-- add the new file for fastAPI

Author: codewithdark-git

Dockerfile

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+FROM python:3.11-slim
+
+WORKDIR /app
+
+# Copy requirements first to leverage Docker cache
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the rest of the application
+COPY . .
+
+# Command to run the application
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"] 

LICENSE

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+MIT License
+
+Copyright (c) 2025 
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+# DocGPT
+ 

app/schemas/__init__.py

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+"""
+Schema package initialization.
+""" 

app/schemas/health.py

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+from pydantic import BaseModel
+from typing import Dict, Literal
+
+class HealthCheck(BaseModel):
+    status: Literal["healthy", "unhealthy"]
+    api_version: str
+    services: Dict[str, Literal["up", "down"]]

app/schemas/prediction.py

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+from pydantic import BaseModel, Field
+from typing import Literal, Optional
+from enum import Enum
+
+class DiseaseType(str, Enum):
+    PNEUMONIA = "Pneumonia"
+    MELANOMA = "Melanoma"
+    BRAIN = "Brain"
+    HEART = "Heart"
+
+class PredictionRequest(BaseModel):
+    disease_type: DiseaseType = Field(..., description="Type of disease to predict")
+    image_base64: str = Field(..., description="Base64 encoded image data")
+
+class PredictionResponse(BaseModel):
+    disease_type: DiseaseType
+    prediction: str
+    confidence: float = Field(..., ge=0.0, le=1.0)
+    analysis: str
+    status: Literal["success", "error"] = "success"
+    error_message: Optional[str] = None 

app/services/Brain.py

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+import torch
+from torchvision import transforms
+from PIL import Image
+import logging
+from pathlib import Path
+from typing import Optional, Tuple
+from app.config import get_settings
+
+settings = get_settings()
+
+class Eye:
+    MODEL_PATH = settings.EYE_MODEL_PATH
+    def __init__(self):
+        self.model = self._load_model()
+        self.transform = self._get_transforms()
+
+    def _get_transforms(self) -> torch.transforms.Compose:
+        # Define the transformations
+        return transforms.Compose([
+            transforms.Resize((224, 224)),
+            transforms.ToTensor(),
+            transforms.Normalize(
+                mean=[0.485, 0.456, 0.406],
+                std=[0.229, 0.224, 0.225]
+            )
+        ])
+    
+    def _load_model(self) -> torch.nn.Module:
+        """Load and initialize the PyTorch model."""
+        try:
+            # Ensure the model path exists
+            model_path = Path(self.MODEL_PATH)
+            if not model_path.exists():
+                raise FileNotFoundError(f"Model file not found at {model_path}")
+            model = torch.load(model_path, map_location=torch.device("cpu"))
+            # Set the model to evaluation mode
+            model.eval()
+            logging.info("Model loaded successfully")
+            return model
+        except Exception as e:
+            logging.error(f"Model loading error: {str(e)}")
+
+    def predict_with_torch(self, image_path: str) -> Tuple[str, float]:
+        """
+        Predict using PyTorch model.
+        Returns prediction and confidence score.
+        """
+        try:
+            image = Image.open(image_path).convert("RGB")
+            image_tensor = self.transform(image).unsqueeze(0)
+
+            with torch.no_grad():
+                outputs = self.model(image_tensor)
+                probabilities = torch.softmax(outputs, dim=1)
+                confidence, predicted = torch.max(probabilities, 1)
+
+            return self.CLASSES[predicted.item()], confidence.item()
+        except Exception as e:
+            logging.error(f"Prediction error: {str(e)}")
+            raise RuntimeError("Error during model prediction.")
+

app/services/Eye.py

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+import torch
+from torchvision import transforms
+from PIL import Image
+import logging
+from pathlib import Path
+from typing import Optional, Tuple
+from app.config import get_settings
+
+settings = get_settings()
+
+class Pneumonia:
+    CLASSES = ["Normal", "Pneumonia"]
+    MODEL_PATH = settings.PNEUMONIA_MODEL_PATH
+
+    def __init__(self):
+        self.model = self._load_model()
+        self.transform = self._get_transforms()
+
+    def _load_model(self) -> torch.nn.Module:
+        """Load and initialize the PyTorch model."""
+        try:
+            # Ensure the model path exists
+            model_path = Path(self.MODEL_PATH)
+            if not model_path.exists():
+                raise FileNotFoundError(f"Model file not found at {model_path}")
+
+            model = torch.load(model_path, map_location=torch.device("cpu"))
+            model.eval()
+            logging.info("Model loaded successfully")
+            return model
+
+        except Exception as e:
+            logging.error(f"Model loading error: {str(e)}")
+
+    @staticmethod
+    def _get_transforms():
+        """Define image transformations."""
+        return transforms.Compose([
+            transforms.Resize((224, 224)),
+            transforms.ToTensor(),
+            transforms.Normalize(
+                mean=[0.485, 0.456, 0.406],
+                std=[0.229, 0.224, 0.225]
+            )
+        ])
+
+    def predict_with_torch(self, image_path: str) -> Tuple[str, float]:
+        """
+        Predict using PyTorch model.
+        Returns prediction and confidence score.
+        """
+        try:
+            image = Image.open(image_path).convert("RGB")
+            image_tensor = self.transform(image).unsqueeze(0)
+
+            with torch.no_grad():
+                outputs = self.model(image_tensor)
+                probabilities = torch.softmax(outputs, dim=1)
+                confidence, predicted = torch.max(probabilities, 1)
+
+            return self.CLASSES[predicted.item()], confidence.item()
+        except Exception as e:
+            logging.error(f"Prediction error: {str(e)}")
+            raise RuntimeError("Error during model prediction.")