Update app.py

app.py

@@ -1,78 +1,78 @@
-import pandas as pd
-from langchain_chroma import Chroma
-from langchain_huggingface import HuggingFaceEmbeddings
-from sentence_transformers import CrossEncoder
-import gradio as gr
-import torch
-
-# 🔹 Pfade
-CHROMA_DIR = r"C:\Unkram123\Full_stack_projects\erkam_netflix\FINALE\chroma_movies_bge"  # Lokaler Chroma-Ordner
-CSV_PATH = r"C:\Unkram123\Full_stack_projects\erkam_netflix\FINALE\FINALE_NEW_WITH_IMAGES.csv"  # CSV mit Poster URLs
-PLACEHOLDER_IMAGE = r"C:\Unkram123\Full_stack_projects\erkam_netflix\FINALE\placeholder.png"  # Falls Poster fehlt
-
-# 🔹 CSV laden
-movies = pd.read_csv(CSV_PATH, encoding="utf-8")
-movies["doc_id"] = movies.index
-
-# 🔹 CUDA Check
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-# 🔹 Embeddings laden
-embeddings = HuggingFaceEmbeddings(
-    model_name="BAAI/bge-m3",
-    model_kwargs = {"device": device},
-    encode_kwargs={"normalize_embeddings": True}
-)
-
-# 🔹 Chroma-Index laden
-db_movies = Chroma(
-    persist_directory=CHROMA_DIR,
-    embedding_function=embeddings
-)
-
-# 🔹 Cross-Encoder laden
-reranker = CrossEncoder("cross-encoder/stsb-roberta-large", device=device)
-
-# 🔹 Funktion für semantische Empfehlungen
-def retrieve_semantic_recommendations(query: str, top_k: int = 10):
-    recs = db_movies.similarity_search(query, k=50)
-    pairs = [(query, rec.page_content) for rec in recs]
-    scores = reranker.predict(pairs, batch_size=8)
-
-    scores_dict = {}
-    for rec, score in zip(recs, scores):
-        doc_id = rec.metadata["doc_id"]
-        if doc_id not in scores_dict or score > scores_dict[doc_id]["score"]:
-            scores_dict[doc_id] = {"score": score, "rec": rec}
-
-    unique_top_recs = sorted(scores_dict.values(), key=lambda x: x["score"], reverse=True)[:top_k]
-
-    gallery = []
-    for item in unique_top_recs:
-        rec = item["rec"]
-        metadata = rec.metadata
-
-        # Poster aus CSV anhand doc_id
-        movie_row = movies[movies["doc_id"] == metadata["doc_id"]].iloc[0]
-        cover_url = movie_row.get("poster_url", PLACEHOLDER_IMAGE)
-
-        label = f"{movie_row['title']}\n\n{movie_row['description']}"
-        gallery.append((cover_url, label))
-
-    return gallery
-
-# 🔹 Gradio-App
-with gr.Blocks(theme=gr.themes.Glass()) as demo:
-    gr.Markdown("# 🎬 Semantic Movie Recommender (Online Posters)")
-
-    with gr.Row():
-        user_query = gr.Textbox(label="Describe your ideal movie", placeholder="e.g., A sci-fi movie about time travel")
-        submit_button = gr.Button("🔍 Find recommendations")
-
-    gr.Markdown("## 🍿 Recommended Movies")
-    output = gr.Gallery(label="Movies", columns=3, rows=3, show_label=True)
-
-    submit_button.click(fn=retrieve_semantic_recommendations, inputs=user_query, outputs=output)
-
-if __name__ == "__main__":
-    demo.launch()
+import pandas as pd
+from langchain_chroma import Chroma
+from langchain_huggingface import HuggingFaceEmbeddings
+from sentence_transformers import CrossEncoder
+import gradio as gr
+import torch
+
+# 🔹 Pfade
+CHROMA_DIR = "chroma_movies_bge"  # Lokaler Chroma-Ordner
+CSV_PATH = "FINALE_NEW_WITH_IMAGES.csv"  # CSV mit Poster URLs
+PLACEHOLDER_IMAGE = "placeholder.png"  # Falls Poster fehlt
+
+# 🔹 CSV laden
+movies = pd.read_csv(CSV_PATH, encoding="utf-8")
+movies["doc_id"] = movies.index
+
+# 🔹 CUDA Check
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# 🔹 Embeddings laden
+embeddings = HuggingFaceEmbeddings(
+    model_name="BAAI/bge-m3",
+    model_kwargs = {"device": device},
+    encode_kwargs={"normalize_embeddings": True}
+)
+
+# 🔹 Chroma-Index laden
+db_movies = Chroma(
+    persist_directory=CHROMA_DIR,
+    embedding_function=embeddings
+)
+
+# 🔹 Cross-Encoder laden
+reranker = CrossEncoder("cross-encoder/stsb-roberta-large", device=device)
+
+# 🔹 Funktion für semantische Empfehlungen
+def retrieve_semantic_recommendations(query: str, top_k: int = 10):
+    recs = db_movies.similarity_search(query, k=50)
+    pairs = [(query, rec.page_content) for rec in recs]
+    scores = reranker.predict(pairs, batch_size=8)
+
+    scores_dict = {}
+    for rec, score in zip(recs, scores):
+        doc_id = rec.metadata["doc_id"]
+        if doc_id not in scores_dict or score > scores_dict[doc_id]["score"]:
+            scores_dict[doc_id] = {"score": score, "rec": rec}
+
+    unique_top_recs = sorted(scores_dict.values(), key=lambda x: x["score"], reverse=True)[:top_k]
+
+    gallery = []
+    for item in unique_top_recs:
+        rec = item["rec"]
+        metadata = rec.metadata
+
+        # Poster aus CSV anhand doc_id
+        movie_row = movies[movies["doc_id"] == metadata["doc_id"]].iloc[0]
+        cover_url = movie_row.get("poster_url", PLACEHOLDER_IMAGE)
+
+        label = f"{movie_row['title']}\n\n{movie_row['description']}"
+        gallery.append((cover_url, label))
+
+    return gallery
+
+# 🔹 Gradio-App
+with gr.Blocks(theme=gr.themes.Glass()) as demo:
+    gr.Markdown("# 🎬 Semantic Movie Recommender (Online Posters)")
+
+    with gr.Row():
+        user_query = gr.Textbox(label="Describe your ideal movie", placeholder="e.g., A sci-fi movie about time travel")
+        submit_button = gr.Button("🔍 Find recommendations")
+
+    gr.Markdown("## 🍿 Recommended Movies")
+    output = gr.Gallery(label="Movies", columns=3, rows=3, show_label=True)
+
+    submit_button.click(fn=retrieve_semantic_recommendations, inputs=user_query, outputs=output)
+
+if __name__ == "__main__":
+    demo.launch()