Delete src/streamlit_app.py

src/streamlit_app.py

@@ -1,249 +0,0 @@
-import streamlit as st
-import torch
-import torch.nn as nn
-import torchvision.models as models
-import torchvision.transforms as transforms
-import numpy as np
-from PIL import Image
-import cv2
-from huggingface_hub import hf_hub_download
-
-HF_REPO_ID = "Eklavya16/DermAssist"
-
-CLASSIFICATION_THRESHOLD = 0.5
-UNCERTAINTY_THRESHOLD = 0.165
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-st.set_page_config(
-    page_title="DermAssist – Clinical Dermoscopic AI",
-    layout="wide"
-)
-
-def build_model():
-    model = models.resnet50(weights="IMAGENET1K_V1")
-    in_features = model.fc.in_features
-    model.fc = nn.Sequential(
-        nn.Linear(in_features, 256),
-        nn.ReLU(),
-        nn.Dropout(p=0.5),
-        nn.Linear(256, 1)
-    )
-    return model.to(device)
-
-@st.cache_resource
-def load_models():
-    models_list = []
-    for i in range(1, 4):
-        model_path = hf_hub_download(
-            repo_id=HF_REPO_ID,
-            filename=f"resnet50_model_{i}.pth"
-        )
-        
-        model = build_model()
-        model.load_state_dict(
-            torch.load(model_path, map_location=device)
-        )
-        model.eval()
-        models_list.append(model)
-    return models_list
-
-ensemble_models = load_models()
-
-val_transform = transforms.Compose([
-    transforms.Resize((224, 224)),
-    transforms.ToTensor(),
-    transforms.Normalize(
-        mean=[0.485, 0.456, 0.406],
-        std=[0.229, 0.224, 0.225]
-    )
-])
-
-class GradCAM:
-    def __init__(self, model, target_layer):
-        self.model = model
-        self.gradients = None
-        self.activations = None
-
-        target_layer.register_forward_hook(self.save_activation)
-        target_layer.register_backward_hook(self.save_gradient)
-
-    def save_activation(self, module, input, output):
-        self.activations = output
-
-    def save_gradient(self, module, grad_input, grad_output):
-        self.gradients = grad_output[0]
-
-    def generate(self, input_image, class_idx):
-        self.model.zero_grad()
-        output = self.model(input_image)
-        loss = output[0]
-        loss.backward()
-
-        gradients = self.gradients[0].cpu().data.numpy()
-        activations = self.activations[0].cpu().data.numpy()
-
-        weights = np.mean(gradients, axis=(1, 2))
-        cam = np.zeros(activations.shape[1:], dtype=np.float32)
-
-        for i, w in enumerate(weights):
-            cam += w * activations[i]
-
-        cam = np.maximum(cam, 0)
-        cam = cv2.resize(cam, (224, 224))
-
-        cam[cam < np.percentile(cam, 75)] = 0
-
-        if cam.max() > 0:
-            cam = cam / cam.max()
-
-        return cam
-
-target_layer = ensemble_models[0].layer4[-1]
-gradcam = GradCAM(ensemble_models[0], target_layer)
-
-def ensemble_predict(models, image_tensor):
-    probs_list = []
-
-    with torch.no_grad():
-        for model in models:
-            output = model(image_tensor)
-            prob = torch.sigmoid(output).item()
-            probs_list.append(prob)
-
-    mean_prob = np.mean(probs_list)
-    std_prob = np.std(probs_list)
-
-    return mean_prob, std_prob, probs_list
-
-def decision_logic(mean_prob, std_prob):
-    if std_prob > UNCERTAINTY_THRESHOLD:
-        return "UNCERTAIN"
-
-    if mean_prob >= 0.75:
-        return "HIGH RISK"
-
-    if mean_prob >= CLASSIFICATION_THRESHOLD:
-        return "MODERATE RISK"
-
-    return "LOW RISK"
-
-def overlay_gradcam(original_image, cam):
-    image = np.array(original_image.resize((224, 224)))
-    heatmap = cv2.applyColorMap(np.uint8(255 * cam), cv2.COLORMAP_JET)
-    heatmap = cv2.cvtColor(heatmap, cv2.COLOR_BGR2RGB)
-
-    overlay = cv2.addWeighted(image, 0.6, heatmap, 0.4, 0)
-    return overlay
-
-st.sidebar.title("About DermAssist")
-
-st.sidebar.write("""
-DermAssist is an AI-powered dermoscopic analysis system trained on HAM10000
-and externally validated on ISIC 2019.
-
-This system:
-- Uses a 3-model ResNet50 ensemble
-- Provides calibrated risk scores
-- Estimates uncertainty via model disagreement
-- Generates Grad-CAM visual explanations
-""")
-
-st.sidebar.write("---")
-st.sidebar.write("Clinical Use Disclaimer:")
-st.sidebar.write("""
-This tool is for research and educational purposes only.
-It does not replace professional medical diagnosis.
-""")
-
-st.title("DermAssist – Clinical Dermoscopic Risk Triage System")
-
-page = st.radio("Select View", ["Prediction", "Validation Metrics"])
-
-if page == "Prediction":
-
-    uploaded_file = st.file_uploader("Upload Dermoscopic Image", type=["jpg","jpeg","png"])
-
-    if uploaded_file:
-        image = Image.open(uploaded_file).convert("RGB")
-
-        col1, col2 = st.columns(2)
-
-        with col1:
-            st.image(image, caption="Uploaded Image", use_container_width=True)
-
-        image_tensor = val_transform(image).unsqueeze(0).to(device)
-
-        mean_prob, std_prob, individual_probs = ensemble_predict(
-            ensemble_models,
-            image_tensor
-        )
-
-        decision = decision_logic(mean_prob, std_prob)
-        confidence = 1 - std_prob
-
-        target_class = 1 if mean_prob >= CLASSIFICATION_THRESHOLD else 0
-        cam = gradcam.generate(image_tensor, target_class)
-        overlay = overlay_gradcam(image, cam)
-
-        with col2:
-            st.image(overlay, caption="Grad-CAM Attention Map", use_container_width=True)
-
-        st.write("---")
-
-        if decision == "HIGH RISK":
-            st.error("High Risk – Immediate Clinical Evaluation Recommended")
-        elif decision == "MODERATE RISK":
-            st.warning("Moderate Risk – Professional Evaluation Recommended")
-        elif decision == "UNCERTAIN":
-            st.info("Uncertain – Dermatologist Review Recommended")
-        else:
-            st.success("Low Risk – Monitor and Recheck")
-
-        st.subheader("Prediction Summary")
-
-        st.metric("Malignancy Probability", f"{mean_prob * 100:.2f}%")
-        st.metric("Confidence Score", f"{confidence * 100:.2f}%")
-        st.metric("Model Disagreement", f"{std_prob * 100:.2f}%")
-
-        st.write("Individual Model Outputs:")
-        for i, p in enumerate(individual_probs):
-            st.write(f"Model {i+1}: {p*100:.2f}%")
-
-        st.write("---")
-        st.write("Clinical Notes:")
-        st.write("""
-- Probability reflects estimated malignancy risk.
-- Confidence is derived from ensemble agreement.
-- High model disagreement indicates uncertainty.
-- Grad-CAM highlights regions influencing the model's decision.
-""")
-
-if page == "Validation Metrics":
-
-    st.subheader("Internal Validation (HAM10000)")
-
-    st.write("""
-    Ensemble AUC: 0.937  
-    Malignant Recall: ~0.93  
-    Accuracy (t=0.35): 82%
-    """)
-
-    st.write("""
-    The model demonstrates strong internal performance with
-    calibrated sensitivity for melanoma detection.
-    """)
-
-    st.write("---")
-    st.subheader("External Validation (ISIC 2019)")
-    st.write("""
-    External AUC: 0.740  
-    Precision (t=0.31): 80.7%  
-    Recall (t=0.31): 50.0%  
-    """)
-    
-    st.write("""
-    External testing revealed expected performance drop due to domain shift,
-    while maintaining clinically useful accuracy. The model generalizes well
-    to independent datasets.
-    """)