Update app.py

app.py

@@ -1,168 +1,118 @@
-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 streamlit as st
+from transformers import pipeline, AutoModelForImageClassification, AutoFeatureExtractor
+from PIL import Image
+import openai
+import os
+import torch
+
+# =======================
+# Streamlit Page Config (MUST BE FIRST)
+# =======================
+st.set_page_config(
+    page_title="AI-Powered Skin Cancer Detection",
+    page_icon="🩺",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# =======================
+# OpenAI API Configuration
+# =======================
+openai.api_key = os.getenv("OPENAI_API_KEY", "your_openai_api_key_here")
+
+# =======================
+# Load Model with PyTorch
+# =======================
+@st.cache_resource
+def load_model():
+    """
+    Load the pre-trained skin cancer classification model using PyTorch.
+    Use the AutoModelForImageClassification and AutoFeatureExtractor for explicit local caching.
+    """
+    try:
+        extractor = AutoFeatureExtractor.from_pretrained("Anwarkh1/Skin_Cancer-Image_Classification")
+        model = AutoModelForImageClassification.from_pretrained("Anwarkh1/Skin_Cancer-Image_Classification")
+        return pipeline("image-classification", model=model, feature_extractor=extractor, framework="pt")
+    except Exception as e:
+        st.error(f"Error loading the model: {e}")
+        return None
+
+model = load_model()
+
+# =======================
+# OpenAI Explanation Function
+# =======================
+def generate_openai_explanation(label, confidence):
+    """
+    Generate a detailed explanation for the classification result using OpenAI's GPT model.
+    """
+    prompt = (
+        f"The AI model has classified an image of a skin lesion as **{label}** with a confidence of **{confidence:.2%}**.\n"
+        f"Explain what this classification means, including potential characteristics of this lesion type, "
+        f"what steps a patient should take next, and how the AI might have arrived at this conclusion. "
+        f"Use language that is easy for a non-medical audience to understand."
+    )
+    try:
+        response = openai.Completion.create(
+            model="text-davinci-003",  # Replace with "gpt-4" if available
+            prompt=prompt,
+            max_tokens=300,
+            temperature=0.7
+        )
+        return response.choices[0].text.strip()
+    except Exception as e:
+        return f"Error generating explanation: {e}"
+
+# =======================
+# Streamlit App Title and Sidebar
+# =======================
+st.title("🔍 AI-Powered Skin Cancer Classification and Explanation")
+st.write("Upload an image of a skin lesion, and the AI model will classify it and provide a detailed explanation.")
+
+st.sidebar.info("""
+**AI Cancer Detection Platform**  
+This application uses AI to classify skin lesions and generate detailed explanations for informational purposes.  
+It is not intended for medical diagnosis. Always consult a healthcare professional for medical advice.
+""")
+
+# =======================
+# File Upload and Prediction
+# =======================
+uploaded_image = st.file_uploader("Upload a skin lesion image (PNG, JPG, JPEG)", type=["png", "jpg", "jpeg"])
+
+if uploaded_image:
+    # Display uploaded image
+    image = Image.open(uploaded_image).convert("RGB")
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    # Perform classification
+    if model is None:
+        st.error("Model could not be loaded. Please try again later.")
+    else:
+        with st.spinner("Classifying the image..."):
+            try:
+                results = model(image)
+                label = results[0]['label']
+                confidence = results[0]['score']
+
+                # Display prediction results
+                st.markdown(f"### Prediction: **{label}**")
+                st.markdown(f"### Confidence: **{confidence:.2%}**")
+
+                # Provide confidence-based insights
+                if confidence >= 0.8:
+                    st.success("High confidence in the prediction.")
+                elif confidence >= 0.5:
+                    st.warning("Moderate confidence in the prediction. Consider additional verification.")
+                else:
+                    st.error("Low confidence in the prediction. Results should be interpreted with caution.")
+
+                # Generate explanation
+                with st.spinner("Generating a detailed explanation..."):
+                    explanation = generate_openai_explanation(label, confidence)
+
+                st.markdown("### Explanation")
+                st.write(explanation)
+
+            except Exception as e:
+                st.error(f"Error during classification: {e}")