update

app.py

@@ -1,11 +1,14 @@
-from pathlib import Path
-
 import streamlit as st
-from googlesearch import search
-import pandas as pd
 import os
+from pathlib import Path
+from PIL import Image
 from rag_sec.document_search_system import DocumentSearchSystem
 from chainguard.blockchain_logger import BlockchainLogger
+import requests
+import pandas as pd
+
+# SerpAPI Key (Replace with your SerpAPI key)
+SERPAPI_KEY = "your_serpapi_api_key"
 
 # Blockchain Logger
 blockchain_logger = BlockchainLogger()
@@ -14,6 +17,7 @@ blockchain_logger = BlockchainLogger()
 @st.cache_resource
 def initialize_system():
     """Initialize the DocumentSearchSystem and load documents."""
+    home_dir = Path(os.getenv("HOME", "/"))
     system = DocumentSearchSystem(
         neo4j_uri="neo4j+s://0ca71b10.databases.neo4j.io",
         neo4j_user="neo4j",
@@ -25,56 +29,116 @@ def initialize_system():
 # Initialize the system
 system = initialize_system()
 
+# Function to Fetch News from SerpAPI
+def fetch_news(query, num_results=5):
+    """Fetch search results using SerpAPI."""
+    url = "https://serpapi.com/search"
+    params = {
+        "engine": "google",
+        "q": query,
+        "api_key": SERPAPI_KEY,
+        "num": num_results
+    }
+    try:
+        response = requests.get(url, params=params)
+        response.raise_for_status()
+        search_results = response.json().get("organic_results", [])
+        return [{"title": result["title"], "link": result["link"]} for result in search_results]
+    except Exception as e:
+        return [{"error": f"An error occurred: {str(e)}"}]
+
+# Mock User Information (Replace with actual Google Login integration if needed)
+def get_user_info():
+    """Fetch or mock user details."""
+    return {"name": "Talex Maxim", "email": "taimax13@gmail.com"}  # Replace with dynamic user info
+
+# Directory for storing uploaded files
+UPLOAD_DIR = "uploaded_files"
+os.makedirs(UPLOAD_DIR, exist_ok=True)
+
 # Streamlit Layout
-st.title("Memora: Advanced Search and News Insights")
-st.subheader("Personalized news and global updates at your fingertips")
+st.title("Memora: Advanced File Upload and News Insights")
+st.subheader("Securely upload, organize, and query your files while staying informed.")
+
+# User-Specific Information
+user_info = get_user_info()
+if user_info:
+    st.sidebar.write("### Logged in as:")
+    st.sidebar.write(f"**Name:** {user_info['name']}")
+    st.sidebar.write(f"**Email:** {user_info['email']}")
+else:
+    st.sidebar.write("### Not Logged In")
+    st.sidebar.write("We invite you on the journey! Please log in with your Google account.")
 
 # Google Search: User-Specific News
-st.subheader("1. Latest News About You")
-user_name = st.text_input("Enter your name or handle to search for recent news", value="Talex Maxim")
-
-if st.button("Search News About Me"):
-    if user_name:
-        st.write(f"Searching Google for news about **{user_name}**...")
-        try:
-            results = list(search(user_name, num_results=5))
-            if results:
-                st.success(f"Top {len(results)} results for '{user_name}':")
-                user_news_data = {"URL": results}
-                df_user_news = pd.DataFrame(user_news_data)
-                st.dataframe(df_user_news)
-            else:
-                st.warning("No recent news found about you.")
-        except Exception as e:
-            st.error(f"An error occurred during the search: {str(e)}")
+if user_info:
+    st.subheader("1. Latest News About You")
+    user_name = user_info["name"]
+    st.write(f"Fetching latest news for **{user_name}**...")
+    user_news = fetch_news(user_name, num_results=5)
+
+    if user_news and "error" not in user_news[0]:
+        st.success(f"Top {len(user_news)} results for '{user_name}':")
+        user_news_df = pd.DataFrame(user_news)
+        st.dataframe(user_news_df)
     else:
-        st.warning("Please enter your name or handle to search.")
+        st.error(user_news[0].get("error", "No news found."))
+else:
+    st.warning("Please log in with your Google account to fetch personalized news.")
 
 # Google Search: Global News Categories
 st.subheader("2. Global News Insights")
 categories = ["Technology", "Sports", "Politics", "Entertainment", "Science"]
-news_results = {}
 
-if st.button("Fetch Global News"):
-    try:
-        for category in categories:
-            st.write(f"Fetching news for **{category}**...")
-            try:
-                category_results = list(search(f"latest {category} news", num_results=3))
-                news_results[category] = category_results
-            except Exception as e:
-                news_results[category] = [f"Error fetching news: {str(e)}"]
-
-        # Display results
-        for category, articles in news_results.items():
-            st.write(f"### Top News in {category}:")
-            for idx, article in enumerate(articles, start=1):
-                st.write(f"{idx}. [Read here]({article})")
-    except Exception as e:
-        st.error(f"An error occurred while fetching global news: {str(e)}")
+for category in categories:
+    st.write(f"Fetching news for **{category}**...")
+    category_results = fetch_news(f"latest {category} news", num_results=3)
+    if category_results and "error" not in category_results[0]:
+        st.success(f"Top {len(category_results)} results for '{category}':")
+        for idx, result in enumerate(category_results, start=1):
+            st.write(f"{idx}. [{result['title']}]({result['link']})")
+    else:
+        st.error(category_results[0].get("error", "No news found."))
+
+# File Upload Section
+st.subheader("3. Upload and Organize Files")
+
+uploaded_files = st.file_uploader("Upload your files", accept_multiple_files=True, type=['jpg', 'jpeg', 'png', 'mp4', 'avi'])
+
+if uploaded_files:
+    for uploaded_file in uploaded_files:
+        # Save file locally
+        file_path = os.path.join(UPLOAD_DIR, uploaded_file.name)
+        with open(file_path, "wb") as f:
+            f.write(uploaded_file.getbuffer())
+        st.success(f"File saved locally: {file_path}")
+
+        # Display file details
+        if uploaded_file.type.startswith('image'):
+            image = Image.open(uploaded_file)
+            st.image(image, caption=uploaded_file.name, use_column_width=True)
+
+        # Metadata Input
+        album = st.text_input(f"Album for {uploaded_file.name}", "Default Album")
+        tags = st.text_input(f"Tags for {uploaded_file.name} (comma-separated)", "")
+
+        # Log Metadata
+        if st.button(f"Log Metadata for {uploaded_file.name}"):
+            metadata = {"file_name": uploaded_file.name, "tags": tags.split(','), "album": album}
+            blockchain_details = blockchain_logger.log_data(metadata)
+            blockchain_hash = blockchain_details.get("block_hash", "N/A")
+
+            # Use Neo4jHandler from DocumentSearchSystem to log transaction
+            system.neo4j_handler.log_relationships(uploaded_file.name, album, blockchain_hash, [])
+            st.write(f"Metadata logged successfully! Blockchain Details: {blockchain_details}")
+
+# Blockchain Integrity Validation
+if st.button("Validate Blockchain Integrity"):
+    is_valid = blockchain_logger.is_blockchain_valid()
+    st.write("Blockchain Integrity:", "Valid ✅" if is_valid else "Invalid ❌")
 
-# Document Search
-st.subheader("3. Search Documents")
+# Query System
+st.subheader("4. Search Documents")
 query = st.text_input("Enter your query (e.g., 'sports news', 'machine learning')")
 
 if st.button("Search Documents"):