forst

.gitignore

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+env
+__pycache__
+oral_cancer_model.h5

Docerfile

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+FROM python:3.12.1
+
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user . /app
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI, File, UploadFile, HTTPException
+from fastapi.staticfiles import StaticFiles
+from fastapi.middleware.cors import CORSMiddleware
+import tensorflow as tf
+from tensorflow import keras
+import numpy as np
+from PIL import Image
+import io
+from huggingface_hub import hf_hub_download
+
+app = FastAPI(title="Medical Image Classification API", version="1.0.0")
+
+# Add CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # Configure this appropriately for production
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+weights_path = hf_hub_download(
+    repo_id="Rahul-Samedavar/OralCancer_Predictor",
+    filename="weights.h5",
+)
+
+# Load model
+base_model = tf.keras.applications.DenseNet121(
+    input_shape=(224, 224, 3),
+    include_top=False,
+    weights='imagenet'
+)
+base_model.trainable = False
+
+model = keras.models.Sequential()
+model.add(base_model)
+model.add(keras.layers.BatchNormalization())
+model.add(keras.layers.Flatten())
+model.add(keras.layers.Dense(1024, activation=tf.nn.relu, kernel_regularizer=keras.regularizers.l2(0.01)))
+model.add(keras.layers.Dropout(.3))
+model.add(keras.layers.Dense(2, activation=tf.nn.softmax))
+model.load_weights(weights_path)
+
+class_names = ['Normal', 'OSCC']
+
+def preprocess_image(image_bytes: bytes) -> np.ndarray:
+    """Preprocess image for model prediction"""
+    image = Image.open(io.BytesIO(image_bytes)).convert('RGB')
+    image = image.resize((224, 224))
+    img_array = np.array(image) / 255.0
+    return np.expand_dims(img_array, axis=0)
+
+@app.post("/predict")
+async def predict(image: UploadFile = File(...)):
+    """Predict medical condition from uploaded image"""
+    
+    # Validate file type
+    if not image.content_type.startswith('image/'):
+        raise HTTPException(status_code=400, detail="File must be an image")
+    
+    try:
+        # Read image bytes
+        img_bytes = await image.read()
+        
+        # Preprocess image
+        processed_image = preprocess_image(img_bytes)
+        
+        # Make prediction
+        predictions = model.predict(processed_image)[0]
+        predicted_class = class_names[np.argmax(predictions)]
+        
+        # Format confidence scores
+        confidence = {
+            class_names[i]: float(f"{predictions[i]:.4f}") 
+            for i in range(len(class_names))
+        }
+        
+        return {
+            "predicted_class": predicted_class,
+            "confidence_scores": confidence
+        }
+        
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error processing image: {str(e)}")
+
+
+app.mount("/", StaticFiles(directory="static/main", html=True),  name="main")
+app.mount("/cnn/", StaticFiles(directory="static/cnn", html=True), name="cnn")
+
+@app.get("/health")
+async def health():
+    """Health check endpoint"""
+    return {"status": "healthy"}
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)

requirements.txt

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+tensorflow
+numpy
+fastapi
+uvicorn
+pillow
+flask_cors
+huggingface
+python-multipart
+huggingface_hub

static/cnn/assets/index-BkrFbvbF.css

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theme</span></button><button class="text-gray-300 hover:text-white focus:outline-none"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-menu"><line x1="4" x2="20" y1="12" y2="12"></line><line x1="4" x2="20" y1="6" y2="6"></line><line x1="4" x2="20" y1="18" y2="18"></line></svg></button></div></div></div></nav><main class="flex-grow"><div class="py-20 bg-gray-950 min-h-screen"><div class="container mx-auto px-4"><div class="max-w-4xl mx-auto" style="opacity:0"><h1 class="text-3xl md:text-5xl font-bold mb-8 text-center" style="opacity:0;transform:translateY(20px)">Diagnosis Steps</h1><p class="text-gray-300 text-center mb-16 max-w-2xl mx-auto" style="opacity:0;transform:translateY(20px)">The AI-powered oral cancer diagnosis process follows a systematic workflow from image acquisition to final report generation. Each step is optimized for accuracy and efficiency.</p><div class="relative"><div class="absolute left-1/2 transform -translate-x-1/2 h-full w-1 bg-gradient-to-b from-purple-600 to-cyan-500 rounded-full hidden md:block"></div><div class="space-y-24 relative"><div class="flex flex-col md:flex-row items-center" style="opacity:0;transform:translateY(50px)"><div class="md:w-1/2 md:pr-12 md:text-right"><h3 class="text-2xl font-semibold mb-3">Tissue Collection</h3><p class="text-gray-300"><span class="relative inline-block"><span class="border-b border-dashed border-purple-400 text-purple-400 cursor-help" tabindex="0">Biopsy</span></span> samples are collected from the epithelial lining of the oral cavity for further analysis.</p></div><div class="my-6 md:my-0 relative"><div class="w-16 h-16 rounded-full bg-gray-800 flex items-center justify-center z-10 relative border-4 border-gray-950"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-microscope h-8 w-8"><path d="M6 18h8"></path><path d="M3 22h18"></path><path d="M14 22a7 7 0 1 0 0-14h-1"></path><path d="M9 14h2"></path><path d="M9 12a2 2 0 0 1-2-2V6h6v4a2 2 0 0 1-2 2Z"></path><path d="M12 6V3a1 1 0 0 0-1-1H9a1 1 0 0 0-1 1v3"></path></svg></div></div><div class="md:w-1/2"></div></div><div class="flex flex-col md:flex-row-reverse items-center" style="opacity:0;transform:translateY(50px)"><div class="md:w-1/2 md:pl-12"><h3 class="text-2xl font-semibold mb-3">Microscopic Imaging</h3><p class="text-gray-300">Tissue samples are examined under an electronic microscope to produce high-resolution <span class="relative inline-block"><span class="border-b border-dashed border-purple-400 text-purple-400 cursor-help" tabindex="0">histopathological</span></span> images.</p></div><div class="my-6 md:my-0 relative"><div class="w-16 h-16 rounded-full bg-gray-800 flex items-center justify-center z-10 relative border-4 border-gray-950"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-file-image h-8 w-8"><path d="M15 2H6a2 2 0 0 0-2 2v16a2 2 0 0 0 2 2h12a2 2 0 0 0 2-2V7Z"></path><path d="M14 2v4a2 2 0 0 0 2 2h4"></path><circle cx="10" cy="12" r="2"></circle><path d="m20 17-1.296-1.296a2.41 2.41 0 0 0-3.408 0L9 22"></path></svg></div></div><div class="md:w-1/2"></div></div><div class="flex flex-col md:flex-row items-center" style="opacity:0;transform:translateY(50px)"><div class="md:w-1/2 md:pr-12 md:text-right"><h3 class="text-2xl font-semibold mb-3">Histopathology Imaging</h3><p class="text-gray-300">The observed cellular structures are digitally captured as <span class="relative inline-block"><span class="border-b border-dashed border-purple-400 text-purple-400 cursor-help" tabindex="0">histopathological</span></span> images for diagnostic evaluation.</p></div><div class="my-6 md:my-0 relative"><div class="w-16 h-16 rounded-full bg-gray-800 flex items-center justify-center z-10 relative border-4 border-gray-950"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-cpu h-8 w-8"><rect width="16" height="16" x="4" y="4" rx="2"></rect><rect width="6" height="6" x="9" y="9" rx="1"></rect><path d="M15 2v2"></path><path d="M15 20v2"></path><path d="M2 15h2"></path><path d="M2 9h2"></path><path d="M20 15h2"></path><path d="M20 9h2"></path><path d="M9 2v2"></path><path d="M9 20v2"></path></svg></div></div><div class="md:w-1/2"></div></div><div class="flex flex-col md:flex-row-reverse items-center" style="opacity:0;transform:translateY(50px)"><div class="md:w-1/2 md:pl-12"><h3 class="text-2xl font-semibold mb-3">Image Digitization</h3><p class="text-gray-300">These images are digitized and fed into the computer system, preprocessed for consistency and clarity.</p></div><div class="my-6 md:my-0 relative"><div class="w-16 h-16 rounded-full bg-gray-800 flex items-center justify-center z-10 relative border-4 border-gray-950"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-brain-circuit h-8 w-8"><path d="M12 5a3 3 0 1 0-5.997.125 4 4 0 0 0-2.526 5.77 4 4 0 0 0 .556 6.588A4 4 0 1 0 12 18Z"></path><path d="M9 13a4.5 4.5 0 0 0 3-4"></path><path d="M6.003 5.125A3 3 0 0 0 6.401 6.5"></path><path d="M3.477 10.896a4 4 0 0 1 .585-.396"></path><path d="M6 18a4 4 0 0 1-1.967-.516"></path><path d="M12 13h4"></path><path d="M12 18h6a2 2 0 0 1 2 2v1"></path><path d="M12 8h8"></path><path d="M16 8V5a2 2 0 0 1 2-2"></path><circle cx="16" cy="13" r=".5"></circle><circle cx="18" cy="3" r=".5"></circle><circle cx="20" cy="21" r=".5"></circle><circle cx="20" cy="8" r=".5"></circle></svg></div></div><div class="md:w-1/2"></div></div><div class="flex flex-col md:flex-row items-center" style="opacity:0;transform:translateY(50px)"><div class="md:w-1/2 md:pr-12 md:text-right"><h3 class="text-2xl font-semibold mb-3">CNN Processing</h3><p class="text-gray-300">A <span class="relative inline-block"><span class="border-b border-dashed border-purple-400 text-purple-400 cursor-help" tabindex="0">convolutional neural network</span></span> analyzes the image, extracting features indicative of cancerous changes.</p></div><div class="my-6 md:my-0 relative"><div class="w-16 h-16 rounded-full bg-gray-800 flex items-center justify-center z-10 relative border-4 border-gray-950"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-brain-circuit h-8 w-8"><path d="M12 5a3 3 0 1 0-5.997.125 4 4 0 0 0-2.526 5.77 4 4 0 0 0 .556 6.588A4 4 0 1 0 12 18Z"></path><path d="M9 13a4.5 4.5 0 0 0 3-4"></path><path d="M6.003 5.125A3 3 0 0 0 6.401 6.5"></path><path d="M3.477 10.896a4 4 0 0 1 .585-.396"></path><path d="M6 18a4 4 0 0 1-1.967-.516"></path><path d="M12 13h4"></path><path d="M12 18h6a2 2 0 0 1 2 2v1"></path><path d="M12 8h8"></path><path d="M16 8V5a2 2 0 0 1 2-2"></path><circle cx="16" cy="13" r=".5"></circle><circle cx="18" cy="3" r=".5"></circle><circle cx="20" cy="21" r=".5"></circle><circle cx="20" cy="8" r=".5"></circle></svg></div></div><div class="md:w-1/2"></div></div><div class="flex flex-col md:flex-row-reverse items-center" style="opacity:0;transform:translateY(50px)"><div class="md:w-1/2 md:pl-12"><h3 class="text-2xl font-semibold mb-3">Diagnosis Output</h3><p class="text-gray-300">The model outputs a classification—<span class="relative inline-block"><span 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focus:outline-none"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-menu"><line x1="4" x2="20" y1="12" y2="12"></line><line x1="4" x2="20" y1="6" y2="6"></line><line x1="4" x2="20" y1="18" y2="18"></line></svg></button></div></div></div></nav><main class="flex-grow"><div class="relative"><section class="relative h-screen flex items-center justify-center overflow-hidden"><canvas class="absolute inset-0 w-full h-full bg-gradient-to-b from-gray-950 to-gray-900"></canvas><div class="container mx-auto px-4 z-10 text-center"><div class="space-y-6" style="opacity:0"><h1 class="text-4xl md:text-6xl font-bold bg-clip-text text-transparent bg-gradient-to-r from-purple-500 to-cyan-500" style="opacity:0;transform:translateY(20px)">AI in Oral Cancer Diagnosis</h1><p class="text-xl md:text-2xl max-w-2xl mx-auto text-gray-300" style="opacity:0;transform:translateY(20px)">Early detection through intelligent imaging</p><div class="flex flex-col sm:flex-row gap-4 justify-center mt-8" style="opacity:0;transform:translateY(20px)"><button class="px-6 py-3 bg-purple-600 hover:bg-purple-700 rounded-lg font-medium flex items-center justify-center gap-2 transition-colors">Learn More <svg xmlns="http://www.w3.org/2000/svg" width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-arrow-down"><path d="M12 5v14"></path><path d="m19 12-7 7-7-7"></path></svg></button><a class="px-6 py-3 bg-cyan-600 hover:bg-cyan-700 rounded-lg font-medium transition-colors" href="/predict">Try Prediction</a></div></div></div><div class="absolute bottom-8 left-1/2 transform -translate-x-1/2 animate-bounce"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-arrow-down"><path d="M12 5v14"></path><path d="m19 12-7 7-7-7"></path></svg></div></section><section class="py-20 bg-gray-900"><div class="container mx-auto px-4"><div class="max-w-3xl mx-auto" style="opacity:0"><h2 class="text-3xl md:text-4xl font-bold mb-8 text-center" style="opacity:0;transform:translateY(20px)">About Oral Cancer</h2><div class="space-y-6 text-gray-300" style="opacity:0;transform:translateY(20px)"><p>Oral cancer is a significant global health concern, characterized by the uncontrolled growth of cells in the oral cavity. It typically manifests as a persistent<!-- --> <span class="relative inline-block"><span class="border-b border-dashed border-purple-400 text-purple-400 cursor-help" tabindex="0">lesion</span></span> <!-- -->in the mouth that doesn&#x27;t heal within two weeks. The most common form is squamous cell carcinoma, which accounts for over 90% of all oral malignancies.</p><p>Risk factors include tobacco use (smoking and smokeless), excessive alcohol consumption, human papillomavirus (HPV) infection, and prolonged sun exposure (for lip cancers). Early detection is crucial, often requiring a<!-- --> <span class="relative inline-block"><span class="border-b border-dashed border-purple-400 text-purple-400 cursor-help" tabindex="0">biopsy</span></span> <!-- -->for definitive diagnosis.</p><p>In countries like India, oral cancer is particularly prevalent, accounting for approximately 30% of all cancers. This high incidence is attributed to widespread tobacco and betel quid chewing habits. The five-year survival rate drops dramatically from 83% for localized cases to just 32% for cases where cancer has spread, highlighting the importance of early detection.</p><p>Early stages of oral cancer may present as white or red patches (leukoplakia or erythroplakia) that can progress to<!-- --> <span class="relative inline-block"><span class="border-b border-dashed border-purple-400 text-purple-400 cursor-help" tabindex="0">dysplasia</span></span> <!-- -->and eventually invasive carcinoma if left untreated. Traditional diagnostic methods rely heavily on clinical examination followed by histopathological analysis, which can be time-consuming and subjective.</p></div></div></div></section><section class="py-20 bg-gray-950"><div class="container mx-auto px-4"><div class="max-w-4xl mx-auto" style="opacity:0"><h2 class="text-3xl md:text-4xl font-bold mb-12 text-center" style="opacity:0;transform:translateY(20px)">AI in Diagnosis</h2><div class="space-y-8 text-gray-300" style="opacity:0;transform:translateY(20px)"><p><span class="relative inline-block"><span class="border-b border-dashed border-purple-400 text-purple-400 cursor-help" tabindex="0">Convolutional neural networks</span></span> <!-- -->(CNNs) have revolutionized medical image analysis by automatically extracting relevant features from oral cavity images. These networks consist of multiple layers that progressively learn to identify patterns associated with cancerous and pre-cancerous conditions.</p><div class="grid grid-cols-1 md:grid-cols-3 gap-8 my-12"><div class="bg-gray-800 p-6 rounded-lg flex flex-col items-center text-center"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-microscope h-12 w-12 text-purple-500 mb-4"><path d="M6 18h8"></path><path d="M3 22h18"></path><path d="M14 22a7 7 0 1 0 0-14h-1"></path><path d="M9 14h2"></path><path d="M9 12a2 2 0 0 1-2-2V6h6v4a2 2 0 0 1-2 2Z"></path><path d="M12 6V3a1 1 0 0 0-1-1H9a1 1 0 0 0-1 1v3"></path></svg><h3 class="text-xl font-semibold mb-2">Input</h3><p class="text-gray-400">High-resolution images of oral tissue are fed into the model</p></div><div class="bg-gray-800 p-6 rounded-lg flex flex-col items-center text-center"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-brain h-12 w-12 text-cyan-500 mb-4"><path d="M12 5a3 3 0 1 0-5.997.125 4 4 0 0 0-2.526 5.77 4 4 0 0 0 .556 6.588A4 4 0 1 0 12 18Z"></path><path d="M12 5a3 3 0 1 1 5.997.125 4 4 0 0 1 2.526 5.77 4 4 0 0 1-.556 6.588A4 4 0 1 1 12 18Z"></path><path d="M15 13a4.5 4.5 0 0 1-3-4 4.5 4.5 0 0 1-3 4"></path><path d="M17.599 6.5a3 3 0 0 0 .399-1.375"></path><path d="M6.003 5.125A3 3 0 0 0 6.401 6.5"></path><path d="M3.477 10.896a4 4 0 0 1 .585-.396"></path><path d="M19.938 10.5a4 4 0 0 1 .585.396"></path><path d="M6 18a4 4 0 0 1-1.967-.516"></path><path d="M19.967 17.484A4 4 0 0 1 18 18"></path></svg><h3 class="text-xl font-semibold mb-2">Feature Extraction</h3><p class="text-gray-400">The CNN identifies key visual patterns using<!-- --> <span class="relative inline-block"><span class="border-b border-dashed border-purple-400 text-purple-400 cursor-help" tabindex="0">activation functions</span></span></p></div><div class="bg-gray-800 p-6 rounded-lg flex flex-col items-center text-center"><div class="h-12 w-12 text-green-500 mb-4 flex items-center justify-center"><svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-10 h-10"><path stroke-linecap="round" stroke-linejoin="round" d="M7.5 14.25v2.25m3-4.5v4.5m3-6.75v6.75m3-9v9M6 20.25h12A2.25 2.25 0 0020.25 18V6A2.25 2.25 0 0018 3.75H6A2.25 2.25 0 003.75 6v12A2.25 2.25 0 006 20.25z"></path></svg></div><h3 class="text-xl font-semibold mb-2">Classification</h3><p class="text-gray-400">The<!-- --> <span class="relative inline-block"><span class="border-b border-dashed border-purple-400 text-purple-400 cursor-help" tabindex="0">classifier</span></span> <!-- -->determines the probability of malignancy</p></div></div><p>AI-powered diagnosis offers several advantages over traditional methods:</p><ul class="list-disc pl-6 space-y-2"><li>Reduced subjectivity and inter-observer variability</li><li>Faster results, enabling quicker treatment decisions</li><li>Potential for earlier detection of subtle changes invisible to the human eye</li><li>Accessibility in regions with limited access to pathologists</li><li>Quantitative assessment of disease progression</li></ul><p>By combining the expertise of healthcare professionals with the analytical power of AI, we can significantly improve early detection rates and patient outcomes in oral cancer cases.</p></div></div></div></section></div></main><footer class="bg-gray-900 py-12 border-t border-gray-800"><div class="container mx-auto px-4"><div class="flex flex-col md:flex-row justify-between items-center"><div class="mb-6 md:mb-0"><h2 class="text-xl font-bold bg-clip-text text-transparent bg-gradient-to-r from-purple-500 to-cyan-500">AI-OralCancer</h2><p class="text-gray-400 mt-2 max-w-md">Advancing early 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