Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,150 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import requests
+import pandas as pd
+from PIL import Image
+import io
+from io import BytesIO
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
+def check_image_accessibility(url):
+    try:
+        response = requests.get(url, timeout=5)
+        if response.status_code == 200:
+            try:
+                image = Image.open(BytesIO(response.content))
+                # Verify if image can be displayed by checking its mode and size
+                if image.mode in ['RGB', 'RGBA', 'L'] and image.size[0] > 0 and image.size[1] > 0:
+                    return "Accessible", image
+                else:
+                    return "Not Accessible", None
+            except Exception:
+                return "Not Accessible", None
+        else:
+            return "Not Accessible", None
+    except Exception:
+        return "Not Accessible", None
 
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
+def analyze_image(image_file):
+    # TinEye API endpoint
+    URL = "https://tineye.com/api/v1/result_json/?sort=score&order=desc"
+    
+    # Read image file
+    #image_data = image_file.read()
+    image_data = image_file.getvalue()
+    # Prepare files for API request
+    files = {'image': ("test.png", image_data)}
+    
+    # Make API request
+    response = requests.post(URL, files=files)
+    output = response.json()
+    
+    # Extract data from response
+    urls = []
+    backlinks = []
+    crawl_dates = []
+    image_urls = []
+    image_statuses = []
+    images = []
+    
+    for match in output['matches']:
+        for backlink in match['backlinks']:
+            urls.append(backlink['url'])
+            backlinks.append(backlink['backlink'])
+            crawl_dates.append(backlink['crawl_date'])
+            image_urls.append(match['image_url'])
+            
+            # Check image accessibility
+            status, image = check_image_accessibility(match['image_url'])
+            image_statuses.append(status)
+            images.append(image)
+    
+    # Create DataFrame
+    df = pd.DataFrame({
+        'URL': urls,
+        'Backlink': backlinks,
+        'Crawl Date': crawl_dates,
+        'Image URL': image_urls,
+        'Image Status': image_statuses
+    })
+    
+    # Sort by crawl date
+    df = df.sort_values(by='Crawl Date', ascending=True)
+    
+    return df, output['first_crawl_date'], images
 
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
+def main():
+    st.title("Image Analysis Tool")
+    
+    st.header("Image Analysis")
+    uploaded_file = st.file_uploader("Choose an image file", type=['png', 'jpg', 'jpeg'])
+    
+    if uploaded_file is not None:
+        # Display the uploaded image
+        image = Image.open(uploaded_file)
+        st.image(image, caption='Uploaded Image', use_column_width=True)
+        
+        if st.button("Analyze Image"):
+            with st.spinner("Analyzing image..."):
+                try:
+                    df, first_crawl_date, images = analyze_image(uploaded_file)
+                    
+                    # Display first crawl date
+                    st.subheader("First Found Date")
+                    st.write(f"This image was first found on: {first_crawl_date}")
+                    
+                    # Display matches with images and details
+                    st.subheader("Matched Images")
+                    
+                    for idx, row in df.iterrows():
+                        # Create a container for each match
+                        with st.container():
+                            # Display the matched image if available and accessible
+                            if row['Image Status'] == "Accessible" and images[idx] is not None:
+                                try:
+                                    st.image(images[idx], use_column_width=True)
+                                except Exception:
+                                    # If image display fails, update status
+                                    df.at[idx, 'Image Status'] = "Not Accessible"
+                                    st.warning("Image not accessible")
+                            else:
+                                st.warning("Image not accessible")
+                            
+                            # Display details in a clean format
+                            st.markdown("---")
+                            st.markdown(f"**Match {idx + 1} Details:**")
+                            st.markdown(f"**URL:** {row['URL']}")
+                            st.markdown(f"**Backlink:** {row['Backlink']}")
+                            st.markdown(f"**Crawl Date:** {row['Crawl Date']}")
+                            st.markdown(f"**Image Status:** {row['Image Status']}")
+                            st.markdown("---")
+                    
+                    # Add a separator before the DataFrame
+                    st.markdown("---")
+                    st.markdown("### Complete Results Table")
+                    
+                    # Display the DataFrame with clickable links
+                    st.dataframe(
+                        df,
+                        use_container_width=True,
+                        hide_index=True,
+                        column_config={
+                            "URL": st.column_config.LinkColumn("URL"),
+                            "Backlink": st.column_config.LinkColumn("Backlink"),
+                            "Crawl Date": st.column_config.DateColumn("Crawl Date"),
+                            "Image URL": st.column_config.LinkColumn("Image URL"),
+                            "Image Status": st.column_config.TextColumn("Image Status")
+                        }
+                    )
+                    
+                    # Download button for results
+                    csv = df.to_csv(index=False)
+                    st.download_button(
+                        label="Download Results as CSV",
+                        data=csv,
+                        file_name="image_analysis_results.csv",
+                        mime="text/csv"
+                    )
+                except Exception as e:
+                    st.error(f"An error occurred: {str(e)}")
 
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+if __name__ == "__main__":
+    main()