Update app.py

app.py

@@ -1,3 +1,5 @@
+import logging
+logging.basicConfig(level=logging.INFO)
 import datetime
 import io
 import base64
@@ -6,11 +8,12 @@ import streamlit as st
 import spacy
 from spacy import displacy
 import re
+import numpy as np
 from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
 from azure.cosmos import CosmosClient
 from azure.cosmos.exceptions import CosmosHttpResponseError
 from pymongo import MongoClient
-import numpy as np
+
 from dotenv import load_dotenv
 load_dotenv()
 
@@ -99,18 +102,37 @@ def load_spacy_models():
         'fr': spacy.load("fr_core_news_lg")
     }
 
-def store_analysis_result(username, text, repeated_words, arc_diagram, network_diagram):
+def store_analysis_result(username, text, repeated_words, arc_diagrams, network_diagram):
     try:
-        analysis_collection.insert_one({
-            'username': username,
+        # Convertir el gráfico de matplotlib a base64
+        buffer = io.BytesIO()
+        network_diagram.savefig(buffer, format='png')
+        buffer.seek(0)
+        network_diagram_base64 = base64.b64encode(buffer.getvalue()).decode()
+
+        # Contar palabras repetidas por categoría gramatical
+        word_count = {}
+        for word, color in repeated_words.items():
+            category = POS_TRANSLATIONS.get(color, 'Otros')
+            word_count[category] = word_count.get(category, 0) + 1
+
+        # Crear el documento para MongoDB
+        analysis_document = {
+            'username': username,  # Este campo se usará como sharded key
+            'timestamp': datetime.datetime.utcnow(),
             'text': text,
-            'repeated_words': repeated_words,
-            'arc_diagram': arc_diagram,
-            'network_diagram': network_diagram
-        })
+            'word_count': word_count,
+            'arc_diagrams': arc_diagrams,
+            'network_diagram': network_diagram_base64
+        }
+
+        # Insertar el documento en la colección
+        result = analysis_collection.insert_one(analysis_document)
+
+        logging.info(f"Análisis guardado con ID: {result.inserted_id} para el usuario: {username}")
         return True
     except Exception as e:
-        st.error(f"Error storing analysis result: {e}")
+        logging.error(f"Error al guardar el análisis para el usuario {username}: {str(e)}")
         return False
 
 def login_page():