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app.py

@@ -1,168 +1,168 @@
-import os
-import traceback
-import numpy as np
-import streamlit as st
-from PIL import Image
-from transformers import pipeline
-import matplotlib.pyplot as plt
-from skimage.color import rgb2gray
-from skimage.filters import threshold_otsu
-
-
-# =======================
-# Configuration and Setup
-# =======================
-
-# Streamlit Page Configuration
-st.set_page_config(
-    page_title="AI Cancer Detection Platform",
-    page_icon="🩺",
-    layout="wide",
-    initial_sidebar_state="expanded",
-    menu_items={
-        "About": "### AI Cancer Detection Platform\n"
-                 "Developed to classify cancer images and provide research insights."
-    }
-)
-
-
-# =======================
-# Helper Functions
-# =======================
-
-@st.cache_resource
-def load_pipeline():
-    """
-    Load the pre-trained image classification pipeline using PyTorch as the backend.
-    """
-    try:
-        model_pipeline = pipeline(
-            "image-classification",
-            model="Anwarkh1/Skin_Cancer-Image_Classification",
-            framework="pt"  # Force PyTorch backend
-        )
-        return model_pipeline
-    except Exception as e:
-        st.error(f"Error loading model: {e}")
-        traceback.print_exc()
-        st.stop()
-
-
-def process_image(image):
-    """
-    Perform image processing to extract features for better visualization.
-    """
-    try:
-        # Convert image to grayscale
-        gray_image = rgb2gray(np.array(image))
-
-        # Apply Otsu's threshold
-        thresh = threshold_otsu(gray_image)
-        binary = gray_image > thresh
-
-        # Calculate edge pixel percentage
-        edge_pixels = np.sum(binary)
-        total_pixels = binary.size
-        edge_percentage = (edge_pixels / total_pixels) * 100
-
-        # Generate plots
-        fig, ax = plt.subplots(1, 2, figsize=(10, 5))
-        ax[0].imshow(gray_image, cmap="gray")
-        ax[0].set_title("Grayscale Image")
-        ax[0].axis("off")
-
-        ax[1].imshow(binary, cmap="gray")
-        ax[1].set_title("Binary Image (Thresholded)")
-        ax[1].axis("off")
-
-        plt.tight_layout()
-        st.pyplot(fig)
-
-        # Feature description
-        return f"{edge_percentage:.2f}% of the image contains edge pixels after thresholding."
-
-    except Exception as e:
-        st.error(f"Error processing image: {e}")
-        traceback.print_exc()
-        return "No significant features extracted."
-
-
-def classify_image(image, model_pipeline):
-    """
-    Classify the uploaded image using the pre-trained model pipeline.
-    """
-    try:
-        # Resize image to 224x224 as required by the model
-        image_resized = image.resize((224, 224))
-        predictions = model_pipeline(image_resized)
-
-        if predictions:
-            top_prediction = predictions[0]
-            label = top_prediction["label"]
-            score = top_prediction["score"]
-            return label, score
-        else:
-            st.warning("No predictions were made.")
-            return None, None
-    except Exception as e:
-        st.error(f"Error during classification: {e}")
-        traceback.print_exc()
-        return None, None
-
-
-# =======================
-# Streamlit Main Content
-# =======================
-
-st.title("🩺 AI-Powered Cancer Detection")
-
-# Image Upload Section
-st.subheader("📤 Upload a Cancer Image")
-uploaded_image = st.file_uploader("Choose an image file...", type=["png", "jpg", "jpeg"])
-
-if uploaded_image is not None:
-    try:
-        # Open the uploaded image
-        image = Image.open(uploaded_image).convert("RGB")
-
-        # Display the uploaded image
-        st.image(image, caption="Uploaded Image", use_column_width=True)
-
-        # Process the image
-        st.markdown("### 🛠️ Image Processing")
-        processed_features = process_image(image)
-
-        # Load the model pipeline
-        st.markdown("### 🔍 Classifying the Image")
-        model_pipeline = load_pipeline()
-
-        # Classify the image
-        with st.spinner("Classifying..."):
-            label, confidence = classify_image(image, model_pipeline)
-
-        if label and confidence:
-            st.write(f"**Prediction:** {label}")
-            st.write(f"**Confidence:** {confidence:.2%}")
-
-            # Highlight prediction confidence
-            if confidence > 0.80:
-                st.success("High confidence in the prediction.")
-            elif confidence > 0.50:
-                st.warning("Moderate confidence in the prediction.")
-            else:
-                st.error("Low confidence in the prediction.")
-
-    except Exception as e:
-        st.error(f"An unexpected error occurred: {e}")
-        traceback.print_exc()
-else:
-    st.info("Upload an image to start the classification.")
-
-# =======================
-# Footer
-# =======================
-
-st.markdown("""
----
-**AI Cancer Detection Platform** | This application is for informational purposes only and is not intended for medical diagnosis.
-""")
+import os
+import traceback
+import numpy as np
+import streamlit as st
+from PIL import Image
+from transformers import pipeline
+import matplotlib.pyplot as plt
+from skimage.color import rgb2gray
+from skimage.filters import threshold_otsu
+
+
+# =======================
+# Configuration and Setup
+# =======================
+
+# Streamlit Page Configuration
+st.set_page_config(
+    page_title="AI Cancer Detection Platform",
+    page_icon="🩺",
+    layout="wide",
+    initial_sidebar_state="expanded",
+    menu_items={
+        "About": "### AI Cancer Detection Platform\n"
+                 "Developed to classify cancer images and provide research insights."
+    }
+)
+
+
+# =======================
+# Helper Functions
+# =======================
+
+@st.cache_resource
+def load_pipeline():
+    """
+    Load the pre-trained image classification pipeline using PyTorch as the backend.
+    """
+    try:
+        model_pipeline = pipeline(
+            "image-classification",
+            model="Anwarkh1/Skin_Cancer-Image_Classification",
+            framework="pt"  # Force PyTorch backend
+        )
+        return model_pipeline
+    except Exception as e:
+        st.error(f"Error loading model: {e}")
+        traceback.print_exc()
+        st.stop()
+
+
+def process_image(image):
+    """
+    Perform image processing to extract features for better visualization.
+    """
+    try:
+        # Convert image to grayscale
+        gray_image = rgb2gray(np.array(image))
+
+        # Apply Otsu's threshold
+        thresh = threshold_otsu(gray_image)
+        binary = gray_image > thresh
+
+        # Calculate edge pixel percentage
+        edge_pixels = np.sum(binary)
+        total_pixels = binary.size
+        edge_percentage = (edge_pixels / total_pixels) * 100
+
+        # Generate plots
+        fig, ax = plt.subplots(1, 2, figsize=(10, 5))
+        ax[0].imshow(gray_image, cmap="gray")
+        ax[0].set_title("Grayscale Image")
+        ax[0].axis("off")
+
+        ax[1].imshow(binary, cmap="gray")
+        ax[1].set_title("Binary Image (Thresholded)")
+        ax[1].axis("off")
+
+        plt.tight_layout()
+        st.pyplot(fig)
+
+        # Feature description
+        return f"{edge_percentage:.2f}% of the image contains edge pixels after thresholding."
+
+    except Exception as e:
+        st.error(f"Error processing image: {e}")
+        traceback.print_exc()
+        return "No significant features extracted."
+
+
+def classify_image(image, model_pipeline):
+    """
+    Classify the uploaded image using the pre-trained model pipeline.
+    """
+    try:
+        # Resize image to 224x224 as required by the model
+        image_resized = image.resize((224, 224))
+        predictions = model_pipeline(image_resized)
+
+        if predictions:
+            top_prediction = predictions[0]
+            label = top_prediction["label"]
+            score = top_prediction["score"]
+            return label, score
+        else:
+            st.warning("No predictions were made.")
+            return None, None
+    except Exception as e:
+        st.error(f"Error during classification: {e}")
+        traceback.print_exc()
+        return None, None
+
+
+# =======================
+# Streamlit Main Content
+# =======================
+
+st.title("🩺 AI-Powered Cancer Detection")
+
+# Image Upload Section
+st.subheader("📤 Upload a Cancer Image")
+uploaded_image = st.file_uploader("Choose an image file...", type=["png", "jpg", "jpeg"])
+
+if uploaded_image is not None:
+    try:
+        # Open the uploaded image
+        image = Image.open(uploaded_image).convert("RGB")
+
+        # Display the uploaded image
+        st.image(image, caption="Uploaded Image", use_column_width=True)
+
+        # Process the image
+        st.markdown("### 🛠️ Image Processing")
+        processed_features = process_image(image)
+
+        # Load the model pipeline
+        st.markdown("### 🔍 Classifying the Image")
+        model_pipeline = load_pipeline()
+
+        # Classify the image
+        with st.spinner("Classifying..."):
+            label, confidence = classify_image(image, model_pipeline)
+
+        if label and confidence:
+            st.write(f"**Prediction:** {label}")
+            st.write(f"**Confidence:** {confidence:.2%}")
+
+            # Highlight prediction confidence
+            if confidence > 0.80:
+                st.success("High confidence in the prediction.")
+            elif confidence > 0.50:
+                st.warning("Moderate confidence in the prediction.")
+            else:
+                st.error("Low confidence in the prediction.")
+
+    except Exception as e:
+        st.error(f"An unexpected error occurred: {e}")
+        traceback.print_exc()
+else:
+    st.info("Upload an image to start the classification.")
+
+# =======================
+# Footer
+# =======================
+
+st.markdown("""
+---
+**AI Cancer Detection Platform** | This application is for informational purposes only and is not intended for medical diagnosis.
+""")

requirements.txt

@@ -0,0 +1,5 @@
+streamlit
+transformers
+torch
+requests
+pillow