changes

Dockerfile

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+
+FROM python:3.9
+
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user . /app
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI, File, UploadFile, Form, HTTPException
+from typing import List
+from pydantic import BaseModel
+from PIL import Image
+import io
+from transformers import AutoModel, AutoTokenizer
+import torch
+
+app = FastAPI()
+
+# Load model and tokenizer
+model = AutoModel.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True,
+                                  attn_implementation='sdpa', torch_dtype=torch.bfloat16)
+model = model.eval().cuda()
+tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True)
+
+class FewshotExample(BaseModel):
+    image: bytes
+    question: str
+    answer: str
+
+class PredictRequest(BaseModel):
+    fewshot_examples: List[FewshotExample]
+    test_image: bytes
+    test_question: str
+
+@app.post("/predict_with_fewshot")
+async def predict_with_fewshot(
+    fewshot_images: List[UploadFile] = File(...),
+    fewshot_questions: List[str] = Form(...),
+    fewshot_answers: List[str] = Form(...),
+    test_image: UploadFile = File(...),
+    test_question: str = Form(...)
+):
+    # Validate input lengths
+    if len(fewshot_images)!= len(fewshot_questions) or len(fewshot_questions)!= len(fewshot_answers):
+        raise HTTPException(status_code=400, detail="Number of few-shot images, questions, and answers must match.")
+    
+    msgs = []
+    try:
+        for fs_img, fs_q, fs_a in zip(fewshot_images, fewshot_questions, fewshot_answers):
+            img_content = await fs_img.read()
+            img = Image.open(io.BytesIO(img_content)).convert('RGB')
+            msgs.append({'role': 'user', 'content': [img, fs_q]})
+            msgs.append({'role': 'assistant', 'content': [fs_a]})
+        
+        # Test example
+        test_img_content = await test_image.read()
+        test_img = Image.open(io.BytesIO(test_img_content)).convert('RGB')
+        msgs.append({'role': 'user', 'content': [test_img, test_question]})
+        
+        # Get answer
+        answer = model.chat(
+            image=None,
+            msgs=msgs,
+            tokenizer=tokenizer
+        )
+        
+        return {"answer": answer}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error processing request: {str(e)}")

requirements.txt

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+fastapi
+uvicorn[standard]
+Pillow==10.1.0
+torch==2.1.2
+torchvision==0.16.2
+transformers==4.40.0
+sentencepiece==0.1.99
+decord
+python-multipart