fix(detection): Fix missing import os, static folder path, and stream read error in Brain Tumor Detection Web App #1732

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Describe the bug

The Flask web application for Brain Tumor Detection in projects/detection/Brain Tumor Detection/Web App/app.py has several runtime and design issues that prevent it from running properly:

1. Missing import os (NameError):
   Line 10 calls:

   dir_path = os.path.dirname(os.path.realpath(__file__))

   However, import os is never imported anywhere in app.py, immediately crashing the app upon launch with NameError: name 'os' is not defined.

2. CSS 404 / Missing Static Folder Configuration:
   The templates (index.html and result.html) reference CSS via {{ url_for('static', filename='styles.css') }} and {{ url_for('static', filename='styles1.css') }}. However, the directory in Web App/ is named styles/ rather than static/, resulting in 404 HTTP errors for styling unless static_folder='styles' is configured in the Flask application.

3. Double Stream Read / RGBA Crash on Upload:
   In predict():

   img = Image.open(image.stream)
   ...
   img_base64 = image_to_base64(Image.open(image.stream))

   Reading image.stream twice without image.stream.seek(0) causes PIL.UnidentifiedImageError. Additionally, uploading RGBA/PNG files crashes img.save(buffered, format="JPEG") with OSError: cannot write mode RGBA as JPEG.

4. Missing Graceful Model Fallback:
   If best_model.h5 is not yet placed in the folder, the entire Flask server crashes immediately on startup rather than launching and providing informative status.

5. Non-Portable Requirements (tensorflow_intel):
   requirements.txt pins tensorflow_intel==2.12.0, which causes dependency resolution failures on non-Windows environments.

Expected behavior

1. app.py should import os and run without NameError.
2. Flask should configure static_folder='styles' so url_for('static', ...) serves styles.css and styles1.css properly.
3. Uploaded images should be opened and converted to RGB once, avoiding stream exhaustion and RGBA JPEG errors.
4. If best_model.h5 is absent, the app should start and inform the user cleanly on prediction instead of failing startup.
5. requirements.txt should specify portable dependencies.

Proposed Changes

1. Add import os to app.py.
2. Initialize Flask with app = Flask(__name__, static_folder='styles').
3. Read the uploaded image once, convert to RGB, and pass to both preprocessing and base64 encoding.
4. Add safe model loading with friendly fallback when best_model.h5 is missing.
5. Update requirements.txt with portable dependencies.

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