Add Reranker and Optimized Embeddings and metadata by connecting everything with postgres

pyproject.toml

@@ -5,7 +5,7 @@ description = "Add your description here"
 readme = "README.md"
 requires-python = ">=3.11,<3.14"
 dependencies = [
-    "chromadb>=0.5.0",
+    "chromadb>=1.5.2",
     "fastapi[all]>=0.122.0",
     "gradio>=6.2.0",
     "langchain>=1.1.3",

src/api/main.py

@@ -62,7 +62,9 @@ async def get_recommendations(request: RecommendationRequest, fastapi_req: Reque
     {
         "query": "Anime similar to Death Note but lighter",
         "n_results": 5,
-        "min_score": 7.5
+        "min_score": 7.5,
+        "genre_filter": ["Comedy", "Fantasy"],
+        "anime_type": TV
     }
     ```
     """
@@ -77,6 +79,9 @@ async def get_recommendations(request: RecommendationRequest, fastapi_req: Reque
 
         if request.genre_filter:
             filters["genre_filter"] = request.genre_filter
+        
+        if request.anime_type:
+            filters["anime_type"] = request.anime_type
 
         start_time = time.time()
         result = rag_pipeline.recommend(
@@ -86,12 +91,13 @@ async def get_recommendations(request: RecommendationRequest, fastapi_req: Reque
         end_time = time.time()
 
         # print(f"Retrieved anime : \n{result["retrieved_animes"][0]}")
-        print(f"Retrieved anime Count : \n{result["retrieved_count"]}")
+        print(f"Retrieved anime Count : \n{result["reranked_count"]}")
         print(f"Result Recommendations: \n{result["recommendations"][:20]}")
         return RecommendationResponse(
             query=result["query"],
             recommendations=result["recommendations"],
-            retrieved_count=result["retrieved_count"],
+            retrieved_count=result["reranked_count"],
+            retrieved_animes=result["retrieved_animes"],
             metadata={
                 "model": settings.model_name,
                 "retriever_k": rag_pipeline.retriever_k,

src/api/schemas.py

@@ -7,11 +7,13 @@ class RecommendationRequest(BaseModel):
         5, description="Number of recommendations to return")
     min_score: float | None = Field(
         None, description="Minimum MyAnimeList Score filter")
-    genre_filter: str | None = Field(None, description="Filter by genre")
+    genre_filter: list[str] | None = Field(None, description="Filter by genre")
+    anime_type: str | None = Field(None, description="Filter by type(TV, Movie, etc)")
 
 
 class RecommendationResponse(BaseModel):
     query: str
     recommendations: str
     retrieved_count: int
+    retrieved_animes: list[dict]
     metadata: dict = {}

src/data_ingestion/create_embeddings.py

@@ -1,11 +1,15 @@
-import pandas as pd
 from sentence_transformers import SentenceTransformer
 import chromadb
 from chromadb.config import Settings
-import os
+from sqlmodel import Session, select
 import logging
 
+from src.database.session import engine
+from src.database.models import Animes
+
 logger = logging.getLogger(__name__)
+logging.basicConfig(level=logging.INFO,
+                    format='%(asctime)s - %(levelname)s - %(message)s')
 
 
 class EmbeddingPipeline:
@@ -50,6 +54,14 @@ class EmbeddingPipeline:
 
         return collection
 
+    def fetch_data_from_postgres(self):
+        """Fetch all anime records from PostgreSQL"""
+        logger.info("Fetching data from PostgreSQL...")
+        with Session(engine) as session:
+            statement = select(Animes).where(Animes.searchable_text != None)
+            results = session.exec(statement).all()
+            return results
+
     def embed_texts(self, texts: list[str], batch_size: int = 32) -> list[list[float]] | None:
         """
         Create embeddings for texts
@@ -74,62 +86,73 @@ class EmbeddingPipeline:
         else:
             logger.info(f"Using existing stored embeddings.")
 
-    def store_in_chromadb(self, collection, df: pd.DataFrame, embeddings: list[list[float]]):
+    def store_in_chromadb(self, collection, db_records: list[Animes], embeddings: list[list[float]]):
         """
         Store embeddings and metadata in ChromaDB
 
         Args:
             collection: ChromaDB collection,
-            df: DataFrame with anime data,
+            db_records: List of Anime data retrieved from PostgreSQL database,
             embeddings: Pre_commputed embeddings
         """
 
         logger.info("Storing in ChromaDB...")
 
-        ids = [str(mal_id) for mal_id in df["mal_id"].tolist()]
-        documents = df["searchable_text"].tolist()
-
-        # Metadata
+        ids = []
+        documents = []
         metadatas = []
-        for _, row in df.iterrows():
-            metadata = {
-                "title": row["title"],
-                "genres": row["genres"],
-                "score": float(row["score"]) if pd.notna(row["score"]) else 0.0,
-                "type": row["type"] if pd.notna(row["type"]) else "Unknown",
-                "year": int(row["year"]) if pd.notna(row["year"]) else 0,
-                "synopsis": row["synopsis"][:500],
-                "rating": row["rating"] if pd.notna(row["rating"]) else "Rating Unspecified",
-                "scored_by": row['scored_by'],
-            }
-            metadatas.append(metadata)
-
-        collection.add(
-            ids=ids,
-            embeddings=embeddings,
-            documents=documents,
-            metadatas=metadatas
-        )
-
-        logger.info(f"Stored {len(ids)} animes in ChromaDB")
-        logger.info(f"Collection now has {collection.count()} documents")
-
-    def run_pipeline(self, csv_path: str = 'data/processed/anime_clean.csv'):
+
+        for row in db_records:
+            ids.append(str(row.mal_id))
+            documents.append(row.searchable_text)
+
+            # genres_str = ", ".join(row.genres) if isinstance(
+            #     row.genres, list) else str(row.genres or "")
+            genres_list = row.genres if isinstance(row.genres, list) else []
+            if len(genres_list) == 0:
+                genres_list = ["Unknown"]
+
+            metadatas.append({
+                "title": row.title,
+                "genres": genres_list,
+                "score": float(row.score) if row.score else 0.0,
+                "type": row.type if row.type else "Unknown",
+                "scored_by": row.scored_by if row.scored_by else 0
+            })
+            print(f"Genre List saved: {", ".join(genres_list)}")
+
+        chunk_size = 500
+        total_chunks = (len(ids) // chunk_size) + 1
+        logger.info(f"Inserting into ChromaDB in {total_chunks} batches...")
+
+        for i in range(0, len(ids), chunk_size):
+            collection.add(
+                ids=ids[i: i + chunk_size],
+                embeddings=embeddings[i: i + chunk_size],
+                documents=documents[i: i + chunk_size],
+                metadatas=metadatas[i: i + chunk_size]
+            )
+            logger.info(f"Inserted batch {(i//chunk_size)+1}/{total_chunks}")
+
+        logger.info(f"✅ Successfully stored {len(ids)} animes in ChromaDB")
+
+    def run_pipeline(self):
         """Run complete embedding pipeline"""
 
-        logger.info("Loading processed data...")
-        df = pd.read_csv(csv_path)
-        logger.info(f"Loaded {len(df)} animes")
+        # 1. Fetch from DB instead of CSV
+        db_records = self.fetch_data_from_postgres()
+        logger.info(f"Loaded {len(db_records)} animes from Postgres")
 
         collection = self.create_or_get_collection()
 
-        if self.use_existing_embeddings == False:
-            embeddings = self.embed_texts(df["searchable_text"].tolist())
-            if embeddings:
-                self.store_in_chromadb(collection, df, embeddings)
+        if not self.use_existing_embeddings:
+            texts_to_embed = [record.searchable_text for record in db_records]
+            embeddings = self.embed_texts(texts_to_embed)
 
-        logger.info("Embedding pipeline complete !")
+            if embeddings:
+                self.store_in_chromadb(collection, db_records, embeddings)
 
+        logger.info("Embedding pipeline complete!")
         return collection
 
 
@@ -138,13 +161,13 @@ if __name__ == "__main__":
     collection = pipeline.run_pipeline()
 
     print("\n--- Testing vector search ---")
-    query = "Anime with female main character on adventure"
+    query = "Anime similar to Attack Titan"
 
     print(f"Query: {query}")
 
     results = collection.query(query_texts=[query], n_results=15)
 
-    print("\n--- TOP 5 RESULTS ---")
+    print("\n--- TOP 15 RESULTS ---")
 
     for i, (title, distance) in enumerate(zip(
         [m["title"] for m in results["metadatas"][0]],

src/llm/anime_reranker.py

@@ -0,0 +1,114 @@
+import pandas as pd
+from sentence_transformers import CrossEncoder
+from sklearn.preprocessing import MinMaxScaler
+
+
+class AnimeReranker:
+    def __init__(self, model_name: str = 'cross-encoder/ms-marco-MiniLM-L-6-v2', device: str = 'cpu'):
+        """
+        Initializes the reranker, loading the PyTorch model into memory once.
+        """
+        print(f"Loading CrossEncoder model '{model_name}' on {device}...")
+        self.reranker = CrossEncoder(model_name, device=device)
+        self.scaler = MinMaxScaler()
+
+        # ----Postgres Global Truth
+        self.C = 7.676           # The true weighted average score
+        # 50th percentile of votes (Confidence threshold)
+        self.bayesian_m = 10875
+
+        # Hyperparameters for scoring
+        self.passion_weight = 50     # Scaling factor for favorites/scored_by
+        self.alpha = 0.75            # Weight for Semantic Relevance
+        self.beta = 0.25             # Weight for Objective Quality
+
+    def _calculate_quality_score(self, df: pd.DataFrame) -> pd.DataFrame:
+        """Internal method to calculate the objective quality of the anime."""
+        # Calculate Bayesian Average
+        c = self.C
+        v = df['scored_by']
+        r = df['score']
+        df['bayesian_score'] = (v / (v + self.bayesian_m)) * \
+            r + (self.bayesian_m / (v + self.bayesian_m)) * c
+
+        # Calculate Passion Rate
+        df['passion_rate'] = df['favorites'] / (df['scored_by'] + 1)
+
+        # Combine into raw quality score
+        df['raw_quality_score'] = df['bayesian_score'] + \
+            (df['passion_rate'] * self.passion_weight)
+        return df
+
+    # def process(self, user_query: str, retrieved_anime: list[dict], top_k: int = 25) -> tuple[str, pd.DataFrame]:
+    def process(self, user_query: str, retrieved_anime: list[dict], top_k: int = 25) -> pd.DataFrame:
+        """
+        Public method to rerank, filter, and format the final context for the LLM.
+        """
+        if not retrieved_anime:
+            return pd.DataFrame()
+
+        df = pd.DataFrame(retrieved_anime)
+
+        # 1. Semantic Score (Cross-Encoder)
+        cross_input = [[user_query, text] for text in df['searchable_text']]
+        df['semantic_score'] = self.reranker.predict(cross_input)
+
+        # 2. Quality Score (Custom Math)
+        df = self._calculate_quality_score(df)
+
+        # 3. Normalize & Combine
+        df[['norm_semantic', 'norm_quality']] = self.scaler.fit_transform(
+            df[['semantic_score', 'raw_quality_score']])
+        df['final_hybrid_score'] = (
+            self.alpha * df['norm_semantic']) + (self.beta * df['norm_quality'])
+
+        # 4. Sort and Filter
+        df_sorted = df.sort_values(
+            by='final_hybrid_score', ascending=False).head(top_k)
+
+        # # 5. Format for LLM
+        # llm_context = "Here are the top retrieved anime matches:\n\n"
+        # for _, row in df_sorted.iterrows():
+        #     llm_context += f"Title: {row['title']} (English: {row.get('title_english', 'N/A')})\n"
+        #     llm_context += f"Genres: {row.get('genres', 'N/A')}\n"
+        #     llm_context += f"Synopsis: {row.get('synopsis', 'N/A')}\n"
+        #     llm_context += "-" * 40 + "\n"
+
+        # return llm_context, df_sorted
+        return df_sorted
+
+
+# ==========================================
+# USAGE IN PRODUCTION
+# ==========================================
+# 1. Initialize ONCE during app startup
+if __name__ == "__main__":
+    from src.retrieval.vector_search import AnimeRetriever
+    reranker_service = AnimeReranker()
+    retriever = AnimeRetriever()
+
+    user_query = "Modern Romantic Comedy Anime"
+    vector_db_results = retriever.search(query=user_query,
+                                         n_results=50)
+
+    print("BEFORE RERANKING")
+    print("--------------------------------------------------")
+    for res in vector_db_results:
+        print(
+            f"Title: {res["title"]} (Score: {res["score"]}, Scored by: {res["scored_by"]}) .... Favorites={res["favorites"]}")
+
+    # 2. Call the `.process()` method during runtime (e.g., inside an API endpoint)
+    df = reranker_service.process(user_query,
+                                  vector_db_results,
+                                  top_k=10)
+    df_dict_list = df.to_dict(orient="records") if not df.empty else []
+    print("\nAFTER RERANKING")
+    print("--------------------------------------------------")
+    for res in df_dict_list:
+        print(
+            f"Title: {res["title"]} (Score: {res["score"]}, Scored by: {res["scored_by"]}) Favorites={res["favorites"]}")
+        print(f"-> Has Raw Quality Score: {res["raw_quality_score"]}")
+        print(f"-> Has Final normalized Quality Score: {res["norm_quality"]}")
+        print(f"-> Has Raw Semantic Score: {res["semantic_score"]}")
+        print(f"-> Has Final Semantic Score: {res["norm_semantic"]}")
+        print(f"-> Has Final Hybrid Score: {res["final_hybrid_score"]}\n")

src/llm/prompts.py

@@ -35,54 +35,57 @@ You are a strict Gatekeeper for an Anime Recommendation System. Your ONLY job is
 If you are less than 90% sure what the user wants, DO NOT call the tool. Ask for clarification instead.
 """
 
+
 def create_recommendation_prompt(
         user_query: str,
         retrieved_animes: list,
         n_recommendations: int | None = 5
 ):
     """
-    Create prompt for LLM to reason about recommendations
-
-    Args:
-        user_query: Original user question,
-        retrieved_animes: List of dict from vector search,
-        n_recommendations: How many to recommend
+    Create prompt for LLM to reason about recommendations with strict anti-hallucination guardrails.
     """
 
     context_parts = []
-    for i, anime in enumerate(retrieved_animes, 1):
-        context_parts.append(
-            f"{i}. **{anime['title']}** (Score: {anime['score']}/10, Scored by: {anime['scored_by']})\n"
-            f"  Genres: {anime['genres']}\n"
-            f"  Synopsis: {anime['synopsis']}\n"
+    for i, anime in enumerate(retrieved_animes):
+        # We pick the most 'semantic' fields for the LLM to reason with
+        block = (
+            f"Title: {anime['title']} ({anime['year']})\n"
+            f"Type: {anime['type']} | Score: {anime['score']} | Studio: {', '.join(anime['studios'] if isinstance(anime['studios'], list) else [anime.get('studios', 'Unknown')])}\n"
+            f"Genres: {', '.join(anime['genres'] if isinstance(anime['genres'], list) else [anime.get('genres', '')])} | Themes: {', '.join(anime['themes'] if isinstance(anime['themes'], list) else [anime.get('themes', '')])}\n"
+            f"Synopsis: {anime.get('synopsis', 'No synopsis available.')}\n"
+            f"---"
         )
+        context_parts.append(block)
 
     context = "\n".join(context_parts)
 
     prompt = f"""
-You are an expert, polite, casual and friendly anime recommender. Your goal is to give personalized anime recommendations based strictly on the provided database context.
+You are an expert, casual, and friendly anime recommender. Your goal is to give personalized anime recommendations based STRICTLY on the provided database context.
 
 User's Query: "{user_query}"
 
-Available Anime Context (from vector search):
+Available Anime Context (from vector search & reranker):
 {context}
 
+IF THE CONTEXT IS EMPTY:
+Politely say you cannot find any matching anime in your current database. DO NOT make up recommendations.
+
+ELSE:
 # Core Directives:
-1. **Curate, Don't List**: Do not just repeat the context. Analyze the user's specific vibe/theme requests and pick the top {n_recommendations} absolute best matches.
-2. **THE SEQUEL RULE**: You MUST NOT recommend direct sequels, prequels, or movies of the exact anime the user mentioned, UNLESS they explicitly ask for a watch order or sequel. (e.g., If they ask for "shows like Bleach", do NOT recommend "Bleach: Thousand-Year Blood War"). 
-3. **Conversational Tone**: Speak like a relaxed anime fan chatting with a friend. DO NOT use robotic transitions like "Based on the provided context..." or "Here are your recommendations...". Just jump right into the good stuff.
-4. **The Pitch**: For each pick, write 1-2 sentences explaining exactly *why* it fits their specific request based on the synopsis and genres.
-5. **No Hallucinations**: If an anime lacks a synopsis, do not invent one. Explain it using its genres and your general knowledge of its themes.
-6. **Honesty**: If the retrieved context doesn't have any genuinely good matches for the user's query, be honest. Tell them the database didn't have a perfect fit and suggest what kind of show they should look for instead.
+1. Quality Over Quantity (CRITICAL): You are asked to provide up to {n_recommendations} recommendations. However, if only 1 or 2 anime in the context genuinely fit the user's specific vibe/theme request, ONLY recommend those. Do NOT force connections or hallucinate similarities just to reach the maximum number.
+2. The Sequel Rule: You MUST NOT recommend direct sequels, prequels, recap movies, or specials of the exact anime the user mentioned in their query, UNLESS they explicitly ask for a watch order or more of that exact show.
+3. Ruthless Curation: If the user asks for a specific contrast (e.g., "like Death Note but funny"), actively ignore anime in the context that do not fit that contrast. 
+4. Conversational Tone: Speak like a relaxed, highly knowledgeable anime fan chatting with a friend. Never use robotic transitions like "Based on the provided context..." or "Here are your recommendations."
+5. The Pitch: For each pick, write a punchy 2-3 sentence pitch explaining exactly *why* it fits their request. Use specific plot points, themes, or the studio from the provided synopsis to back up your claim.
 
 # Required Format:
 **[Anime Title]**
-[Your 1-2 sentence pitch on why it fits the user's exact vibe]
+[Your 2-3 sentence pitch on why it fits the user's exact vibe, drawing directly from the synopsis or themes.]
 
 **[Anime Title]**
 [Your pitch...]
 
-(Limit to {n_recommendations} recommendations maximum.)
+(Limit to {n_recommendations} recommendations maximum, but fewer is completely fine if the matches are weak.)
 """
 
     return prompt

src/retrieval/rag_pipeline.py

@@ -1,5 +1,6 @@
 import json
 from src.retrieval.vector_search import AnimeRetriever
+from src.llm.anime_reranker import AnimeReranker
 from src.llm.groq_client import GroqLLM
 from src.llm.prompts import create_recommendation_prompt, create_system_prompt, ANIME_SEARCH_TOOL, ROUTER_SYSTEM_PROMPT
 import logging
@@ -12,8 +13,9 @@ class AnimeRAGPipeline:
     def __init__(
             self,
             retriever: AnimeRetriever | None = None,
+            reranker: AnimeReranker | None = None,
             llm: GroqLLM | None = None,
-            retriever_k: int = 10,
+            retriever_k: int = 50,
             recommendation_n: int | None = 5
     ):
         """ 
@@ -26,12 +28,14 @@ class AnimeRAGPipeline:
             recommendation_n: How many to recommend in final output
         """
         self.retriever = retriever or AnimeRetriever()
+        self.reranker = reranker or AnimeReranker()
         self.llm = llm or GroqLLM(model=settings.model_name)
         self.retriever_k = retriever_k
         self.recommendation_n = recommendation_n
 
         logger.info("RAG Pipeline initialized")
         logger.info(f" - Retrieve top {retriever_k} anime from vector search")
+        logger.info(f" - Rerank and filter using Cross-Encoder & Bayesian Math")
         logger.info(
             f" - LLM reasons and recommends top {recommendation_n} anime")
 
@@ -45,7 +49,7 @@ class AnimeRAGPipeline:
 
         Args:
             user_query: User's request (e.g., "Anime like death note for lighter")
-            filters: Optional filters (min_score, genre_filter)
+            filters: Optional filters (min_score, genre_filter, anime_type)
 
         Returns:
             Dict with:
@@ -58,7 +62,7 @@ class AnimeRAGPipeline:
         filters = filters or {}
 
         # [STEP 1] The Agentic Decision Call
-        logger.info("[1/4] Asking LLM if it needs to search...")
+        logger.info("[1/5] Asking LLM if it needs to search...")
 
         initial_response = self.llm.chat_with_tools(
             messages=[{"role": "user", "content": user_query}],
@@ -77,7 +81,7 @@ class AnimeRAGPipeline:
             }
         if not initial_response.tool_calls:
             logger.info(
-                "[2/4] No search needed. Returning conversational response.")
+                "[2/5] No search needed. Returning conversational response.")
             return {
                 "query": user_query,
                 "retrieved_count": 0,
@@ -86,7 +90,7 @@ class AnimeRAGPipeline:
             }
 
         # [STEP 3] The LLM wants to search. Extract its optimized parameters.
-        logger.info("[2/4] Tool called! Executing vector search...")
+        logger.info("[2/5] Tool called! Executing vector search...")
         tool_call = initial_response.tool_calls[0]
         tool_args = json.loads(tool_call.function.arguments)
 
@@ -94,21 +98,40 @@ class AnimeRAGPipeline:
 
         optimized_query = tool_args.get("optimized_query", user_query)
 
+        # 3A: Fetch Top >=50 Anime from Chromadb
         retrieved_animes = self.retriever.search(
             query=optimized_query,
             n_results=self.retriever_k,
             **filters
         )
 
+        # 3B: Rerank the Top 50 Using Pytorch + Math
+        logger.info("[3/5] Reranking results with Cross-Encoder")
+
+        reranked_df = self.reranker.process(
+            user_query=optimized_query,
+            retrieved_anime=retrieved_animes,
+            top_k=15
+        )
+
+        top_animes_list = reranked_df.to_dict(
+            orient="records") if not reranked_df.empty else []
+        print("FIRST IN TOP ANIMES LIST AFTER RERANKING \n",
+              top_animes_list[0])
+        print("SECOND IN TOP ANIMES LIST AFTER RERANKING \n",
+              top_animes_list[1])
+        print(
+            f"After reranking, fetched {len(top_animes_list)} top animes....")
+
         # [STEP 4] The Final Recommendation Call
-        logger.info("[3/4] Creating prompt with retrieved content...")
+        logger.info("[4/5] Creating prompt with retrieved content...")
         prompt = create_recommendation_prompt(
             user_query=user_query,
-            retrieved_animes=retrieved_animes,
+            retrieved_animes=top_animes_list,
             n_recommendations=self.recommendation_n
         )
 
-        logger.info("[4/4] LLM generating final response...")
+        logger.info("[5/5] LLM generating final response...")
         system_prompt = create_system_prompt()
 
         recommendations = self.llm.generate(
@@ -123,8 +146,9 @@ class AnimeRAGPipeline:
         return {
             "query": user_query,
             "retrieved_count": len(retrieved_animes),
+            "reranked_count": len(top_animes_list),
             "recommendations": recommendations,
-            "retrieved_animes": retrieved_animes
+            "retrieved_animes": top_animes_list
         }
 
     def recommend_streaming(self, user_query: str, filters: dict | None = None):
@@ -137,40 +161,79 @@ class AnimeRAGPipeline:
 
 if __name__ == "__main__":
     import json
+    import os
+
+    # Ensure the data directory exists
+    os.makedirs("data", exist_ok=True)
 
     pipeline = AnimeRAGPipeline(
-        retriever_k=10,
+        retriever_k=50,
         recommendation_n=5
     )
 
     test_queries = [
         "Anime similar to Death Note but lighter in tone",
-        "Romantic comedy set in high school",
-        "Dark psychological thriller",
-        "Action anime with great animation and epic fights"
+        "A really obscure and weird sci-fi mecha from the 90s", # Test the hidden gem / passion rate
+        "A generic isekai with an overpowered main character"   # Test the mainstream math
     ]
 
     for query in test_queries:
-        result = pipeline.recommend(user_query=query)
+        print(f"\n" + "="*80)
+        print(f"TESTING QUERY: '{query}'")
+        print("="*80)
 
-        print(f"-------\nQuery: {query}:::\n-----")
-
-        print(f"\nRecommendations:\n---------------")
-
-        print(result["recommendations"])
-
-        # Save results for inspection
-        filename = f"test_result_{query[:30].replace(' ', '_')}.json"
-        with open(f"data/{filename}", "w") as f:
-            json.dump({
-                "query": result["query"],
-                "retrieved_titles": [a["title"] for a in result["retrieved_animes"]],
-                "recommendations": result["recommendations"]
-            }, f, indent=2)
+        # 1. Run the pipeline (This executes the LLM routing, retrieval, reranking, and generation)
+        result = pipeline.recommend(user_query=query)
 
-        print(f"\nSaved to data/{filename}")
-        print("\n" + "="*80 + "\n")
+        # 2. Extract the data for the diagnostic report
+        # We need to reach into the pipeline's retriever to see what the raw ChromaDB output was,
+        # since the pipeline only returns the *reranked* list.
+        # Note: If the LLM chose NOT to search, result["retrieved_count"] will be 0.
+        
+        diagnostic_data = {
+            "1_initial_user_query": result["query"],
+            "2_llm_routing_decision": "Searched" if result["retrieved_count"] > 0 else "Chatted",
+            "3_total_retrieved_from_chroma": result["retrieved_count"],
+            "4_total_survived_reranking": result.get("reranked_count", 0),
+            "5_final_llm_recommendation_text": result["recommendations"],
+            
+            # The "Before" state: We want to see what ChromaDB found just using embeddings
+            "6_raw_chroma_results_before_reranking": [],
+            
+            # The "After" state: We want to see the exact math scores for the top survivors
+            "7_reranked_results_with_math": []
+        }
 
-        # Pause between queries to respect rate limits
+        # If the LLM actually performed a search, let's build the detailed lists
+        if result["retrieved_count"] > 0:
+            
+            # We need to get the optimized query that the LLM generated for the tool call.
+            # (In a real app, you might want to return `optimized_query` in the `result` dict from `recommend()`)
+            # For this test, we will just assume it's the original query if we can't easily grab it here.
+            
+            # Let's format the top 15 Reranked items with all their math exposed
+            for rank, anime in enumerate(result["retrieved_animes"]):
+                diagnostic_data["7_reranked_results_with_math"].append({
+                    "rank": rank + 1,
+                    "title": anime["title"],
+                    "hybrid_score_final": round(anime.get("final_hybrid_score", 0), 4),
+                    "semantic_score_raw": round(anime.get("semantic_score", 0), 4),
+                    "quality_score_raw": round(anime.get("raw_quality_score", 0), 4),
+                    "bayesian_average": round(anime.get("bayesian_score", 0), 4),
+                    "passion_rate": round(anime.get("passion_rate", 0), 5),
+                    "chroma_distance": round(1 - anime.get("relevance_score", 1), 4) # Showing original vector distance
+                })
+
+        # 3. Save the diagnostic report
+        safe_filename = f"diagnostic_{query[:20].replace(' ', '_').lower()}.json"
+        filepath = os.path.join("data", safe_filename)
+        
+        with open(filepath, "w") as f:
+            json.dump(diagnostic_data, f, indent=2)
+
+        print(f"\n✅ Diagnostic saved to {filepath}")
+        print(f"Generated Output:\n{result['recommendations']}")
+
+        # Pause to respect Groq API rate limits
         import time
-        time.sleep(2)
+        time.sleep(3)

src/retrieval/vector_search.py

@@ -1,5 +1,14 @@
 import chromadb
 from sentence_transformers import SentenceTransformer
+from src.database.session import engine
+from src.database.models import Animes
+from sqlmodel import Session, select
+import logging
+
+
+logger = logging.getLogger(__name__)
+logging.basicConfig(level=logging.INFO,
+                    format='%(asctime)s - %(levelname)s - %(message)s')
 
 
 class AnimeRetriever:
@@ -15,12 +24,22 @@ class AnimeRetriever:
 
         print(f"Loaded collection with {self.collection.count()} anime")
 
+    def fetch_anime_batch_from_postgres(self, mal_ids: list[int]) -> dict[int, Animes]:
+        """Fetch multiple animes at once and return a dictionary mapped by mal_id"""
+        with Session(engine) as session:
+            # The .in_() operator acts like SQL's "WHERE mal_id IN (1, 2, 3)"
+            statement = select(Animes).where(Animes.mal_id.in_(mal_ids))
+            results = session.exec(statement).all()
+
+            return {anime.mal_id: anime for anime in results}
+
     def search(
             self,
             query: str,
             n_results: int = 5,
-            genre_filter: str | None = None,
-            min_score: float | None = None
+            genre_filter: list[str] | None = None,
+            min_score: float | None = 6.0,
+            anime_type: str | None = None
     ) -> list[dict]:
         """
         Search for anime similar to query
@@ -30,50 +49,94 @@ class AnimeRetriever:
             n_results: Number of results to return
             genre_filter: Optional genre to filter by
             min_score: Minimum MAL score (e.g., 7.0)
+            anime_type: Type of Anime (e.g. TV, Movie, etc)
 
         Returns:
             List of dicts with anime info
         """
-        where_clause = {}
+        # 1. Build chromadb WHERE clause for filtering
+        conditions = [{"scored_by": {"$gte": 9000}}]
         if min_score:
-            where_clause["score"] = {"$gte": min_score}
+            logger.info(f"SCORE: Filtered based on min_score: {min_score}")
+            conditions.append({"score": {"$gte": min_score}})
+
+        if anime_type:
+            logger.info(
+                f"ANIME TYPE: Filtered based on anime_type: {anime_type}")
+            conditions.append({"type": {"$eq": anime_type}})
 
-        # Genre Filtering later
+        if genre_filter:
+            logger.info(
+                f"GENRE: Pre-filtering (OR) for genres: {', '.join(genre_filter)}")
 
+            genre_or_conditions = [
+                {"genres": {"$contains": genre}} for genre in genre_filter]
+
+            if len(genre_or_conditions) == 1:
+                conditions.append(genre_or_conditions[0])
+            else:
+                conditions.append({"$or": genre_or_conditions})
+
+        where_clause = None
+        if len(conditions) == 1:
+            where_clause = conditions[0]
+        elif len(conditions) > 1:
+            where_clause = {"$and": conditions}
+
+        # 2. Query ChromaDB
         results = self.collection.query(
             query_texts=[query],
-            n_results=n_results * 2 if genre_filter else n_results,
-            where=where_clause if where_clause else None
+            n_results=n_results,
+            where=where_clause
         )
+        if not results["ids"][0]:
+            return []
+
+        # 3. Batch fetch from PostgreSQL
+        retrieved_ids = [int(id_str) for id_str in results["ids"][0]]
+        postgres_data_map = self.fetch_anime_batch_from_postgres(retrieved_ids)
 
         anime_list = []
-        for i in range(len(results["ids"][0])):
-            metadata = results["metadatas"][0][i]  # type: ignore
-            distance = results["distances"][0][i]  # type: ignore
+        for i, id_str in enumerate(results["ids"][0]):
+            mal_id = int(id_str)
+            distance = results["distances"][0][i]
 
-            # Genre filtering (if specified)
-            if genre_filter and "genres" in metadata:
-                # type: ignore
-                if genre_filter.lower() not in metadata["genres"].lower():
-                    continue
+            # Get the rich data from our Postgres map
+            pg_anime = postgres_data_map.get(mal_id)
 
+            if not pg_anime:
+                logger.warning(
+                    f"Anime ID {mal_id} found in Chroma but missing in Postgres!")
+                continue
+
+            # Merge Chroma math with Postgres truths
             anime_info = {
-                "mal_id": results["ids"][0][i],
-                "title": metadata["title"],
-                "genres": metadata.get("genres", ""),
-                "score": metadata["score"],
-                "type": metadata["type"],
-                "year": metadata["year"],
-                "synopsis": metadata["synopsis"],
-                "distance": distance,
-                "scored_by": metadata["scored_by"],
-                "relevance_score": 1 - distance  # Convert distance to similarity
+                "mal_id": pg_anime.mal_id,
+                "mal_url": pg_anime.url,
+                "title": pg_anime.title,
+                "title_english": pg_anime.title_english,
+                "score": pg_anime.score,
+                "scored_by": pg_anime.scored_by,
+                "type": pg_anime.type,
+                "year": pg_anime.year,
+                "genres": pg_anime.genres,  # Now an actual list again!
+                "studios": pg_anime.studios,
+                "themes": pg_anime.themes,
+                "demographics": pg_anime.demographics,
+                "episodes": pg_anime.episodes,
+                "popularity": pg_anime.popularity,
+                "rating": pg_anime.rating,
+                "aired_from": pg_anime.aired_from,
+                "aired_to": pg_anime.aired_to,
+                "favorites": pg_anime.favorites,
+                "images": pg_anime.images,
+                "synopsis": pg_anime.synopsis,
+                "searchable_text": pg_anime.searchable_text,
+                # Clean rounded score
+                "relevance_score": round(1 - distance, 3),
             }
             anime_list.append(anime_info)
 
-            if len(anime_list) >= n_results:
-                break
-
         return anime_list
 
     def get_by_title(self, title: str) -> dict | None:
@@ -86,26 +149,42 @@ class AnimeRetriever:
 if __name__ == "__main__":
     retriever = AnimeRetriever()
 
-    # Test queries
-    print("=== Test 1: Basic Search ===")
-    results = retriever.search("dark psychological anime", n_results=5)
-    for anime in results:
-        print(
-            f"- {anime['title']} (score: {anime['score']}, relevance: {anime['relevance_score']:.3f})")
-
-    print("\n=== Test 2: Genre Filter ===")
-    results = retriever.search(
-        "high school", n_results=5, genre_filter="Comedy")
-    for anime in results:
-        print(f"- {anime['title']} ({anime['genres']})")
+    # # Test queries
+    # print("=== Test 1: Basic Search ===")
+    # results = retriever.search("dark psychological anime", n_results=15)
+    # for anime in results:
+    #     print(
+    #         f"- {anime['title']} (score: {anime['score']}, relevance: {anime['relevance_score']:.3f})")
 
-    print("\n=== Test 3: Score Filter ===")
-    results = retriever.search("adventure", n_results=5, min_score=8.0)
-    for anime in results:
-        print(f"- {anime['title']} (score: {anime['score']})")
+    # print("\n=== Test 2: Genre Filter ===")
+    # results = retriever.search(
+    #     "high school", n_results=30, genre_filter=["Fantasy", "Action", "Comedy", "Adventure"])
+    # for anime in results:
+    #     print(f"- {anime['title']} ({anime['genres']})")
 
-    print("\n=== Test 4: Scored by Filter ===")
-    results = retriever.search("adventure", n_results=5, min_score=8.0)
+    print("\n=== Test 3: Genre Filter ===")
+    results = retriever.search(
+        "Overpowered Main character", n_results=5, genre_filter=["Adventure"])
     for anime in results:
-        print(
-            f"- {anime['title']} (score: {anime['score']}) (scored_by: {anime['scored_by']})")
+        # print(
+        # f"- {anime['title']} ({anime['genres']}) (Score: {anime["score"]}) (Scored by: {anime["scored_by"]})")
+        print(anime)
+        break
+
+    # print("\n=== Test 3: Score Filter ===")
+    # results = retriever.search("adventure", n_results=5, min_score=9.0)
+    # for anime in results:
+    #     print(f"- {anime['title']} (score: {anime['score']})")
+
+    # print("\n=== Test 4: Scored by Filter ===")
+    # results = retriever.search("adventure", n_results=5, min_score=8.0)
+    # for anime in results:
+    #     print(
+    #         f"- {anime['title']} (score: {anime['score']}) (scored_by: {anime['scored_by']})")
+
+    # print("\n=== Test 5: TYPE Filter ===")
+    # results = retriever.search(
+    #     "Attack On Titan", n_results=5, anime_type="Special")
+    # for anime in results:
+    #     print(
+    #         f"- {anime['title']} (Anime Type: {anime['type']})")

ui/gradio_app.py

@@ -2,416 +2,768 @@ import gradio as gr
 from gradio import themes
 import requests
 import os
+import re
 
 API_URL = os.getenv("API_URL", "http://127.0.0.1:8000")
 
+ANIME_TYPES = ["Any", "TV", "Movie", "OVA", "ONA",
+               "Special", "Music", "CM", "PV", "TV Special"]
+GENRES = [
+    "Any", "Action", "Adventure", "Avant Garde", "Award Winning", "Boys Love",
+    "Comedy", "Drama", "Ecchi", "Erotica", "Fantasy", "Girls Love", "Gourmet",
+    "Hentai", "Horror", "Mystery", "Romance", "Sci-Fi", "Slice of Life", "Sports",
+    "Supernatural", "Suspense"
+]
+
+SUGGESTIONS = [
+    {"label": "🌸 Similar to Naruto",
+        "query": "Action packed themed anime similar to Naruto"},
+    {"label": "⚔️  Dark fantasy",
+        "query": "Dark fantasy with an unreliable narrator"},
+    {"label": "🤖 Cyberpunk action",
+        "query": "Fast-paced cyberpunk action with great animation"},
+    {"label": "💀 Psychological",
+        "query": "Psychological thriller that messes with your head"},
+    {"label": "💘 Romance drama",      "query": "Bittersweet romance that makes you cry"},
+]
+
+# ─── HTML builders ────────────────────────────────────────────────────────────
+
+
+def _img(images: dict) -> str:
+    webp = images.get("webp", {}).get("image_url", "")
+    jpg = images.get("jpg",  {}).get("image_url", "")
+    return webp or jpg or ""
+
+
+def build_spotlight_card(anime: dict) -> str:
+    img = _img(anime.get("images", {}))
+    title = anime.get("title", "Unknown")
+    eng = anime.get("title_english") or ""
+    score = anime.get("score") or "—"
+    year = anime.get("year") or "—"
+    kind = anime.get("type") or "—"
+    eps = anime.get("episodes") or "—"
+    genres = anime.get("genres") or []
+    studios = anime.get("studios") or []
+    url = anime.get("mal_url", "#")
+    raw_syn = anime.get("synopsis") or ""
+    synopsis = raw_syn[:220].rstrip() + ("..." if len(raw_syn) > 220 else "")
+
+    genre_pills = "".join(f"<span class='pill'>{g}</span>" for g in genres[:4])
+    studio_str = ", ".join(studios[:2]) if studios else "Unknown Studio"
+
+    eng_html = f"<h4 class='sc-eng'>{eng}</h4>" if eng and eng.strip(
+    ) and eng != title else ""
 
-def get_recommendations(query, min_score, genre_filter, n_results):
+    try:
+        s = float(score)
+        sc = "#00e5a0" if s >= 8 else "#fbbf24" if s >= 7 else "#f87171"
+    except Exception:
+        sc = "#6b7a99"
+
+    return (
+        f"<a href='{url}' target='_blank' class='sc'>"
+        f"<div class='sc-poster'>"
+        f"<img src='{img}' alt='{title}' loading='lazy' "
+        f"onerror=\"this.src='https://placehold.co/280x400/0d1520/00c896?text=?'\"/>"
+        f"<div class='sc-badge' style='color:{sc}'>&#9733; {score}</div>"
+        f"</div>"
+        f"<div class='sc-body'>"
+        f"<div class='sc-chips'>"
+        f"<span class='mc'>{kind}</span><span class='mc'>{year}</span><span class='mc'>{eps} ep</span>"
+        f"</div>"
+        f"<h3 class='sc-title'>{title}</h3>"
+        f"{eng_html}"
+        f"<p class='sc-studio'>{studio_str}</p>"
+        f"<p class='sc-synopsis'>{synopsis}</p>"
+        f"<div class='pill-row'>{genre_pills}</div>"
+        f"<span class='mal-btn'>View on MAL &#8599;</span>"
+        f"</div></a>"
+    )
+
+
+def build_retrieved_row(anime: dict, idx: int) -> str:
+    img = _img(anime.get("images", {}))
+    title = anime.get("title", "Unknown")
+    score = anime.get("score") or "—"
+    year = anime.get("year") or "—"
+    kind = anime.get("type") or ""
+    genres = (anime.get("genres") or [])[:2]
+    url = anime.get("mal_url", "#")
+    raw_score = anime.get("relevance_score", 0)
+    normalized_score = (raw_score + 1) / 2
+    rel_pct = int(normalized_score * 100)
+    genre_str = " · ".join(genres)
+
+    return (
+        f"<a href='{url}' target='_blank' class='rr'>"
+        f"<span class='rr-idx'>{idx:02d}</span>"
+        f"<img src='{img}' alt='{title}' class='rr-thumb' loading='lazy' "
+        f"onerror=\"this.src='https://placehold.co/44x62/0d1520/00c896?text=?'\"/>"
+        f"<div class='rr-info'>"
+        f"<span class='rr-title'>{title}</span>"
+        f"<span class='rr-sub'>{kind} · {year} · {genre_str}</span>"
+        f"<div class='rr-bar-bg'><div class='rr-bar-fill' style='width:{rel_pct}%'></div></div>"
+        f"</div>"
+        f"<div class='rr-right'>"
+        f"<span class='rr-score'>&#9733; {score}</span>"
+        f"<span class='rr-pct'>{rel_pct}%</span>"
+        f"</div>"
+        f"</a>"
+    )
+
+
+def format_output(result: dict) -> tuple[str, str]:
+    recs_text = result.get("recommendations", "")
+    retrieved_count = result.get("retrieved_count", 0)
+    time_str = result.get("metadata", {}).get(
+        "Time taken for LLM + vector search", "?")
+    retrieved = result.get("retrieved_animes", [])
+
+    header = (
+        "<div class='hud'>"
+        "<div class='hud-pill ready'>&#10022; Results ready</div>"
+        "<div class='hud-stats'>"
+        f"<div class='hud-stat'><span class='hud-n'>{retrieved_count}</span>"
+        f"<span class='hud-l'>titles scanned</span></div>"
+        "<div class='hud-sep'></div>"
+        f"<div class='hud-stat'><span class='hud-n'>{time_str[:4]}s</span>"
+        f"<span class='hud-l'>search time</span></div>"
+        "</div></div>"
+    )
+
+    sections = re.split(r'\*\*(.+?)\*\*', recs_text)
+    title_map = {a.get("title", "").lower(): a for a in retrieved}
+    title_map.update(
+        {(a.get("title_english") or "").lower(): a for a in retrieved})
+
+    recs_html = ""
+    if len(sections) > 1:
+        pre = sections[0].strip()
+        if pre:
+            recs_html += f"<p class='llm-intro'>{pre}</p>"
+
+        pairs = list(zip(sections[1::2], sections[2::2]))
+        for i, (rec_title, rec_body) in enumerate(pairs):
+            rec_title = rec_title.strip()
+            rec_body = rec_body.strip().lstrip("\n").rstrip("\n—-").strip()
+
+            matched = title_map.get(rec_title.lower())
+            if not matched:
+                for k, v in title_map.items():
+                    if k and (k in rec_title.lower() or rec_title.lower() in k):
+                        matched = v
+                        break
+
+            card = build_spotlight_card(
+                matched) if matched else f"<div class='no-img-title'>{rec_title}</div>"
+            delay = f"{i * 0.09:.2f}"
+            recs_html += (
+                f"<div class='rec-block' style='animation-delay:{delay}s'>"
+                f"{card}"
+                f"<div class='llm-pitch'>{rec_body}</div>"
+                f"</div>"
+            )
+    else:
+        recs_html = f"<div class='convo-msg'>{recs_text}</div>"
+
+    sidebar = ""
+    if retrieved:
+        rows = "".join(build_retrieved_row(a, i + 1)
+                       for i, a in enumerate(retrieved))
+        sidebar = (
+            "<aside class='sidebar'>"
+            "<div class='sidebar-head'>"
+            "<span class='sidebar-label'>Vector search</span>"
+            f"<span class='sidebar-count'>{retrieved_count}</span>"
+            "</div>"
+            f"<div class='rr-list'>{rows}</div>"
+            "</aside>"
+        )
+
+    body = (
+        "<div class='main-layout'>"
+        "<section class='recs-col'>"
+        "<div class='recs-heading'>Recommendations</div>"
+        f"{recs_html}"
+        "</section>"
+        f"{sidebar}"
+        "</div>"
+    )
+    return header, body
+
+
+def get_recommendations(query, min_score, genre_filter, anime_type, n_results):
     if not query or not query.strip():
         return (
-            "<div class='status-badge warning'>Waiting for prompt</div>",
-            "Tell me what you're in the mood for to get started."
+            "<div class='hud'><div class='hud-pill idle'>Waiting for your prompt...</div></div>",
+            ""
         )
-
     try:
-        payload = {
-            "query": query.strip(),
-            "n_results": int(n_results)
-        }
+        payload = {"query": query.strip(), "n_results": int(n_results)}
         if min_score > 0:
             payload["min_score"] = float(min_score)
-        if genre_filter and genre_filter != "Any":
+        if genre_filter and "Any" not in genre_filter:
             payload["genre_filter"] = genre_filter
+        if anime_type and anime_type != "Any":
+            payload["anime_type"] = anime_type
 
         response = requests.post(
             f"{API_URL}/recommend", json=payload, timeout=30)
         response.raise_for_status()
         result = response.json()
 
-        recommendations_text = result.get("recommendations", "")
-        retrieved_count = result.get("retrieved_count", 0)
-        time_taken = result.get("metadata", {}).get(
-            "Time taken for LLM + vector search", "unknown")
-
-        if not recommendations_text or recommendations_text.strip() == "":
+        if not result.get("recommendations", "").strip():
             return (
-                "<div class='status-badge error'>No Matches</div>",
-                "Couldn't find anything matching that exact vibe. Try tweaking your search."
+                "<div class='hud'><div class='hud-pill err'>No matches</div></div>",
+                "<p class='convo-msg'>Couldn't find anything for that vibe. Try different keywords.</p>"
             )
-
-        header_html = f"""
-        <div class='results-dashboard'>
-            <div class='metric'>
-                <span class='label'>TITLES SCANNED</span>
-                <span class='value'>{retrieved_count}</span>
-            </div>
-            <div class='metric'>
-                <span class='label'>SEARCH TIME</span>
-                <span class='value'>{time_taken}</span>
-            </div>
-            <div class='status-badge success'>Search Complete</div>
-        </div>
-        """
-        return header_html, recommendations_text
+        return format_output(result)
 
     except requests.exceptions.ConnectionError:
         return (
-            "<div class='status-badge error'>Offline</div>",
-            "**Error:** Cannot connect to backend. Make sure the FastAPI server is running."
+            "<div class='hud'><div class='hud-pill err'>Offline</div></div>",
+            "<p class='convo-msg'>Cannot reach the backend. Is FastAPI running?</p>"
         )
     except Exception as e:
-        return ("<div class='status-badge error'>Error</div>", f"**Something went wrong:** {str(e)}")
+        return (
+            "<div class='hud'><div class='hud-pill err'>Error</div></div>",
+            f"<p class='convo-msg'>Something went wrong: {e}</p>"
+        )
 
 
+# =============================================================================
+#  CSS (SCALED DOWN TO "AVERAGE" SIZE)
+# =============================================================================
 CSS = """
-@import url('https://fonts.googleapis.com/css2?family=Outfit:wght@400;600;800&family=Inter:wght@400;500&display=swap');
+@import url('https://fonts.googleapis.com/css2?family=Plus+Jakarta+Sans:wght@300;400;500;600;700;800&display=swap');
 
 :root {
-    --bg-base: #0b0f19;
-    --bg-panel: #111827;
-    --accent: #6366f1;
-    --accent-hover: #4f46e5;
-    --text-main: #f3f4f6;
-    --text-muted: #9ca3af;
-    --border-dim: #1f2937;
+    --bg:    #070c12;
+    --bg2:   #121b29;
+    --bg3:   #1a2639;
+    --bdr:   rgba(255,255,255,0.08);
+    --acc:   #00c896;
+    --acc2:  #00e8b0;
+    --adim:  rgba(0,200,150,0.12);
+    --aglow: rgba(0,200,150,0.3);
+    --amb:   #fbbf24;
+    --red:   #f87171;
+    --txt:   #dde3f0;
+    --mut:   #6b8299;
+    --f:     'Plus Jakarta Sans', sans-serif;
+    --sw:    220px; /* Reduced sidebar width */
 }
 
-body, .gradio-container {
-    background-color: var(--bg-base) !important;
-    font-family: 'Inter', sans-serif !important;
-    color: var(--text-main) !important;
+*, *::before, *::after { box-sizing: border-box; }
+
+html, body {
+    background: var(--bg) !important;
+    font-family: var(--f) !important;
+    color: var(--txt) !important;
+    margin: 0; padding: 0;
 }
 
 .gradio-container {
-    max-width: 960px !important;
-    margin: 0 auto !important;
-    padding: 50px 20px !important;
+    max-width: 100% !important;
+    padding: 0 !important;
+    margin: 0 !important;
+}
+
+#main-content {
+    margin-left: var(--sw);
+    width: calc(100% - var(--sw));
+    padding: 0;
+    min-height: 100vh;
+    display: flex;
+    flex-direction: column;
 }
 
-/* Typography & Headers */
-.app-header {
+footer { display: none !important; }
+
+/* ── HERO BRAND ──────────────────────────────────────────────── */
+.hero-brand {
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+    justify-content: center;
+    padding: 40px 20px 20px;
     text-align: center;
-    margin-bottom: 40px;
 }
+.hero-logo-row {
+    display: flex;
+    align-items: center;
+    gap: 12px;
+    margin-bottom: 6px;
+}
+.hero-icon {
+    width: 38px; height: 38px;
+    background: linear-gradient(135deg, var(--acc), var(--acc2));
+    border-radius: 12px;
+    display: flex; align-items: center; justify-content: center;
+    font-size: 20px; flex-shrink: 0;
+    box-shadow: 0 4px 16px var(--aglow);
+    color: #070c12;
+}
+.hero-name { font-size: 24px; font-weight: 800; color: #fff; letter-spacing: -.5px; }
+.hero-name em { font-style: normal; color: var(--acc); }
+
+/* ── FIXED SIDEBAR ───────────────────────────────────────────── */
+#anifind-sidebar {
+    position: fixed;
+    top: 0; left: 0;
+    width: var(--sw);
+    height: 100vh;
+    background: var(--bg);
+    border-right: 1px solid var(--bdr);
+    display: flex;
+    flex-direction: column;
+    padding: 30px 20px;
+    overflow-y: auto;
+    z-index: 999;
+    scrollbar-width: none;
+    box-shadow: 4px 0 20px rgba(0,0,0,0.2);
+}
+#anifind-sidebar::-webkit-scrollbar { display: none; }
 
-.app-title {
-    font-family: 'Outfit', sans-serif;
-    font-size: 42px;
-    font-weight: 800;
-    color: #ffffff;
-    margin: 0 0 8px 0;
-    letter-spacing: -1px;
+.sb-tagline {
+    font-size: 20px; font-weight: 800; color: #fff;
+    line-height: 1.3; letter-spacing: -.3px; margin-bottom: 10px;
 }
+.sb-tagline em { font-style: normal; color: var(--acc); }
 
-.app-title span { color: var(--accent); }
-.app-subtitle { font-size: 16px; color: var(--text-muted); line-height: 1.5; }
+.sb-sub {
+    font-size: 13px; color: var(--mut);
+    line-height: 1.5; margin-bottom: 30px;
+}
 
-/* 🔥 NEW Unified Chat Input Bar 🔥 */
-#chat-input-container {
-    background: var(--bg-panel);
-    border: 1px solid var(--border-dim);
-    border-radius: 24px;
-    padding: 8px 8px 8px 16px;
-    margin-bottom: 16px;
-    display: flex;
-    align-items: flex-end;
-    transition: all 0.2s ease;
-    box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1);
+.sb-sect {
+    font-size: 11px; font-weight: 700;
+    letter-spacing: 1.2px; text-transform: uppercase;
+    color: var(--mut); margin-bottom: 12px;
 }
 
-#chat-input-container:focus-within {
-    border-color: var(--accent);
-    box-shadow: 0 0 0 2px rgba(99, 102, 241, 0.25);
+.chips-col { display: flex; flex-direction: column; gap: 8px; }
+
+.chip {
+    font-family: var(--f); font-size: 13px; font-weight: 600;
+    color: var(--txt); background: var(--bg2);
+    border: 1px solid var(--bdr);
+    padding: 10px 14px; border-radius: 10px;
+    cursor: pointer; text-align: left;
+    transition: all .3s cubic-bezier(0.34, 1.56, 0.64, 1);
+}
+.chip:hover {
+    color: var(--acc); background: var(--bg3);
+    border-color: rgba(0,200,150,.5);
+    transform: translateX(4px) scale(1.02);
+    box-shadow: 0 4px 12px rgba(0,0,0,0.25);
 }
 
-/* Override Gradio's internal Textbox styling */
-#query-input {
-    border: none !important;
-    box-shadow: none !important;
-    background: transparent !important;
-    flex-grow: 1;
+/* ── STICKY SEARCH BAR ───────────────────────────────────────── */
+#search-zone {
+    position: sticky !important;
+    top: 0 !important; z-index: 100;
+    background: rgba(7,12,18,0.85) !important;
+    backdrop-filter: blur(16px);
+    -webkit-backdrop-filter: blur(16px);
+    border-bottom: 1px solid var(--bdr) !important;
+    padding: 16px 24px !important;
+    gap: 12px !important;
+    align-items: center !important;
 }
 
+#query-input { 
+    background: var(--bg2) !important;
+    border: 2px solid var(--bdr) !important;
+    border-radius: 12px !important;
+    transition: all .3s cubic-bezier(0.16, 1, 0.3, 1) !important;
+}
+#query-input:focus-within {
+    border-color: var(--acc) !important;
+    box-shadow: 0 0 0 3px var(--adim) !important;
+}
 #query-input textarea {
+    font-family: var(--f) !important;
+    font-size: 13px !important;
+    padding: 10px !important;
     background: transparent !important;
+    color: #fff !important;
     border: none !important;
     box-shadow: none !important;
-    color: #ffffff !important;
-    font-family: 'Inter', sans-serif !important;
-    font-size: 16px !important;
-    padding: 12px 0 !important;
-    resize: none !important; /* Removes the little drag handle */
 }
 
-#query-input textarea:focus {
-    box-shadow: none !important;
+#send-btn { min-width: unset !important; }
+#send-btn button {
+    background: linear-gradient(135deg, var(--acc), #00ffc8) !important;
+    color: #000 !important;
     border: none !important;
+    border-radius: 12px !important;
+    height: 42px !important;
+    width: 42px !important;
+    font-size: 18px !important;
+    font-weight: 900 !important;
+    cursor: pointer;
+    transition: all .3s cubic-bezier(0.34, 1.56, 0.64, 1) !important;
+    box-shadow: 0 4px 14px rgba(0, 200, 150, 0.4), inset 0 2px 0 rgba(255,255,255,0.3) !important;
+    display: flex; align-items: center; justify-content: center;
+}
+#send-btn button:hover {
+    transform: translateY(-2px) scale(1.05) !important;
+    box-shadow: 0 8px 20px rgba(0, 200, 150, 0.6) !important;
+}
+#send-btn button:active { transform: scale(0.95) !important; }
+
+/* ── FILTER BAR ──────────────────────────────────────────────── */
+#filter-bar {
+    background: var(--bg2) !important;
+    border: 1px solid var(--bdr) !important;
+    border-radius: 14px !important;
+    margin: 16px 24px 0 !important;
+    padding: 16px 24px !important;
+    box-shadow: 0 4px 16px rgba(0,0,0,0.15) !important;
+    gap: 16px !important;
+}
+#filter-bar label span {
+    color: var(--mut) !important;
+    font-weight: 700 !important;
+    text-transform: uppercase !important;
+    letter-spacing: 1px !important;
+    font-size: 11px !important;
 }
 
-/* 🔥 The Circle Send Button 🔥 */
-#send-btn {
-    background: var(--accent) !important;
-    color: #ffffff !important;
-    border: none !important;
-    border-radius: 50% !important;
-    width: 44px !important;
-    height: 44px !important;
-    min-width: 44px !important;
-    display: flex;
-    align-items: center;
-    justify-content: center;
-    font-size: 20px !important;
-    padding: 0 !important;
-    margin-left: 12px;
-    margin-bottom: 4px; /* Keeps it aligned to the bottom when text expands */
-    cursor: pointer;
-    transition: all 0.2s cubic-bezier(0.4, 0, 0.2, 1) !important;
+/* ── OUTPUT ZONE ─────────────────────────────────────────────── */
+#output-zone { 
+    padding: 24px !important; 
+    flex: 1;
+}
+
+.hud {
+    display: flex; align-items: center; justify-content: space-between;
+    background: var(--bg2); border: 1px solid var(--bdr);
+    border-radius: 12px; padding: 12px 20px;
+    margin-bottom: 24px;
+    box-shadow: 0 2px 8px rgba(0,0,0,0.1);
+}
+.hud-pill {
+    font-size: 13px; font-weight: 700;
+    padding: 6px 14px; border-radius: 100px;
+}
+.hud-pill.ready { background: var(--adim); color: var(--acc); border: 1px solid rgba(0,200,150,.3); }
+.hud-pill.idle  { background: rgba(251,191,36,.1); color: var(--amb); border: 1px solid rgba(251,191,36,.2); }
+.hud-pill.err   { background: rgba(248,113,113,.1); color: var(--red); border: 1px solid rgba(248,113,113,.2); }
+
+.hud-stats { display: flex; align-items: center; gap: 16px; }
+.hud-stat  { display: flex; flex-direction: column; align-items: flex-end; }
+.hud-n { font-size: 18px; font-weight: 800; color: #fff; line-height: 1; }
+.hud-l { font-size: 11px; font-weight: 600; color: var(--mut); text-transform: uppercase; margin-top: 4px; }
+.hud-sep { width: 1px; height: 28px; background: var(--bdr); }
+
+.main-layout {
+    display: grid; grid-template-columns: 1fr 350px;
+    gap: 24px; align-items: start;
 }
 
-#send-btn:hover {
-    background: var(--accent-hover) !important;
-    transform: scale(1.05);
-    box-shadow: 0 4px 12px rgba(99, 102, 241, 0.4) !important;
+.recs-heading {
+    font-size: 12px; font-weight: 800; letter-spacing: 1.5px;
+    text-transform: uppercase; color: var(--mut);
+    margin-bottom: 18px; padding-bottom: 10px;
+    border-bottom: 2px solid var(--bdr);
 }
 
-#send-btn:active {
-    transform: scale(0.95);
+/* ── RECOMMENDATION BLOCK ────────────────────────────────────── */
+.rec-block {
+    background: var(--bg2); 
+    border: 1px solid var(--bdr);
+    border-radius: 16px; overflow: hidden; margin-bottom: 20px;
+    opacity: 0; transform: translateY(15px);
+    animation: up .4s cubic-bezier(0.16, 1, 0.3, 1) forwards;
+    transition: all .3s cubic-bezier(0.175, 0.885, 0.32, 1.275);
+    box-shadow: 0 4px 16px rgba(0,0,0,0.1);
+}
+.rec-block:hover {
+    border-color: rgba(0,200,150,.4);
+    box-shadow: 0 8px 30px rgba(0,200,150,.15);
+    transform: translateY(-4px) scale(1.01);
 }
+@keyframes up { to { opacity: 1; transform: translateY(0); } }
 
-/* Controls Row */
-.control-panel {
-    background: transparent;
-    margin-bottom: 30px;
+.sc {
+    display: grid; grid-template-columns: 130px 1fr;
+    text-decoration: none !important; color: inherit !important;
 }
 
-.control-panel .gr-box, .control-panel select {
-    background: var(--bg-panel) !important;
-    border: 1px solid var(--border-dim) !important;
-    border-radius: 8px !important;
+.sc-poster {
+    position: relative; overflow: hidden;
+    background: var(--bg3); min-height: 180px;
+}
+.sc-poster img {
+    width: 100%; height: 100%; object-fit: cover;
+    display: block; transition: transform .5s cubic-bezier(0.16, 1, 0.3, 1);
+}
+.sc:hover .sc-poster img { transform: scale(1.06); }
+
+.sc-badge {
+    position: absolute; top: 10px; left: 10px;
+    background: rgba(7,12,18,.85); backdrop-filter: blur(8px);
+    font-size: 12px; font-weight: 800; padding: 4px 10px;
+    border-radius: 100px; border: 1px solid rgba(255,255,255,.15);
+    box-shadow: 0 2px 8px rgba(0,0,0,0.3);
 }
 
-label {
-    font-family: 'Outfit', sans-serif !important;
-    font-size: 12px !important;
-    color: var(--text-muted) !important;
-    text-transform: uppercase;
-    letter-spacing: 0.5px;
+.sc-body { padding: 16px; display: flex; flex-direction: column; gap: 8px; }
+
+.sc-chips { display: flex; gap: 6px; flex-wrap: wrap; margin-bottom: 2px; }
+.mc {
+    font-size: 10px; font-weight: 700; letter-spacing: .5px;
+    text-transform: uppercase; color: var(--acc);
+    background: var(--adim); border: 1px solid rgba(0,200,150,.25);
+    padding: 3px 8px; border-radius: 6px;
 }
 
-/* Output Dashboard Elements */
-.results-dashboard {
-    display: flex;
-    gap: 30px;
-    align-items: center;
-    background: var(--bg-panel);
-    border: 1px solid var(--border-dim);
-    padding: 16px 24px;
-    border-radius: 8px;
-    margin-bottom: 16px;
-    box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1);
-}
-
-.metric { display: flex; flex-direction: column; }
-.metric .label { font-family: 'Outfit', sans-serif; font-size: 11px; color: var(--text-muted); letter-spacing: 0.5px; }
-.metric .value { font-family: 'Outfit', sans-serif; font-size: 16px; font-weight: 600; color: #fff; }
-
-.status-badge {
-    margin-left: auto;
-    font-family: 'Outfit', sans-serif;
-    font-size: 12px;
-    padding: 6px 12px;
-    border-radius: 20px;
-    font-weight: 600;
-}
-
-.status-badge.success { background: rgba(16, 185, 129, 0.1); color: #10b981; }
-.status-badge.error { background: rgba(239, 68, 68, 0.1); color: #ef4444; }
-.status-badge.warning { background: rgba(245, 158, 11, 0.1); color: #f59e0b; }
-
-/* Markdown Styling for LLM Output */
-.markdown-output {
-    background: transparent;
-    font-size: 16px;
-    line-height: 1.8;
-    color: var(--text-main);
-}
-.markdown-output h1, .markdown-output h2, .markdown-output h3 { font-family: 'Outfit', sans-serif; color: #fff; margin-top: 24px; margin-bottom: 12px; }
-.markdown-output strong { color: #a855f7; font-weight: 600; }
-.markdown-output a { color: var(--accent); text-decoration: none; }
-.markdown-output a:hover { text-decoration: underline; }
-
-/* Quick Tags */
-.quick-tags { display: flex; gap: 10px; flex-wrap: wrap; margin-top: 12px; justify-content: center; }
-.quick-tag {
-    font-size: 13px;
-    color: var(--text-muted);
-    background: var(--bg-panel);
-    border: 1px solid var(--border-dim);
-    padding: 6px 14px;
-    border-radius: 20px;
-    cursor: pointer;
-    transition: all 0.2s;
+.sc-title { font-size: 17px; font-weight: 800; color: #fff; line-height: 1.2; letter-spacing: -.3px; }
+.sc-eng    { font-size: 12px; color: #a0b2c6; font-weight: 600; font-style: italic; margin-top: -2px; }
+.sc-studio { font-size: 12px; color: #8a9bb0; font-weight: 600; }
+.sc-synopsis { font-size: 12.5px; color: #dde3f0; line-height: 1.6; flex: 1; margin-top: 6px; }
+
+.pill-row { display: flex; gap: 6px; flex-wrap: wrap; margin-top: 12px; }
+.pill {
+    font-size: 12px; font-weight: 600; color: var(--txt);
+    background: var(--bg3); border: 1px solid var(--bdr); 
+    padding: 4px 12px; border-radius: 100px;
+}
+
+.mal-btn {
+    display: inline-block; margin-top: 14px;
+    font-size: 12px; font-weight: 700; color: var(--acc);
+    background: var(--adim); border: 1px solid rgba(0,200,150,.3);
+    padding: 8px 16px; border-radius: 8px; letter-spacing: .5px;
+    text-transform: uppercase; align-self: flex-start; 
+    transition: all .2s ease;
+}
+.sc:hover .mal-btn { 
+    background: var(--acc); color: #000; 
+    transform: translateY(-2px);
+    box-shadow: 0 4px 12px rgba(0,200,150,0.3);
+}
+
+.llm-pitch {
+    padding: 14px 16px; font-size: 14px; color: #fff;
+    line-height: 1.6; border-top: 1px solid var(--bdr);
+    background: rgba(255,255,255,0.02);
+}
+
+/* ── RETRIEVED SIDEBAR ───────────────────────────────────────── */
+.sidebar {
+    background: var(--bg2); border: 1px solid var(--bdr);
+    border-radius: 16px; padding: 18px;
+    position: sticky; top: 110px;
+    max-height: calc(100vh - 130px);
+    overflow-y: auto; scrollbar-width: thin;
+    scrollbar-color: var(--mut) transparent;
+    box-shadow: 0 4px 16px rgba(0,0,0,0.1);
+}
+
+.sidebar-head {
+    display: flex; justify-content: space-between; align-items: center;
+    margin-bottom: 16px; padding-bottom: 12px; border-bottom: 1px solid var(--bdr);
+}
+.sidebar-label {
+    font-size: 11px; font-weight: 700; letter-spacing: 1px;
+    text-transform: uppercase; color: var(--mut);
+}
+.sidebar-count {
+    font-size: 12px; font-weight: 700; color: var(--acc);
+    background: var(--adim); padding: 3px 10px; border-radius: 100px;
+}
+
+.rr-list { display: flex; flex-direction: column; gap: 4px; }
+.rr {
+    display: grid; grid-template-columns: 22px 44px 1fr auto;
+    gap: 12px; align-items: center; padding: 8px;
+    border-radius: 10px; text-decoration: none !important;
+    color: inherit !important; transition: all .2s;
+}
+.rr:hover { 
+    background: var(--bg3); 
+    transform: translateX(4px);
+}
+
+.rr-idx { font-size: 11px; font-weight: 700; color: var(--mut); text-align: center; }
+.rr-thumb {
+    width: 44px; height: 62px; object-fit: cover;
+    border-radius: 6px; background: var(--bg3); display: block;
+}
+.rr-info { display: flex; flex-direction: column; gap: 4px; overflow: hidden; }
+.rr-title {
+    font-size: 13px; font-weight: 700; color: #fff;
+    white-space: nowrap; overflow: hidden; text-overflow: ellipsis;
+}
+.rr-sub {
+    font-size: 11px; font-weight: 500; color: #8a9bb0;
+    white-space: nowrap; overflow: hidden; text-overflow: ellipsis;
 }
-.quick-tag:hover {
-    color: #fff;
-    border-color: var(--accent);
-    box-shadow: 0 0 10px rgba(99, 102, 241, 0.2);
+.rr-bar-bg { height: 4px; background: var(--bg3); border-radius: 4px; overflow: hidden; margin-top: 2px; }
+.rr-bar-fill {
+    height: 100%;
+    background: linear-gradient(90deg, var(--acc), var(--acc2));
+    border-radius: 4px;
+}
+.rr-right { display: flex; flex-direction: column; align-items: flex-end; gap: 4px; }
+.rr-score { font-size: 12px; font-weight: 700; color: var(--amb); }
+.rr-pct   { font-size: 11px; font-weight: 600; color: var(--mut); }
+
+/* ── RESPONSIVE DESIGN ───────────────────────────────────────── */
+@media (max-width: 1024px) {
+    .main-layout { grid-template-columns: 1fr; }
+    .sidebar { position: relative; top: 0; max-height: none; margin-top: 24px; }
+}
+@media (max-width: 860px) {
+    #anifind-sidebar { width: 100%; position: relative; height: auto; border-right: none; border-bottom: 1px solid var(--bdr); }
+    #main-content { margin-left: 0; width: 100%; }
+    .sc { grid-template-columns: 1fr; }
+    .sc-poster { min-height: 200px; }
 }
 """
 
-JS_INTERACTION = """
+# =============================================================================
+#  JAVASCRIPT (INJECTED VIA HEAD TO GUARANTEE EXECUTION)
+# =============================================================================
+JS = """
 <script>
-(function() {
-    // 1. Handle clicking the quick suggestion tags
-    document.addEventListener('click', (e) => {
-        const tag = e.target.closest('.quick-tag');
-        if (tag) {
-            const text = tag.getAttribute('data-text');
-            const textarea = document.querySelector('textarea');
-            if (textarea && text) {
-                textarea.value = text;
-                textarea.dispatchEvent(new Event('input', { bubbles: true }));
-                
-                // Find and click the send button
-                let btn = document.querySelector('#send-btn');
-                if (btn && btn.tagName !== 'BUTTON') btn = btn.querySelector('button') || btn;
+function initAniFindJS() {
+    document.body.addEventListener('click', function(e) {
+        var chip = e.target.closest('.chip');
+        if (!chip) return;
+        var q = chip.getAttribute('data-q');
+        var ta = document.querySelector('#query-input textarea');
+        if (ta && q) {
+            // Svelte/React native value setter trigger
+            var setter = Object.getOwnPropertyDescriptor(window.HTMLTextAreaElement.prototype, 'value').set;
+            setter.call(ta, q);
+            ta.dispatchEvent(new Event('input', { bubbles: true }));
+            
+            setTimeout(function() {
+                var btn = document.querySelector('#send-btn button');
                 if (btn) btn.click();
-            }
+            }, 100);
         }
     });
 
-    // 2. Override Gradio's internal key bindings
-    document.addEventListener('keydown', (e) => {
-        // Target our specific textarea
-        if (e.target.tagName.toLowerCase() === 'textarea') {
-            
-            if (e.key === 'Enter') {
-                
-                if (e.shiftKey) {
-                    // SHIFT + ENTER: We want a new line.
-                    // Stop Gradio from seeing this and triggering its default "Submit"
-                    e.stopPropagation();
-                    e.stopImmediatePropagation();
-                    // We DO NOT preventDefault, so the browser natively adds the new line.
-                } 
-                else if (!e.ctrlKey && !e.metaKey) {
-                    // PLAIN ENTER: We want to submit.
-                    // Stop the browser from making a new line
-                    e.preventDefault();
-                    // Stop Gradio from seeing this event at all
-                    e.stopPropagation();
-                    e.stopImmediatePropagation();
-                    
-                    // Click our send button
-                    let btn = document.querySelector('#send-btn');
-                    if (btn && btn.tagName !== 'BUTTON') btn = btn.querySelector('button') || btn;
-                    if (btn) btn.click();
-                }
-            }
+    document.body.addEventListener('keydown', function(e) {
+        if (e.target.tagName.toLowerCase() !== 'textarea') return;
+        if (e.key === 'Enter' && !e.shiftKey && !e.ctrlKey && !e.metaKey) {
+            e.preventDefault();
+            e.stopPropagation();
+            var btn = document.querySelector('#send-btn button');
+            if (btn) btn.click();
         }
-    }, true); // Use the capture phase to intercept before React/Gradio does
-})();
+    }, true);
+}
+
+// Ensure the script runs after DOM is ready
+if (document.readyState === "loading") {
+    document.addEventListener("DOMContentLoaded", initAniFindJS);
+} else {
+    initAniFindJS();
+}
 </script>
 """
 
+# =============================================================================
+#  GRADIO INTERFACE
+# =============================================================================
+
 
 def create_interface():
-    with gr.Blocks(title="Anime Recommender") as demo:
+    chips_html = "".join(
+        f"<button class='chip' data-q='{s['query']}'>{s['label']}</button>"
+        for s in SUGGESTIONS
+    )
 
-        gr.HTML("""
-        <div class="app-header">
-            <h1 class="app-title">Find your next <span>anime</span>.</h1>
-            <p class="app-subtitle">Describe the exact vibe, mood, or story you're looking for.</p>
-            
-            <div class="quick-tags">
-                <div class="quick-tag" data-text="Cozy slice of life set in the countryside">Cozy Countryside</div>
-                <div class="quick-tag" data-text="Dark fantasy with an unreliable narrator">Dark Fantasy</div>
-                <div class="quick-tag" data-text="Fast-paced cyberpunk action with great animation">Cyberpunk Action</div>
-            </div>
-        </div>
-        """)
-
-        # 🔥 NEW UNIFIED INPUT BAR 🔥
-        with gr.Row(elem_id="chat-input-container"):
-            query_input = gr.Textbox(
-                label="",
-                show_label=False,
-                lines=2,
-                placeholder="Message the recommender... (e.g., A melancholic sci-fi)",
-                elem_id="query-input",
-                scale=10
-            )
+    sidebar_html = (
+        "<div id='anifind-sidebar'>"
+        "<p class='sb-tagline'>Find your next<br><em>favourite anime.</em></p>"
+        "<p class='sb-sub'>Describe a vibe, a mood, a feeling — we'll match it to the perfect title.</p>"
+        "<div class='sb-sect'>Quick searches</div>"
+        f"<div class='chips-col'>{chips_html}</div>"
+        "</div>"
+    )
 
-            # Using an upward arrow icon (↑) for the send button
-            submit_btn = gr.Button(
-                "➔",
-                elem_id="send-btn",
-                scale=1
-            )
+    hero_html = (
+        "<div class='hero-brand'>"
+        "<div class='hero-logo-row'>"
+        "<div class='hero-icon'>&#x26E9;</div>"
+        "<span class='hero-name'>Ani<em>Find</em></span>"
+        "</div>"
+        "</div>"
+    )
 
-        with gr.Group(elem_classes="control-panel"):
-            with gr.Row():
-                genre_dropdown = gr.Dropdown(
-                    choices=["Any", "Action", "Sci-Fi", "Slice of Life",
-                             "Fantasy", "Psychological", "Horror", "Romance"],
-                    value="Any",
-                    label="Genre Filter",
-                )
-                min_score_slider = gr.Slider(
-                    minimum=0, maximum=10, value=0, step=0.5,
-                    label="Minimum Rating"
-                )
-                n_results_slider = gr.Slider(
-                    minimum=1, maximum=8, value=3, step=1,
-                    label="Number of Results"
-                )
+    # Added `head=JS` to properly inject the event listeners globally
+    with gr.Blocks(title="AniFind", head=JS) as demo:
 
-        output_header = gr.HTML(
-            value="<div class='results-dashboard'><div class='status-badge warning'>Waiting for input</div></div>"
-        )
+        gr.HTML(sidebar_html)
 
-        output_text = gr.Markdown(
-            value="",
-            elem_classes="markdown-output"
-        )
+        with gr.Column(elem_id="main-content"):
 
-        gr.HTML(JS_INTERACTION)
+            gr.HTML(hero_html)
 
-        submit_btn.click(
-            fn=get_recommendations,
-            inputs=[query_input, min_score_slider,
-                    genre_dropdown, n_results_slider],
-            outputs=[output_header, output_text]
-        )
+            with gr.Row(elem_id="search-zone"):
+                query_input = gr.Textbox(
+                    label="", show_label=False, lines=2,
+                    placeholder="e.g. A melancholic sci-fi about memory and loss...",
+                    elem_id="query-input", scale=10,
+                )
+                submit_btn = gr.Button("➔", elem_id="send-btn", scale=1)
+
+            with gr.Row(elem_id="filter-bar"):
+                anime_type_dd = gr.Dropdown(
+                    choices=ANIME_TYPES, value="Any", label="Type", scale=1)
+                genre_dd = gr.Dropdown(
+                    choices=GENRES, value=["Any"], label="Genre",
+                    multiselect=True, scale=2)
+                score_slider = gr.Slider(
+                    minimum=0, maximum=10, value=0, step=0.5,
+                    label="Min Rating", scale=1)
+                n_slider = gr.Slider(
+                    minimum=1, maximum=8, value=3, step=1,
+                    label="Results", scale=1)
 
-        # Allow native submission as a fallback
-        query_input.submit(
-            fn=get_recommendations,
-            inputs=[query_input, min_score_slider,
-                    genre_dropdown, n_results_slider],
-            outputs=[output_header, output_text]
-        )
+            with gr.Column(elem_id="output-zone"):
+                output_header = gr.HTML(
+                    value="<div class='hud'><div class='hud-pill idle'>Waiting for your prompt...</div></div>"
+                )
+                output_body = gr.HTML(value="")
+
+        ins = [query_input, score_slider, genre_dd, anime_type_dd, n_slider]
+        outs = [output_header, output_body]
+        submit_btn.click(fn=get_recommendations, inputs=ins, outputs=outs)
+        query_input.submit(fn=get_recommendations, inputs=ins, outputs=outs)
 
-        return demo
+    return demo
 
 
 if __name__ == "__main__":
     demo = create_interface()
 
     theme = themes.Base(
-        font=[themes.GoogleFont("Inter"), "ui-sans-serif",
-              "system-ui", "sans-serif"],
+        font=[themes.GoogleFont("Plus Jakarta Sans"),
+              "ui-sans-serif", "sans-serif"],
     ).set(
         body_background_fill="transparent",
         block_background_fill="transparent",
         block_border_width="0px",
         input_background_fill="transparent",
+        border_color_primary="transparent",
+        shadow_drop="none",
+        shadow_drop_lg="none",
     )
 
     demo.launch(
         server_name="0.0.0.0",
         server_port=7860,
-        share=False,
+        share=True,
         css=CSS,
-        theme=theme
+        theme=theme,
     )

uv.lock

@@ -54,7 +54,7 @@ dev = [
 
 [package.metadata]
 requires-dist = [
-    { name = "chromadb", specifier = ">=0.5.0" },
+    { name = "chromadb", specifier = ">=1.5.2" },
     { name = "fastapi", extras = ["all"], specifier = ">=0.122.0" },
     { name = "gradio", specifier = ">=6.2.0" },
     { name = "langchain", specifier = ">=1.1.3" },
@@ -375,25 +375,16 @@ wheels = [
 
 [[package]]
 name = "build"
-version = "1.3.0"
+version = "1.4.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "colorama", marker = "(os_name == 'nt' and platform_machine != 'aarch64' and sys_platform == 'linux') or (os_name == 'nt' and platform_python_implementation != 'CPython' and sys_platform == 'linux') or (os_name == 'nt' and sys_platform != 'darwin' and sys_platform != 'linux')" },
     { name = "packaging" },
     { name = "pyproject-hooks" },
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-sdist = { url = "https://files.pythonhosted.org/packages/25/1c/23e33405a7c9eac261dff640926b8b5adaed6a6eb3e1767d441ed611d0c0/build-1.3.0.tar.gz", hash = "sha256:698edd0ea270bde950f53aed21f3a0135672206f3911e0176261a31e0e07b397", size = 48544, upload-time = "2025-08-01T21:27:09.268Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/cb/8c/2b30c12155ad8de0cf641d76a8b396a16d2c36bc6d50b621a62b7c4567c1/build-1.3.0-py3-none-any.whl", hash = "sha256:7145f0b5061ba90a1500d60bd1b13ca0a8a4cebdd0cc16ed8adf1c0e739f43b4", size = 23382, upload-time = "2025-08-01T21:27:07.844Z" },
-]
-
-[[package]]
-name = "cachetools"
-version = "6.2.4"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/bc/1d/ede8680603f6016887c062a2cf4fc8fdba905866a3ab8831aa8aa651320c/cachetools-6.2.4.tar.gz", hash = "sha256:82c5c05585e70b6ba2d3ae09ea60b79548872185d2f24ae1f2709d37299fd607", size = 31731, upload-time = "2025-12-15T18:24:53.744Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/42/18/94eaffda7b329535d91f00fe605ab1f1e5cd68b2074d03f255c7d250687d/build-1.4.0.tar.gz", hash = "sha256:f1b91b925aa322be454f8330c6fb48b465da993d1e7e7e6fa35027ec49f3c936", size = 50054, upload-time = "2026-01-08T16:41:47.696Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/c5/0d/84a4380f930db0010168e0aa7b7a8fed9ba1835a8fbb1472bc6d0201d529/build-1.4.0-py3-none-any.whl", hash = "sha256:6a07c1b8eb6f2b311b96fcbdbce5dab5fe637ffda0fd83c9cac622e927501596", size = 24141, upload-time = "2026-01-08T16:41:46.453Z" },
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 [[package]]
@@ -512,7 +503,7 @@ wheels = [
 
 [[package]]
 name = "chromadb"
-version = "1.3.7"
+version = "1.5.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "bcrypt" },
@@ -543,13 +534,13 @@ dependencies = [
     { name = "typing-extensions" },
     { name = "uvicorn", extra = ["standard"] },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/a9/b9/23eb242c0bad56bcac57d9f45a6cc85e016a44ae9baf763c0d040e45e2d7/chromadb-1.3.7.tar.gz", hash = "sha256:393b866b6ac60c12fc0f2a43d07b2884f2d02a68a1b2cb43c5ef87d141543571", size = 1960950, upload-time = "2025-12-12T21:03:13.941Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/9e/48/aa5906f9f817b73c9e87e085d3a64705d91b7bb4f76f4649b9379baea980/chromadb-1.5.2.tar.gz", hash = "sha256:4fc3535a0fcd45343f93d298591882f68e659f24ed319aef14094b168105f956", size = 2386235, upload-time = "2026-02-27T19:49:34.167Z" }
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-    { url = "https://files.pythonhosted.org/packages/51/3e/0fbb4c6e7971019c976cf3dbef1c22c1a3089f74ef86c88e2e066edc47e4/chromadb-1.3.7-cp39-abi3-macosx_11_0_arm64.whl", hash = "sha256:fe9c96f73450274d9f722572afc9d455b4f6f4cd960fa49e4bf489075ef30e6f", size = 20113076, upload-time = "2025-12-12T21:03:07.873Z" },
-    { url = "https://files.pythonhosted.org/packages/69/78/2ae4064c9b194271b9c2bc66a26a7e11363d13ed2bd691a563fac1a7c5f2/chromadb-1.3.7-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:972cb168033db76a4bb1031bc38b6cc4e6d05ef716c1ffce8ae95a1a3b515dd2", size = 20738619, upload-time = "2025-12-12T21:03:01.409Z" },
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+    { url = "https://files.pythonhosted.org/packages/6b/3b/36989e7ebfa2ee10a85deacd423989b07f9e3bd176846863ace1305e9460/chromadb-1.5.2-cp39-abi3-macosx_10_12_x86_64.whl", hash = "sha256:a898ab200f9a22a16751eed5444dac330f1f82184264e16d5420e41e0afe63e4", size = 20733964, upload-time = "2026-02-27T19:49:31.683Z" },
+    { url = "https://files.pythonhosted.org/packages/85/b3/db3e5a8a47106d339c3e109e73859647969a81e9c54ca15bc6dde6685c1e/chromadb-1.5.2-cp39-abi3-macosx_11_0_arm64.whl", hash = "sha256:8e6a12adb34bf441f8cc368b6460fbc9e14bee5cf926f34e752da759d68dec56", size = 19993688, upload-time = "2026-02-27T19:49:28.5Z" },
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+    { url = "https://files.pythonhosted.org/packages/ae/74/b8cd9d9bc72c545a579fd1f7bb44558a801ed5f5bab164a25eea16d51ad9/chromadb-1.5.2-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e48e5b0f300d6f709446a5d9299614e3b6bca997772d810e1298b76b0c4e7dbb", size = 21529269, upload-time = "2026-02-27T19:49:25.699Z" },
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 [[package]]
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-[[package]]
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-dependencies = [
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@@ -846,11 +825,10 @@ wheels = [
 
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