main updated

main.py

@@ -1,145 +1,154 @@
-import argparse
-import os
-import random
-import io
-from PIL import Image
-import numpy as np
-import torch
-import torch.backends.cudnn as cudnn
-
-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_eval.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
-
-
-chat_state = CONV_VISION.copy()
-img_list = []
-chatbot = []
-
-
-@app.get("/")
-async def root():
-    return RedirectResponse(url="/docs")
-
-
-@app.post("/upload_img/")
-async def upload_img(
-        file: UploadFile = File(...),
-):
-    pil_image = Image.open(io.BytesIO(await file.read()))
-    chat.upload_img(pil_image, chat_state, img_list)
-    return {"message": "image uploaded  successfully."}
-
-
-@app.post("/process/")
-async def process_item(prompt: str = Form(...)):
-    if not img_list:  # Check if img_list is empty or None
-        return {"error": "No images uploaded."}
-
-    global chatbot
-    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=1, max_length=2000)[0]
-    chatbot[-1][1] = llm_message
-    return chatbot
-
-
-@app.post("/reset/")
-async def reset(
-
-):
-    global chat_state, img_list, chatbot  # Use global keyword to reassign
-    img_list = []
-    if chat_state is not None:
-        chat_state.messages = []
-    if img_list is not None:
-        img_list = []
-    if chatbot is not None:
-        chatbot = []
-
-
-if __name__ == "__main__":
-    # Run the FastAPI app with Uvicorn
-    uvicorn.run("main:app", host="0.0.0.0", port=7860)
+import argparse
+import os
+import random
+import io
+from PIL import Image
+import numpy as np
+import torch
+import torch.backends.cudnn as cudnn
+from typing import List
+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_eval.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
+
+
+chat_state = CONV_VISION.copy()
+img_list = []
+chatbot = []
+
+
+@app.get("/")
+async def root():
+    return RedirectResponse(url="/docs")
+
+
+@app.post("/upload_img/")
+async def upload_img(
+        file: UploadFile = File(...),
+):
+    pil_image = Image.open(io.BytesIO(await file.read()))
+    chat.upload_img(pil_image, chat_state, img_list)
+    return {"message": "image uploaded  successfully."}
+
+
+
+@app.post("/process/")
+async def process_item(prompts: List[str] = Form(...)):
+    if not img_list:  # Check if img_list is empty or None
+        raise HTTPException(status_code=400, detail="No images uploaded.")
+
+    global chatbot
+    responses = []
+
+    for prompt in prompts:
+        # Process each prompt individually
+        chat.ask(prompt, chat_state)
+        chatbot.append([prompt, None])
+        llm_message = chat.answer(conv=chat_state, img_list=img_list, max_new_tokens=300, num_beams=1, temperature=1, max_length=2000)[0]
+        chatbot[-1][1] = llm_message
+        responses.append({
+            "prompt": prompt,
+            "response": llm_message
+        })
+
+    return responses
+
+
+@app.post("/reset/")
+async def reset(
+
+):
+    global chat_state, img_list, chatbot  # Use global keyword to reassign
+    img_list = []
+    if chat_state is not None:
+        chat_state.messages = []
+    if img_list is not None:
+        img_list = []
+    if chatbot is not None:
+        chatbot = []
+
+
+if __name__ == "__main__":
+    # Run the FastAPI app with Uvicorn
+    uvicorn.run("main:app", host="0.0.0.0", port=7860)