Added brain tumor API project

Dockerfile

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+# Use an official Python runtime as a parent image
+FROM python:3.10-slim
+
+# Set environment variables
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+
+# Set working directory
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    libgl1-mesa-glx \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy requirements and install
+COPY requirements.txt .
+RUN pip install --upgrade pip
+RUN pip install -r requirements.txt
+
+# Copy the rest of the app
+COPY . .
+
+# Set the command to run your FastAPI app with uvicorn
+CMD ["uvicorn", "newapi:app", "--host", "0.0.0.0", "--port", "8080"]

newapi.py

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+from fastapi import FastAPI, File, UploadFile
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+import torch
+from torchvision import transforms
+from PIL import Image
+import io
+from huggingface_hub import hf_hub_download
+
+from models.TumorModel import TumorClassification, GliomaStageModel
+from utils import get_precautions_from_gemini
+
+# Initialize FastAPI app
+app = FastAPI(title="Brain Tumor Detection API")
+
+# Enable CORS for all origins (adjust for production)
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Load Tumor Classification Model from Hugging Face
+btd_model_path = hf_hub_download(
+    repo_id="Codewithsalty/brain-tumor-models",
+    filename="BTD_model.pth"
+)
+tumor_model = TumorClassification()
+tumor_model.load_state_dict(torch.load(btd_model_path, map_location="cpu"))
+tumor_model.eval()
+
+# Load Glioma Stage Prediction Model from Hugging Face
+glioma_model_path = hf_hub_download(
+    repo_id="Codewithsalty/brain-tumor-models",
+    filename="glioma_stages.pth"
+)
+glioma_model = GliomaStageModel()
+glioma_model.load_state_dict(torch.load(glioma_model_path, map_location="cpu"))
+glioma_model.eval()
+
+# Image preprocessing pipeline
+transform = transforms.Compose([
+    transforms.Grayscale(),
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.5], std=[0.5]),
+])
+
+# Health check endpoint
+@app.get("/")
+async def root():
+    return {"message": "Brain Tumor Detection API is running."}
+
+# Tumor type labels
+labels = ['glioma', 'meningioma', 'notumor', 'pituitary']
+
+# Predict tumor type from uploaded image
+@app.post("/predict-image")
+async def predict_image(file: UploadFile = File(...)):
+    img_bytes = await file.read()
+    img = Image.open(io.BytesIO(img_bytes)).convert("L")  # Ensure grayscale
+    x = transform(img).unsqueeze(0)
+
+    with torch.no_grad():
+        out = tumor_model(x)
+        idx = torch.argmax(out, dim=1).item()
+        tumor_type = labels[idx]
+
+    if tumor_type == "glioma":
+        return {"tumor_type": tumor_type, "next": "submit_mutation_data"}
+    else:
+        precautions = get_precautions_from_gemini(tumor_type)
+        return {"tumor_type": tumor_type, "precaution": precautions}
+
+# Input model for glioma mutation data
+class MutationInput(BaseModel):
+    gender: str
+    age: float
+    idh1: int
+    tp53: int
+    atrx: int
+    pten: int
+    egfr: int
+    cic: int
+    pik3ca: int
+
+# Predict glioma stage based on mutations
+@app.post("/predict-glioma-stage")
+async def predict_glioma_stage(data: MutationInput):
+    gender_val = 0 if data.gender.lower() == 'm' else 1
+    features = [
+        gender_val, data.age, data.idh1, data.tp53, data.atrx,
+        data.pten, data.egfr, data.cic, data.pik3ca
+    ]
+    x = torch.tensor(features).float().unsqueeze(0)
+
+    with torch.no_grad():
+        out = glioma_model(x)
+        idx = torch.argmax(out, dim=1).item()
+        stages = ['Stage 1', 'Stage 2', 'Stage 3', 'Stage 4']
+        return {"glioma_stage": stages[idx]}
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run("newapi:app", host="0.0.0.0", port=10000)

utils.py

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+def get_user_test_data():
+    print("Enter the following details for prediction:")
+
+    gender_input = input("Gender (M/F): ").strip().lower()
+    gender = 0 if gender_input == 'm' else 1  # Male = 0, Female = 1
+
+    age = float(input("Age at Diagnosis: "))
+
+    idh1 = int(input("IDH1 Mutation? (1 for Yes, 0 for No): "))
+    tp53 = int(input("TP53 Mutation? (1 for Yes, 0 for No): "))
+    atrx = int(input("ATRX Mutation? (1 for Yes, 0 for No): "))
+    pten = int(input("PTEN Mutation? (1 for Yes, 0 for No): "))
+    egfr = int(input("EGFR Mutation? (1 for Yes, 0 for No): "))
+    cic = int(input("CIC Mutation? (1 for Yes, 0 for No): "))
+    pik3ca = int(input("PIK3CA Mutation? (1 for Yes, 0 for No): "))
+
+    return [gender, age, idh1, tp53, atrx, pten, egfr, cic, pik3ca]
+
+def get_precautions_from_gemini(tumor_type):
+    precaution_db = {
+        "meningioma": "Avoid radiation exposure and get regular check-ups.",
+        "pituitary": "Monitor hormonal levels and follow medication strictly.",
+        "notumor": "Stay healthy and get annual MRI scans if symptoms appear."
+    }
+    return precaution_db.get(tumor_type, "No specific precautions found.")