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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+brain_tumor_model.keras filter=lfs diff=lfs merge=lfs -text

app.py

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+import streamlit as st
+import shutil # <-- Nayi library for file copy (Fixes cross-device link error)
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+import numpy as np
+from PIL import Image
+from huggingface_hub import hf_hub_download
+import os
+
+# --- 1. Configuration (Constants) ---
+# Yahan aapka confirmed Repo ID use ho raha hai
+HF_REPO_ID = "saad1BM/brain-tumor-detection" 
+MODEL_FILENAME = "brain_tumor_model.keras" 
+MODEL_PATH = MODEL_FILENAME 
+
+IMAGE_SIZE = (224, 224)
+CLASS_NAMES = ['glioma_tumor', 'meningioma_tumor', 'no_tumor', 'pituitary_tumor']
+
+
+# --- 2. Model Loading (FINAL FIX: Cross-Device Link Resolved) ---
+@st.cache_resource
+def load_trained_model():
+    """Trained model ko Hugging Face se download aur load karta hai."""
+    
+    # Global variables ko function ke andar access karna
+    global HF_REPO_ID, MODEL_FILENAME, MODEL_PATH
+    
+    # Minimum expected model size (approx 100 MB, for size check)
+    MIN_SIZE = 100 * 1024 * 1024 
+
+    try:
+        # Check karein ki model pehle se download hai ya nahi ya file corrupt/incomplete hai
+        if not os.path.exists(MODEL_PATH) or os.path.getsize(MODEL_PATH) < MIN_SIZE: 
+            st.info(f"Downloading model from Hugging Face: {HF_REPO_ID}/{MODEL_FILENAME}")
+            
+            # hf_hub_download model ko /tmp mein download karega
+            downloaded_path = hf_hub_download(
+                repo_id=HF_REPO_ID, 
+                filename=MODEL_FILENAME, 
+                # Cache ko /tmp mein rakhein
+                cache_dir="/tmp/hf_cache", 
+            )
+            
+            st.info("Copying model file locally to avoid cross-device link error...")
+            
+            # os.rename ki jagah shutil.copyfile use karein (Yeh cross-device error ko theek karta hai)
+            shutil.copyfile(downloaded_path, MODEL_PATH) 
+
+            st.success("Model download and copy successful!")
+            
+        # Load the model
+        model = load_model(MODEL_PATH)
+        return model
+        
+    except Exception as e:
+        # Error message mein zaroori details daal di gayi hain
+        st.error(f"Model Load Failed. Ensure 'huggingface-hub' is in requirements.txt and file is public. Error: {e}")
+        return None
+
+model = load_trained_model()
+
+# --- 3. Prediction Function ---
+def predict_image(image_file, model):
+    """Uploaded image par prediction karta hai."""
+    if model is None:
+        return "Model Load Failed", 0.0
+
+    # PIL image ko array mein convert karein
+    img = Image.open(image_file).convert("RGB")
+    # Image ko model ke input size mein resize karein (224x224)
+    img = img.resize(IMAGE_SIZE)
+    img_array = np.array(img)
+    
+    # Batch dimension add karein: (224, 224, 3) se (1, 224, 224, 3)
+    img_array = np.expand_dims(img_array, axis=0)
+    
+    # Normalization (jaisa training mein kiya tha: 1./255)
+    img_array = img_array / 255.0
+
+    # Prediction karein
+    predictions = model.predict(img_array)
+    
+    # Highest probability index aur score nikalien
+    predicted_index = np.argmax(predictions, axis=1)[0]
+    confidence_score = np.max(predictions) * 100
+
+    predicted_class = CLASS_NAMES[predicted_index]
+    
+    return predicted_class, confidence_score
+
+# --- 4. Streamlit UI ---
+
+st.set_page_config(page_title="Brain Tumor Detection", layout="wide")
+
+# a) Page Title
+st.title("🧠 Brain Tumor Detection System (AI Powered)")
+st.write("Upload an MRI image below to classify it as one of the tumor types or no tumor.")
+st.markdown("---")
+
+col1, col2 = st.columns(2)
+
+with col1:
+    # b) Image Upload Section
+    uploaded_file = st.file_uploader("Upload MRI Image:", type=["jpg", "jpeg", "png"])
+    
+    if uploaded_file is not None:
+        # Image Preview
+        st.image(uploaded_file, caption="Uploaded MRI Image", use_column_width=True)
+        st.markdown("---")
+
+        # c) Prediction Button
+        if st.button("Detect Tumor"):
+            st.spinner("Analyzing image and detecting tumor...")
+            
+            # Prediction
+            predicted_class, confidence_score = predict_image(uploaded_file, model)
+            
+            # d) Output Section
+            
+            # Simplified classification for the app display (Tumor / No Tumor)
+            if predicted_class == 'no_tumor':
+                result_label = f"🟢 **Prediction: No Tumor**"
+            elif predicted_class == 'Model Load Failed':
+                result_label = f"❌ **Prediction: Model Initialization Error**"
+                st.error("Model could not be loaded for prediction. Please check logs for Hugging Face download errors.")
+            else:
+                result_label = f"🔴 **Prediction: Tumor ({predicted_class.replace('_', ' ').title()})**"
+            
+            st.success("✅ Analysis Complete")
+            st.subheader(result_label)
+            st.metric(label="Confidence Score", value=f"{confidence_score:.2f}%")
+            
+            st.write("---")
+
+with col2:
+    st.header("Results and Interpretation")
+    st.info("The system uses Transfer Learning (VGG16) to classify the image into four categories: Glioma, Meningioma, Pituitary, or No Tumor.")
+    
+    if uploaded_file is None:
+        st.warning("Please upload an image and click 'Detect Tumor' to see the results.")

brain_tumor_model.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d20e5a0cf49890f3b06898737a2f0a84dc9febfe70a184da563c967d7846cbf5
+size 136037810

requirements.txt

@@ -0,0 +1,166 @@
+absl-py==2.3.1
+aiohappyeyeballs==2.6.1
+aiohttp==3.13.2
+aiosignal==1.4.0
+altair==6.0.0
+annotated-types==0.7.0
+anyio==4.12.0
+astunparse==1.6.3
+async-timeout==4.0.3
+attrs==25.4.0
+backoff==2.2.1
+bcrypt==5.0.0
+blinker==1.9.0
+build==1.3.0
+cachetools==6.2.2
+certifi==2025.11.12
+charset-normalizer==3.4.4
+chromadb==1.3.6
+click==8.3.1
+colorama==0.4.6
+coloredlogs==15.0.1
+dataclasses-json==0.6.7
+distro==1.9.0
+durationpy==0.10
+exceptiongroup==1.3.1
+filelock==3.20.0
+flatbuffers==25.9.23
+frozenlist==1.8.0
+fsspec==2025.12.0
+gast==0.7.0
+gitdb==4.0.12
+GitPython==3.1.45
+google-auth==2.43.0
+google-pasta==0.2.0
+googleapis-common-protos==1.72.0
+greenlet==3.3.0
+grpcio==1.76.0
+h11==0.16.0
+h5py==3.15.1
+hf-xet==1.2.0
+httpcore==1.0.9
+httptools==0.7.1
+httpx==0.28.1
+httpx-sse==0.4.3
+huggingface_hub==1.2.2
+humanfriendly==10.0
+idna==3.11
+importlib_metadata==8.7.0
+importlib_resources==6.5.2
+Jinja2==3.1.6
+jiter==0.12.0
+jsonpatch==1.33
+jsonpointer==3.0.0
+jsonschema==4.25.1
+jsonschema-specifications==2025.9.1
+kagglehub==0.3.13
+keras==3.12.0
+kubernetes==34.1.0
+langchain==1.1.3
+langchain-classic==1.0.0
+langchain-community==0.4.1
+langchain-core==1.1.3
+langchain-openai==1.1.1
+langchain-text-splitters==1.0.0
+langgraph==1.0.4
+langgraph-checkpoint==3.0.1
+langgraph-prebuilt==1.0.5
+langgraph-sdk==0.2.15
+langsmith==0.4.59
+libclang==18.1.1
+Markdown==3.10
+markdown-it-py==4.0.0
+MarkupSafe==3.0.3
+marshmallow==3.26.1
+mdurl==0.1.2
+ml_dtypes==0.5.4
+mmh3==5.2.0
+mpmath==1.3.0
+multidict==6.7.0
+mypy_extensions==1.1.0
+namex==0.1.0
+narwhals==2.13.0
+numpy==2.2.6
+oauthlib==3.3.1
+onnxruntime==1.23.2
+openai==2.9.0
+opentelemetry-api==1.39.0
+opentelemetry-exporter-otlp-proto-common==1.39.0
+opentelemetry-exporter-otlp-proto-grpc==1.39.0
+opentelemetry-proto==1.39.0
+opentelemetry-sdk==1.39.0
+opentelemetry-semantic-conventions==0.60b0
+opt_einsum==3.4.0
+optree==0.18.0
+orjson==3.11.5
+ormsgpack==1.12.0
+overrides==7.7.0
+packaging==25.0
+pandas==2.3.3
+pillow==12.0.0
+posthog==5.4.0
+propcache==0.4.1
+protobuf==6.33.2
+pyarrow==22.0.0
+pyasn1==0.6.1
+pyasn1_modules==0.4.2
+pybase64==1.4.3
+pydantic==2.12.5
+pydantic-settings==2.12.0
+pydantic_core==2.41.5
+pydeck==0.9.1
+Pygments==2.19.2
+pypdf==6.4.1
+PyPika==0.48.9
+pyproject_hooks==1.2.0
+pyreadline3==3.5.4
+python-dateutil==2.9.0.post0
+python-dotenv==1.2.1
+pytz==2025.2
+PyYAML==6.0.3
+referencing==0.37.0
+regex==2025.11.3
+requests==2.32.5
+requests-oauthlib==2.0.0
+requests-toolbelt==1.0.0
+huggingface-hub
+rich==14.2.0
+rpds-py==0.30.0
+rsa==4.9.1
+shellingham==1.5.4
+six==1.17.0
+smmap==5.0.2
+sniffio==1.3.1
+SQLAlchemy==2.0.45
+streamlit==1.52.1
+sympy==1.14.0
+tenacity==9.1.2
+tensorboard==2.20.0
+tensorboard-data-server==0.7.2
+tensorflow==2.20.0
+termcolor==3.2.0
+tiktoken==0.12.0
+tokenizers==0.22.1
+toml==0.10.2
+tomli==2.3.0
+tornado==6.5.3
+tqdm==4.67.1
+typer==0.20.0
+typer-slim==0.20.0
+typing-inspect==0.9.0
+typing-inspection==0.4.2
+typing_extensions==4.15.0
+tzdata==2025.2
+urllib3==2.3.0
+uuid_utils==0.12.0
+uvicorn==0.38.0
+watchdog==6.0.0
+watchfiles==1.1.1
+websocket-client==1.9.0
+websockets==15.0.1
+Werkzeug==3.1.4
+wrapt==2.0.1
+xxhash==3.6.0
+yarl==1.22.0
+zipp==3.23.0
+zstandard==0.25.0

train_model.py

@@ -0,0 +1,83 @@
+import tensorflow as tf
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras.applications import VGG16
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Flatten, Dropout
+import os
+
+# --- 1. Configuration ---
+# Aapke folder structure ke hisaab se path
+DATA_DIR = 'dataset/Training' 
+IMAGE_SIZE = (224, 224) # VGG16 ke liye standard input size
+BATCH_SIZE = 32
+NUM_CLASSES = 4 # gliomat_tumor, meningioma_tumor, no_tumor, pituitary_tumor
+
+# --- 2. Data Preprocessing aur Augmentation ---
+# Yahan hum images ko normalize (0 se 1 ke beech) karenge.
+# Aur validation data ke liye, training data mein se hi kuch images nikalenge.
+train_datagen = ImageDataGenerator(
+    rescale=1./255,
+    validation_split=0.2, # 20% data validation ke liye use hoga
+    # Aap data augmentation bhi add kar sakte hain (e.g., rotation, zoom)
+)
+
+# Training Data Generator (80% images)
+train_generator = train_datagen.flow_from_directory(
+    DATA_DIR,
+    target_size=IMAGE_SIZE,
+    batch_size=BATCH_SIZE,
+    class_mode='categorical', # 4 classes ke liye 'categorical' use karte hain
+    subset='training'
+)
+
+# Validation Data Generator (20% images)
+validation_generator = train_datagen.flow_from_directory(
+    DATA_DIR,
+    target_size=IMAGE_SIZE,
+    batch_size=BATCH_SIZE,
+    class_mode='categorical',
+    subset='validation'
+)
+
+# --- 3. Transfer Learning Model Setup (VGG16) ---
+
+# Base model ko load karein. include_top=False ka matlab hai ki final classification layers nahi leni.
+base_model = VGG16(weights='imagenet', 
+                   include_top=False, 
+                   input_shape=(IMAGE_SIZE[0], IMAGE_SIZE[1], 3))
+
+# Base layers ko freeze karein taaki unke weights change na hon.
+for layer in base_model.layers:
+    layer.trainable = False
+
+# --- 4. Custom Classification Layers Add Karein ---
+model = Sequential([
+    base_model,
+    Flatten(), # 2D output ko 1D vector mein badalna
+    Dense(256, activation='relu'), # Pehli hidden layer
+    Dropout(0.5), # Overfitting se bachne ke liye
+    Dense(NUM_CLASSES, activation='softmax') # Output layer (4 classes)
+])
+
+# Model compile karein
+model.compile(optimizer='adam',
+              loss='categorical_crossentropy',
+              metrics=['accuracy'])
+
+model.summary()
+
+# --- 5. Model Training ---
+print("\n--- Starting Model Training ---")
+
+# Aapko epochs ki value badhaani pad sakti hai (e.g., 20 ya 30) acchi accuracy ke liye.
+history = model.fit(
+    train_generator,
+    epochs=10, 
+    validation_data=validation_generator
+)
+
+# --- 6. Model Save Karein ---
+# Ab is trained model ko save kar dein taki Streamlit use kar sake
+MODEL_FILENAME = 'brain_tumor_model.keras'
+model.save(MODEL_FILENAME) 
+print(f"\nModel successfully saved as: {MODEL_FILENAME}")