sxs

app.py

@@ -21,118 +21,7 @@ from sklearn.preprocessing import StandardScaler  # Standardization of image dat
 
 # Load Gemini API key from Streamlit Secrets configuration
 api_key = st.secrets["gemini"]["api_key"]  # Get API key from Streamlit secrets
-genai.configure(api_key=api_key)  # Configure the Gemini API with the API keyimport streamlit as st
-import numpy as np
-import google.generativeai as genai
-import matplotlib.pyplot as plt
-
-from sklearn.ensemble import RandomForestClassifier   
-from sklearn.linear_model import LogisticRegression   
-from skimage.filters import sobel  
-from skimage.segmentation import watershed  
-from skimage.feature import canny, hog  
-from skimage.color import rgb2gray  
-from skimage import io  
-from sklearn.preprocessing import StandardScaler  
-from sklearn.metrics import accuracy_score
-
-# Load Gemini API key
-api_key = st.secrets["gemini"]["api_key"]
-genai.configure(api_key=api_key)
-MODEL_ID = "gemini-1.5-flash"
-gen_model = genai.GenerativeModel(MODEL_ID)
-
-def explain_ai(prompt):
-    try:
-        response = gen_model.generate_content(prompt)
-        return response.text
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-
-# Sidebar navigation
-st.sidebar.title("Navigation")
-page = st.sidebar.radio("Go to", ["Home", "Edge Detection", "Segmentation", "Feature Extraction", "AI Classification"])
-
-# Home Page
-if page == "Home":
-    uploaded_file = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
-    if uploaded_file is not None:
-        image = io.imread(uploaded_file)
-        if image.shape[-1] == 4:
-            image = image[:, :, :3]
-        gray = rgb2gray(image)
-        st.image(image, caption="Uploaded Image", use_container_width=True)
-        st.session_state["gray"] = gray  # Store for use in other pages
-
-# Edge Detection Page
-elif page == "Edge Detection":
-    st.title("Edge Detection")
-    gray = st.session_state.get("gray")
-    if gray is not None:
-        edge_method = st.selectbox("Select Edge Detection Method", ["Canny", "Sobel"])
-        edges = canny(gray) if edge_method == "Canny" else sobel(gray)
-        st.image(edges, caption=f"{edge_method} Edge Detection", use_container_width=True)
-        st.text_area("Explanation", explain_ai(f"Explain how {edge_method} edge detection works in computer vision."), height=300)
-    else:
-        st.warning("Please upload an image on the Home page.")
-
-# Segmentation Page
-elif page == "Segmentation":
-    st.title("Image Segmentation")
-    gray = st.session_state.get("gray")
-    if gray is not None:
-        seg_method = st.selectbox("Select Segmentation Method", ["Watershed", "Thresholding"])
-        if seg_method == "Watershed":
-            elevation_map = sobel(gray)
-            markers = np.zeros_like(gray)
-            markers[gray < 0.3] = 1
-            markers[gray > 0.7] = 2
-            segmented = watershed(elevation_map, markers.astype(np.int32))
-        else:
-            threshold_value = st.slider("Choose threshold value", 0, 255, 127)
-            segmented = (gray > (threshold_value / 255)).astype(np.uint8) * 255
-        st.image(segmented, caption=f"{seg_method} Segmentation", use_container_width=True)
-        st.text_area("Explanation", explain_ai(f"Explain how {seg_method} segmentation works in image processing."), height=300)
-    else:
-        st.warning("Please upload an image on the Home page.")
-
-# Feature Extraction Page
-elif page == "Feature Extraction":
-    st.title("HOG Feature Extraction")
-    gray = st.session_state.get("gray")
-    if gray is not None:
-        fd, hog_image = hog(gray, pixels_per_cell=(8, 8), cells_per_block=(2, 2), visualize=True)
-        st.image(hog_image, caption="HOG Features", use_container_width=True)
-        st.text_area("Explanation", explain_ai("Explain how Histogram of Oriented Gradients (HOG) feature extraction works."), height=300)
-    else:
-        st.warning("Please upload an image on the Home page.")
-
-# AI Classification Page
-elif page == "AI Classification":
-    st.title("AI Classification")
-    gray = st.session_state.get("gray")
-    if gray is not None:
-        model_choice = st.selectbox("Select AI Model", ["Random Forest", "Logistic Regression"])
-        flat_image = gray.flatten().reshape(-1, 1)
-        labels = (flat_image > 0.5).astype(int).flatten()
-        ai_model = RandomForestClassifier(n_jobs=1) if model_choice == "Random Forest" else LogisticRegression()
-        scaler = StandardScaler()
-        flat_image_scaled = scaler.fit_transform(flat_image)
-        ai_model.fit(flat_image_scaled, labels)
-        predictions = ai_model.predict(flat_image_scaled).reshape(gray.shape)
-        predictions = (predictions * 255).astype(np.uint8)
-        accuracy = accuracy_score(labels, ai_model.predict(flat_image_scaled))
-        st.image(predictions, caption=f"{model_choice} Pixel Classification", use_container_width=True)
-        st.text_area("Explanation", explain_ai(f"Explain how {model_choice} is used for image classification."), height=300)
-        st.write(f"### Accuracy: {accuracy:.2f}")
-        fig, ax = plt.subplots()
-        ax.bar(["Accuracy"], [accuracy], color='blue')
-        ax.set_ylim([0, 1])
-        st.pyplot(fig)
-    else:
-        st.warning("Please upload an image on the Home page.")
-
+genai.configure(api_key=api_key)  # Configure the Gemini API with the API key
 
 MODEL_ID = "gemini-1.5-flash"  # Specify the model ID for Gemini
 gen_model = genai.GenerativeModel(MODEL_ID)  # Initialize the Gemini model
@@ -149,8 +38,8 @@ def explain_ai(prompt):
 # App title
 st.title("Imaize: Smart Image Analyzer with XAI")
 
-# # Image upload section
-# uploaded_file = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])  # Allow user to upload an image file
+# Image upload section
+uploaded_file = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])  # Allow user to upload an image file
 
 # App Description 
 st.markdown("""