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demo/README.md

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+
+##  0 . Fine tuning restrictions
+Fine tuning supports training for image understanding but not image generation
+
+##  1. install ms-swift
+use ms-swift Fine tune the Janus-Pro-7B model,
+First, install ms-swift
+----------------------------------------------
+pip install git+https://github.com/modelscope/ms-swift.git
+cd ms-swift
+pip install -e .
+--------------------------------------------------------
+##  2. Datasets
+The dataset format is
+{"messages": [{"role": "user", "content": "<image>Does the construction worker in this picture comply with the safety regulations for high-altitude operations？"}, {"role": "assistant", "content": "In the high-altitude work area, people entering the construction site must wear safety helmets, and high-altitude workers should wear safety belts. The other end of the safety belt must be hung higher than the human body, which is called high hanging and low use. The high-altitude workers in the picture did not wear safety belts, which does not meet the safety standards for high-altitude operations."}], "images": ["root/train/train_images/wpd-36.jpg"]}
+
+##  3. Fine tuning
+lora Fine tuning
+swift sft --model_type deepseek_janus_pro --model  <Janus-Pro-7B model path> --dataset <dataset path> --target_modules all-linear
+
+full Fine tuning
+swift sft --model_type deepseek_janus_pro --model  <Janus-Pro-7B model path> --dataset <dataset path> --train_type  full
+
+
+
+##  4. swift model export 
+Export can merge two previously dispersed models into one model system
+swift export  --ckpt_dir <swift model path>
+
+##  5. swift model Service
+swift deploy --ckpt_dir <Export or Swift model path>
+
+
+##  6. swift model Proxy Service
+Create an empty uploads directory
+fastapi_swift.py
+
+##  7. Client API fastapi_client. py
+Submit questions and receive responses to the swift model Proxy Service 
+using fastapi_client. py
+
+
+##  Other1. 
+fastap_client.py parameters(seed、top_p、temperature ) are no longer useful, 
+but in order to maintain interface reuse, 
+they are retained
+
+##  Other2.
+If no export is performed, then:
+If the Swift model needs to change the directory, the configuration file needs to be changed
+Adapterconfig.json Modify 'base_madel_name_or_path'
+Args.json modifies 'model'
+Specify the Janus-Pro-7B directory for classical gravity

demo/fastapi_swift.py

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+from fastapi import FastAPI, File, Form, UploadFile, HTTPException
+from fastapi.responses import JSONResponse, StreamingResponse
+from PIL import Image
+import numpy as np
+import io
+import hashlib
+import traceback
+import json
+import requests
+
+
+app = FastAPI()
+understand_image_and_question_url = "http://localhost:8000/v1/chat/completions"
+
+
+@app.post("/understand_image_and_question/")
+async def understand_image_and_question(
+    file: UploadFile = File(...),
+    question: str = Form(...),
+    seed: int = Form(42),
+    top_p: float = Form(0.95),
+    temperature: float = Form(0.1)
+):
+    # images file max size 8mb
+    maxfilesize = 8 * 1024 * 1024
+    image_data = await file.read(maxfilesize)
+    try:
+        # Upload file directory
+        imagedirectory = "./uploads/"
+        # Need to match version with Swift service
+        JanusVersion = "Janus-Pro-7B"
+        #JanusVersion = "Janus-Pro-1B"
+
+        file = Image.open(io.BytesIO(image_data))
+        hash_obj = hashlib.md5()
+        hash_obj.update(image_data)
+        file_hash = hash_obj.hexdigest()
+        filename =  imagedirectory + file_hash + ".png"
+        file.save(filename, format='PNG')
+        file.close()
+
+        outjson =   {"model": JanusVersion,
+            "messages": [{"role": "user", 
+                          "content": "<image>"  + question} ],
+                 "images": [filename]}
+        
+        outjson = json.dumps(outjson,ensure_ascii=False)
+        response = requests.post(understand_image_and_question_url, data=outjson, stream=False)
+        response_data = response.json()
+        return response_data
+
+    except  Exception as e:
+        print("-----------------------------------------------")
+        error_type = type(e).__name__
+        error_msg = str(e)
+        print(error_type)
+        print(error_msg)
+        traceback.print_exc()
+        print("-----------------------------------------------")
+        
+        return "images file bad"
+
+
+
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)