num_beams, temperature):
+#     llm_message = \
+#     chat.answer(conv=chat_state, img_list=img_list, max_new_tokens=300, num_beams=1, temperature=temperature,
+#                 max_length=2000)[0]
+#     chatbot[-1][1] = llm_message
+#     return chatbot, chat_state, img_list
+#
+#
+# title = """<h1 align="center">Demo of MiniGPT-4</h1>"""
+# description = """<h3>This is the demo of MiniGPT-4. Upload your images and start chatting!</h3>"""
+# article = """<div style='display:flex; gap: 0.25rem; '><a href='https://minigpt-4.github.io'><img src='https://img.shields.io/badge/Project-Page-Green'></a><a href='https://github.com/Vision-CAIR/MiniGPT-4'><img src='https://img.shields.io/badge/Github-Code-blue'></a><a href='https://github.com/TsuTikgiau/blip2-llm/blob/release_prepare/MiniGPT_4.pdf'><img src='https://img.shields.io/badge/Paper-PDF-red'></a></div>
+# """
+#
+# # TODO show examples below
+#
+# with gr.Blocks() as demo:
+#     gr.Markdown(title)
+#     gr.Markdown(SHARED_UI_WARNING)
+#     gr.Markdown(description)
+#     gr.Markdown(article)
+#
+#     with gr.Row():
+#         with gr.Column(scale=0.5):
+#             image = gr.Image(type="pil")
+#             upload_button = gr.Button(value="Upload & Start Chat", interactive=True, variant="primary")
+#             clear = gr.Button("Restart")
+#
+#             num_beams = gr.Slider(
+#                 minimum=1,
+#                 maximum=5,
+#                 value=1,
+#                 step=1,
+#                 interactive=True,
+#                 label="beam search numbers)",
+#             )
+#
+#             temperature = gr.Slider(
+#                 minimum=0.1,
+#                 maximum=2.0,
+#                 value=1.0,
+#                 step=0.1,
+#                 interactive=True,
+#                 label="Temperature",
+#             )
+#
+#         with gr.Column():
+#             chat_state = gr.State()
+#             img_list = gr.State()
+#             chatbot = gr.Chatbot(label='MiniGPT-4')
+#             text_input = gr.Textbox(label='User', placeholder='Please upload your image first', interactive=False)
+#
+#     upload_button.click(upload_img, [image, text_input, chat_state],
+#                         [image, text_input, upload_button, chat_state, img_list])
+#
+#     text_input.submit(gradio_ask, [text_input, chatbot, chat_state], [text_input, chatbot, chat_state]).then(
+#         gradio_answer, [chatbot, chat_state, img_list, num_beams, temperature], [chatbot, chat_state, img_list]
+#     )
+#     clear.click(gradio_reset, [chat_state, img_list], [chatbot, image, text_input, upload_button, chat_state, img_list],
+#                 queue=False)
+#
+# demo.launch(enable_queue=True)