Update app.py

app.py

@@ -36,96 +36,84 @@ def convert_graph_to_html(graph, full_html=False):
 @app.route("/", methods=["GET", "POST"])
 def index():
     video_details = None
-    k_distance_graph = None
-    scores_graph = None
     sankey_graph = None
+    scores_graph = None
     image_path = None
     sentiment_daily_graph = None
     sentiment_count = None
+    error_message = None
 
     current_directory = os.getcwd()
     log_message("Iniciando procesamiento...")
 
     if request.method == "POST":
-        url = request.form["url"]
-        if url:
-            log_message("Obteniendo datos de Youtube")
-            video_details = clustering.get_youtube_video_details(url, api_key)
-            comments_df = clustering.get_youtube_comments(api_key, url)
-            log_message("Generando embeddings")
-            comments_df = clustering.add_normalized_embeddings_to_dataframe(
-                comments_df, "comment"
-            )
-            log_message("Procesamiento de los datos")
-            comments_df["published_at"] = pd.to_datetime(
-                comments_df["published_at"]
-            ).dt.date
-            log_message("Clasificación de los sentimientos")
-            comments_df = clustering.classify_sentiment_df(comments_df)
-            comments_df.to_pickle(
-                "./data/Comentarios-Youtube/comments_df.pkl"
-            )
-            comments_df = pd.read_pickle(
-                "./data/Comentarios-Youtube/comments_df.pkl"
-            )
-            sentiment_count = comments_df["sentimiento"].value_counts().to_dict()
-            sentiment_daily_graph = clustering.plot_sentiment_daily(comments_df)
-
-            sentiment_daily_graph = convert_graph_to_html(sentiment_daily_graph)
-
-            umap_df, min_eps, max_eps = clustering.transform_embeddings(
-                comments_df, embeddings_col="embeddings"
-            )
-            log_message("Generación de wordcloud")
-            image_path = os.path.join("static", "wordcloud.png")
-            clustering.plot_wordcloud(comments_df, text_column="comment", output_filename=image_path)
-
-            total = comments_df.shape[0]
-            
-            min_items_by_cluster = clustering.determine_min_items_by_cluster(total)
-            log_message("Modelado y generación de métricas")
-            (
-                cluster_assignments,
-                cluster_counts,
-                calinski_harabasz_scores,
-                silhouette_scores,
-                most_similar_comments,
-                umap_df,
-            ) = clustering.perform_clustering(
-                umap_df, min_eps, max_eps, n=10, 
-                embeddings_col="embeddings"
-            )
-            log_message(f"Clusters assignments {cluster_assignments}")
-            log_message("Creación de gráfico de Sankey")
-            labels, source, target, values, comments = clustering.build_sankey_data(
-                cluster_assignments,
-                cluster_counts,
-                most_similar_comments,
-                min_items_by_cluster=min_items_by_cluster,
-            )
-
-            sankey_graph = clustering.plot_sankey(
-                labels, source, target, values, comments, height=1000, width=1200
-            )
-            sankey_graph = convert_graph_to_html(sankey_graph)
-
-            scores_graph, _ = clustering.plot_clustering_metric(
-                silhouette_scores, calinski_harabasz_scores
-            )
-            scores_graph = convert_graph_to_html(scores_graph)
+        url = request.form.get("url")  # Utiliza get para evitar KeyError
+        if not url:
+            error_message = "La URL es requerida."
+            return render_template("index.html", error_message=error_message)
+
+        log_message("Obteniendo datos de Youtube")
+        video_details = clustering.get_youtube_video_details(url, api_key)
+        if "error" in video_details:  # Manejo de error al obtener detalles del video
+            error_message = video_details["error"]
+            return render_template("index.html", error_message=error_message)
+
+        comments_df = clustering.get_youtube_comments(api_key, url)
+        if comments_df is None:  # Verifica si no hay comentarios
+            error_message = "No se pudieron obtener comentarios."
+            return render_template("index.html", error_message=error_message)
+
+        log_message("Generando embeddings")
+        comments_df = clustering.add_normalized_embeddings_to_dataframe(comments_df, "comment")
+        
+        log_message("Procesamiento de los datos")
+        comments_df["published_at"] = pd.to_datetime(comments_df["published_at"]).dt.date
+        
+        log_message("Clasificación de los sentimientos")
+        comments_df = clustering.classify_sentiment_df(comments_df)
+        comments_df.to_pickle("./data/Comentarios-Youtube/comments_df.pkl")
+        
+        sentiment_count = comments_df["sentimiento"].value_counts().to_dict()
+        sentiment_daily_graph = clustering.plot_sentiment_daily(comments_df)
+        sentiment_daily_graph = convert_graph_to_html(sentiment_daily_graph)
+
+        umap_df, min_eps, max_eps = clustering.transform_embeddings(comments_df, embeddings_col="embeddings")
+
+        log_message("Generación de wordcloud")
+        image_path = os.path.join("static", "wordcloud.png")
+        clustering.plot_wordcloud(comments_df, text_column="comment", output_filename=image_path)
+
+        total = comments_df.shape[0]
+        min_items_by_cluster = clustering.determine_min_items_by_cluster(total)
+
+        log_message("Modelado y generación de métricas")
+        (cluster_assignments, cluster_counts, calinski_harabasz_scores, silhouette_scores, most_similar_comments, umap_df) = clustering.perform_clustering(
+            umap_df, min_eps, max_eps, n=10, embeddings_col="embeddings"
+        )
+
+        log_message(f"Clusters assignments {cluster_assignments}")
+        log_message("Creación de gráfico de Sankey")
+        labels, source, target, values, comments = clustering.build_sankey_data(
+            cluster_assignments, cluster_counts, most_similar_comments, min_items_by_cluster=min_items_by_cluster
+        )
+
+        sankey_graph = clustering.plot_sankey(labels, source, target, values, comments, height=1000, width=1200)
+        sankey_graph = convert_graph_to_html(sankey_graph)
+
+        scores_graph, _ = clustering.plot_clustering_metric(silhouette_scores, calinski_harabasz_scores)
+        scores_graph = convert_graph_to_html(scores_graph)
 
     return render_template(
         "index.html",
         video_details=video_details,
-        k_distance_graph=k_distance_graph,
         sankey_graph=sankey_graph,
         scores_graph=scores_graph,
         wordcloud_path=image_path,
         sentiment_daily_graph=sentiment_daily_graph,
         sentiment_count=sentiment_count,
+        error_message=error_message,  # Incluye el mensaje de error si existe
     )
 
-
 #  gunicorn -b 0.0.0.0:5000 app_clustering.app:app
 # http://172.20.0.2:5000/
 # http://0.0.0.0:5000/