Update app.py

app.py

@@ -1,15 +1,18 @@
 import streamlit as st
 import json
+import uuid
+from datetime import datetime
 from rapidfuzz import process, fuzz
+from huggingface_hub import HfApi
+import os
 
 # --- CONFIGURATION ---
 DATA_FILE = "navy_acronyms_clean.json"
+SUGGESTIONS_REPO = "NavyDevilDoc/navy-acronym-suggestions" 
 
 # --- PAGE SETUP ---
 st.set_page_config(page_title="Navy Acronym Finder", page_icon="⚓")
-
 st.title("EDQP Acronym Lookup")
-st.markdown("Type an acronym below to find its definition. Uses fuzzy matching to handle typos.")
 
 # --- LOAD DATA ---
 @st.cache_data
@@ -18,52 +21,69 @@ def load_acronyms():
         with open(DATA_FILE, 'r', encoding='utf-8') as f:
             return json.load(f)
     except FileNotFoundError:
-        return {"ERROR": "navy_acronyms_clean.json not found!"}
+        return {}
 
 acronym_dict = load_acronyms()
 acronym_keys = list(acronym_dict.keys())
 
 # --- SEARCH BAR ---
-query = st.text_input("Enter Acronym:", placeholder="e.g., ACAT, MDA, ASW...", max_chars=10)
+query = st.text_input("Enter Acronym:", placeholder="e.g., ACAT, C4ISR...", max_chars=10)
 
-# --- RESULTS ---
 if query:
     query = query.upper().strip()
-    
-    # 1. Exact Match (The "Right" Answer)
     if query in acronym_dict:
         st.success(f"**{query}**")
         st.markdown(f"### {acronym_dict[query]}")
         st.divider()
 
-    # 2. Fuzzy / Top 5 Suggestions
     st.markdown("#### Did you mean?")
-    
-    # Extracts top 5 similar keys based on the user query
-    # scorer=fuzz.ratio gives a simple similarity score
     matches = process.extract(query, acronym_keys, limit=5, scorer=fuzz.ratio)
-    
-    for match_tuple in matches:
-        # Tuple format: (matched_string, score, index)
-        candidate = match_tuple[0]
-        score = match_tuple[1]
-        
-        # Don't show the exact match again if we already showed the big green box
-        if candidate == query:
-            continue
-            
-        # Only show relevant matches (score > 40)
-        if score > 40:
-            with st.expander(f"**{candidate}** (Match: {int(score)}%)"):
+    for match in matches:
+        candidate, score = match[0], match[1]
+        if candidate != query and score > 40:
+            with st.expander(f"**{candidate}** ({int(score)}%)"):
                 st.write(acronym_dict[candidate])
 
-# --- SIDEBAR (Database Stats) ---
-with st.sidebar:
-    st.header("Database Info")
-    st.metric("Total Acronyms", len(acronym_dict))
-    
-    st.divider()
+# --- SUGGESTION BOX (The New Feature) ---
+st.divider()
+with st.expander("📝 Suggest an Acronym / Report an Error"):
+    st.write("Don't see an acronym? Let me know and I'll add it to the database.")
     
-    # Option to view raw list
-    if st.checkbox("Show Full List"):
-        st.dataframe(acronym_dict, use_container_width=True)
+    with st.form("suggestion_form"):
+        s_acr = st.text_input("Acronym")
+        s_def = st.text_area("Definition / Context")
+        submitted = st.form_submit_button("Submit Suggestion")
+        
+        if submitted and s_acr and s_def:
+            try:
+                # 1. Prepare the Payload
+                payload = {
+                    "acronym": s_acr.upper(),
+                    "definition": s_def,
+                    "timestamp": datetime.utcnow().isoformat()
+                }
+                
+                # 2. Create a unique filename (so suggestions never overwrite each other)
+                # Example: suggestion_2026-01-11_uuid.json
+                file_name = f"suggestion_{datetime.now().strftime('%Y%m%d_%H%M%S')}_{uuid.uuid4().hex[:6]}.json"
+                
+                # 3. Create a temporary local file
+                with open(file_name, "w") as f:
+                    json.dump(payload, f)
+                
+                # 4. Upload to the "Mailbox" Dataset
+                api = HfApi(token=os.getenv("HF_TOKEN"))
+                api.upload_file(
+                    path_or_fileobj=file_name,
+                    path_in_repo=file_name,
+                    repo_id=SUGGESTIONS_REPO,
+                    repo_type="dataset"
+                )
+                
+                st.success("✅ Suggestion sent! I'll review it shortly.")
+                
+                # Cleanup local temp file
+                os.remove(file_name)
+                
+            except Exception as e:
+                st.error(f"Error sending suggestion: {e}")