feat: First commit

.gitattributes

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+gloco-backend 
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+gloco-backend
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+initial commit
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.gitignore

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+/env
+__pycache__/
+.env

Dockerfile

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+FROM python:3.10.13-slim-bookworm
+
+# Define the working directory
+WORKDIR /code
+
+# Copy the requirements file
+COPY ./requirements.txt /code/requirements.txt
+
+# Install the requirements
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# Copy the rest of the files
+COPY . /code
+
+# Run the application
+ENTRYPOINT ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

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+---
+title: Gloco Backend
+emoji: 🏢
+colorFrom: blue
+colorTo: red
+sdk: docker
+pinned: false
+---

apps/create_app.py

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+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+
+
+def create_app():
+    app = FastAPI(
+        title="Trash Detection API",
+        description="GLOCO Object Detection API using FastAPI and YOLOv8",
+        docs_url="/"
+    )
+
+    # Add CORS middleware
+    app.add_middleware(
+        CORSMiddleware,
+        allow_origins=["*"],
+        allow_credentials=True,
+        allow_methods=["*"],
+        allow_headers=["*"],
+    )
+
+    return app

apps/detection/__init__.py

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+from fastapi import APIRouter, UploadFile, File, HTTPException, status
+from fastapi.responses import JSONResponse
+from .model import Detections
+from .controller import detect_object
+
+
+router = APIRouter(
+    prefix="/detection",
+    tags=["detection"],
+    default_response_class=JSONResponse
+)
+
+
+@router.post("/", response_model=Detections)
+async def detection(
+    image: UploadFile = File(..., description="File to detect"),
+):
+    if not image.content_type.startswith("image"):
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="Invalid file type. Only image files are allowed."
+        )
+
+    # Read image file
+    contents = await image.read()
+
+    detections = detect_object(contents)
+    return detections

apps/detection/controller.py

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+from .provider import model
+from io import BytesIO
+from PIL import Image
+
+
+def detect_object(image: bytes):
+    # Convert bytes to PIL image
+    image = Image.open(BytesIO(image))
+
+    detections = model.predict(image)
+
+    # Format detections
+    bboxs = detections[0].boxes.data
+    labels = detections[0].names
+
+    # Get boxs result
+    boxs_result = []
+    for box in bboxs:
+        result_conf = box[4].item()
+        if result_conf > model.conf:
+            boxs_result.append({
+                "class_name": labels[int(box[5].item())],
+                "confidence": result_conf,
+                "xmin": box[0].item(),
+                "ymin": box[1].item(),
+                "xmax": box[2].item(),
+                "ymax": box[3].item()
+            })
+
+    print(boxs_result)
+
+    return {
+        "detections": boxs_result
+    }

apps/detection/model.py

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+from pydantic import BaseModel, Field
+
+
+class Detection(BaseModel):
+    class_name: str = Field(..., example="person")
+    confidence: float = Field(..., example=0.99)
+    xmin: float = Field(..., example=0.1)
+    ymin: float = Field(..., example=0.2)
+    xmax: float = Field(..., example=0.3)
+    ymax: float = Field(..., example=0.4)
+
+
+class Detections(BaseModel):
+    detections: list[Detection] = Field(..., description="List of detections")

apps/detection/provider.py

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+import os
+from ultralytics import YOLO
+
+
+model = YOLO(os.path.join(os.getcwd(), "models", "yolo.pt"))
+model.conf = 0.5

apps/query/__init__.py

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+from fastapi import APIRouter
+from fastapi.responses import JSONResponse
+
+
+router = APIRouter(
+    prefix="/query",
+    tags=["query"],
+    default_response_class=JSONResponse
+)
+
+
+@router.post("/")
+def query():
+    return {"message": "Query"}

apps/recommend/__init__.py

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+from fastapi import APIRouter
+from fastapi.responses import JSONResponse
+from .model import Recommendations, RecommendType
+from .controller import recycle_recommendation
+
+
+router = APIRouter(
+    prefix="/recommend",
+    tags=["recommend"],
+    default_response_class=JSONResponse,
+)
+
+
+@router.post("/", response_model=Recommendations)
+async def recommend(data: RecommendType):
+    return recycle_recommendation(data.recommend_type)

apps/recommend/controller.py

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+import json
+from fastapi import HTTPException, status
+from .provider import model
+
+
+prompt_template = '''Think and generate content about how to recycle {type}
+The recycle recommendation should be easy to understand and follow.
+- For example, for the trash type "plastic bottle", provide how to use the plastic for household items.
+- For the trash type "paper", provide how to do origami, etc.
+
+You must answer clearly, easy to understand and step by step.
+You must prompt professional and reliable information. No yapping.
+
+You must provide an answer as a following JSON format:
+{{
+    "recommendations": [
+        {{
+            "title": "Foo Bar",
+            "steps": [
+                "Foo",
+                "Bar"
+            ]
+        }}
+    ]
+}}
+'''
+
+
+def recycle_recommendation(trash_type: str):
+    prompt = prompt_template.format(type=trash_type)
+
+    answer = model.generate_content(prompt)
+
+    try:
+        parsed_data = json.loads(answer.text)
+    except json.JSONDecodeError:
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail="Failed to parse the answer from the model"
+        )
+
+    return parsed_data

apps/recommend/model.py

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+from pydantic import BaseModel, Field
+
+
+class Recommendation(BaseModel):
+    title: str = Field(..., title="Title of the recommendation")
+    steps: list[str] = Field(...,
+                             title="Steps to follow to implement the recommendation")
+
+
+class Recommendations(BaseModel):
+    recommendations: list[Recommendation] = Field(
+        ..., title="List of recommendations")
+
+
+class RecommendType(BaseModel):
+    recommend_type: str = Field(..., title="Type of recommendation to return")

apps/recommend/provider.py

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+import os
+import google.generativeai as genai
+
+
+genai.configure(api_key=os.environ.get("GOOGLE_API_KEY"))
+
+model = genai.GenerativeModel("gemini-pro")

main.py

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+from apps.create_app import create_app
+from apps.detection import router as detection_router
+from apps.recommend import router as recommend_router
+
+
+app = create_app()
+
+
+# Register router
+app.include_router(detection_router, prefix="/api")
+app.include_router(recommend_router, prefix="/api")

models/yolo.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b4292d72e8b4975e9561760e274839c18c066caa43b65c8dcea0fb37287d64eb
+size 22557081