Update src/streamlit_app.py

src/streamlit_app.py

@@ -6,6 +6,18 @@ import datetime
 # --- PAGE CONFIG ---
 st.set_page_config(page_title="SaaS Media Vault", layout="wide")
 
+# Custom CSS to prevent layout shifting and "shaking"
+st.markdown("""
+    <style>
+    .stColumn {
+        transition: none !important;
+    }
+    div[data-testid="stVerticalBlock"] > div {
+        animation: none !important;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
 st.title("🗄️ SaaS Media Intelligence Vault")
 st.markdown("Analyze, store, and **search** your media assets using AI-generated metadata.")
 
@@ -13,18 +25,18 @@ st.markdown("Analyze, store, and **search** your media assets using AI-generated
 if "media_library" not in st.session_state:
     st.session_state.media_library = []
 
-# --- MODEL LOADING (Option 1: Auto-Detect Task) ---
+# --- MODEL LOADING ---
 @st.cache_resource
 def load_models():
-    # CLIP for matching labels
+    # CLIP for general categorization
     classifier = pipeline("zero-shot-image-classification", 
                           model="openai/clip-vit-base-patch32", 
                           device=-1)
     
-    # Use the specific task name from your error message
+    # BLIP for natural language description
     captioner = pipeline("image-text-to-text", 
-                         model="Salesforce/blip-image-captioning-base", 
-                         device=-1)
+                          model="Salesforce/blip-image-captioning-base", 
+                          device=-1)
     
     return classifier, captioner
 
@@ -32,44 +44,44 @@ classifier, captioner = load_models()
 
 # --- SIDEBAR: UPLOAD & SETTINGS ---
 st.sidebar.header("📥 Asset Management")
-labels_input = st.sidebar.text_input("Analysis Keywords", "professional, tech, lifestyle, nature")
 uploaded_file = st.sidebar.file_uploader("Add New Image", type=["jpg", "png", "jpeg"])
 
 if uploaded_file:
     if st.sidebar.button("Process & Index Asset"):
         image = Image.open(uploaded_file)
         
-        with st.spinner("AI is thinking..."):
+        with st.spinner("AI is indexing..."):
             # 1. BLIP Description
             prompt = "a photo of"
             caption_out = captioner(image, text=prompt, max_new_tokens=30)
-            description = caption_out[0]['generated_text']
+            description = caption_out[0]['generated_text'].replace("a photo of ", "")
             
-            # 2. CLIP Top Label
-            labels = [l.strip() for l in labels_input.split(",")]
-            clip_out = classifier(image, candidate_labels=labels)
+            # 2. Internal Auto-Tagging (Replaces the manual keywords input)
+            # We use a broad set of categories to give the AI context without user input
+            auto_labels = ["object", "person", "place", "nature", "technology", "document"]
+            clip_out = classifier(image, candidate_labels=auto_labels)
             top_label = clip_out[0]['label']
             top_score = clip_out[0]['score']
 
             # 3. SAVE TO ARRAY
             asset_data = {
-                "id": len(st.session_state.media_library),
+                "id": f"{datetime.datetime.now().timestamp()}", # Unique ID to prevent UI jitter
                 "timestamp": datetime.datetime.now().strftime("%H:%M:%S"),
                 "image": image,
                 "description": description.lower(),
                 "tag": top_label.lower(),
                 "confidence": top_score
             }
+            # Insert at the top
             st.session_state.media_library.insert(0, asset_data)
         st.sidebar.success("Asset Cataloged!")
 
 # --- MAIN SECTION: SEARCH & RETRIEVAL ---
 st.subheader("🔍 Intelligent Retrieval")
-search_query = st.text_input("Search the vault by keyword (e.g., 'dog', 'tech', 'landscape')", "").lower()
+search_query = st.text_input("Search the vault (e.g., 'flowers', 'tech', 'laptop')", "").lower()
 
 # --- FILTER LOGIC ---
 if search_query:
-    # We check if the search term is in the AI description or the chosen Tag
     filtered_items = [
         item for item in st.session_state.media_library 
         if search_query in item["description"] or search_query in item["tag"]
@@ -78,11 +90,12 @@ else:
     filtered_items = st.session_state.media_library
 
 # --- DISPLAY VAULT ---
-st.write(f"Showing {len(filtered_items)} of {len(st.session_state.media_library)} assets")
+st.write(f"Showing **{len(filtered_items)}** assets")
 
 if not filtered_items:
     st.info("No matching assets found in the vault.")
 else:
+    # Using a container with a fixed key helps Streamlit manage the DOM state better
     for item in filtered_items:
         with st.container():
             col1, col2 = st.columns([1, 3])
@@ -91,7 +104,7 @@ else:
             with col2:
                 st.write(f"**🕒 Logged:** {item['timestamp']}")
                 st.info(f"**AI Description:** {item['description'].capitalize()}")
-                st.write(f"**🏷️ Primary Tag:** `{item['tag']}` ({round(item['confidence']*100, 1)}%)")
+                st.write(f"**🏷️ Type:** `{item['tag']}` ({round(item['confidence']*100, 1)}%)")
             st.divider()
 
 # --- CLEAR UTILITY ---