Update app.py

app.py

@@ -1,14 +1,28 @@
-# app.py
-
 import os
-os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
-os.environ['KMP_DUPLICATE_LIB_OK']='TRUE'
-
 import streamlit as st
 import spacy
 from spacy import displacy
 import re
 from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
+from azure.cosmos import CosmosClient
+from azure.cosmos.exceptions import CosmosHttpResponseError
+from pymongo import MongoClient
+import numpy as np
+
+# Azure Cosmos DB configuration
+cosmos_endpoint = "YOUR_COSMOS_DB_ENDPOINT"
+cosmos_key = "YOUR_COSMOS_DB_KEY"
+cosmos_client = CosmosClient(cosmos_endpoint, cosmos_key)
+
+# SQL API database for user management
+user_database = cosmos_client.get_database_client("user_database")
+user_container = user_database.get_container_client("users")
+
+# MongoDB API configuration for text analysis results
+mongo_connection_string = "YOUR_MONGODB_CONNECTION_STRING"
+mongo_client = MongoClient(mongo_connection_string)
+mongo_db = mongo_client['aideatext_db']
+analysis_collection = mongo_db['text_analysis']
 
 from modules.auth import register_user, authenticate_user, get_user_role
 from modules.morpho_analysis import get_repeated_words_colors, highlight_repeated_words, POS_COLORS, POS_TRANSLATIONS
@@ -22,13 +36,12 @@ st.set_page_config(
 )
 
 @st.cache_resource
-
 def load_chatbot_model():
     tokenizer = BlenderbotTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
     model = BlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-400M-distill")
     return tokenizer, model
 
-# Cargar el modelo del chatbot
+# Load the chatbot model
 chatbot_tokenizer, chatbot_model = load_chatbot_model()
 
 def get_chatbot_response(input_text):
@@ -44,6 +57,20 @@ def load_spacy_models():
         'fr': spacy.load("fr_core_news_lg")
     }
 
+def store_analysis_result(username, text, repeated_words, arc_diagram, network_diagram):
+    try:
+        analysis_collection.insert_one({
+            'username': username,
+            'text': text,
+            'repeated_words': repeated_words,
+            'arc_diagram': arc_diagram,
+            'network_diagram': network_diagram
+        })
+        return True
+    except Exception as e:
+        st.error(f"Error storing analysis result: {e}")
+        return False
+
 def login_page():
     st.title("Iniciar Sesión")
     username = st.text_input("Usuario")
@@ -64,14 +91,11 @@ def register_page():
     new_password = st.text_input("Nueva Contraseña", type='password')
     role = st.selectbox("Rol", ["Estudiante", "Profesor"])
     
-    # Aquí puedes añadir campos adicionales según el rol si lo deseas
     additional_info = {}
     if role == "Estudiante":
-        # Por ejemplo: additional_info['carrera'] = st.text_input("Carrera")
-        pass
+        additional_info['carrera'] = st.text_input("Carrera")
     elif role == "Profesor":
-        # Por ejemplo: additional_info['departamento'] = st.text_input("Departamento")
-        pass
+        additional_info['departamento'] = st.text_input("Departamento")
 
     if st.button("Registrarse"):
         if register_user(new_username, new_password, role, additional_info):
@@ -132,45 +156,45 @@ def main_app():
     # Use translations
     t = translations[lang_code]
 
-    # Crear dos columnas: una para el chat y otra para el análisis
+    # Create two columns: one for chat and one for analysis
     col1, col2 = st.columns([1, 2])
 
     with col1:
         st.markdown(f"### Chat con AIdeaText")
 
-        # Inicializar el historial de chat si no existe
+        # Initialize chat history if it doesn't exist
         if 'chat_history' not in st.session_state:
             st.session_state.chat_history = []
 
-        # Mostrar el historial de chat
+        # Display chat history
         for i, (role, text) in enumerate(st.session_state.chat_history):
             if role == "user":
                 st.text_area(f"Tú:", value=text, height=50, key=f"user_message_{i}", disabled=True)
             else:
                 st.text_area(f"AIdeaText:", value=text, height=50, key=f"bot_message_{i}", disabled=True)
 
-        # Campo de entrada para el usuario
+        # User input field
         user_input = st.text_input("Escribe tu mensaje aquí:")
 
         if st.button("Enviar"):
             if user_input:
-                # Añadir mensaje del usuario al historial
+                # Add user message to history
                 st.session_state.chat_history.append(("user", user_input))
                 
-                # Obtener respuesta del chatbot
+                # Get chatbot response
                 response = get_chatbot_response(user_input)
                 
-                # Añadir respuesta del chatbot al historial
+                # Add chatbot response to history
                 st.session_state.chat_history.append(("bot", response))
                 
-                # Limpiar el campo de entrada
+                # Clear input field
                 st.experimental_rerun()
 
     with col2: 
         st.markdown(f"### {t['title']}")
     
         if st.session_state.role == "Estudiante":
-            # Código para la interfaz del estudiante
+            # Student interface code
             if 'input_text' not in st.session_state:
                 st.session_state.input_text = ""
     
@@ -199,6 +223,7 @@ def main_app():
                     # Arc Diagram
                     with st.expander(t['arc_diagram'], expanded=True):
                         sentences = list(doc.sents)
+                        arc_diagrams = []
                         for i, sent in enumerate(sentences):
                             st.subheader(f"{t['sentence']} {i+1}")
                             html = displacy.render(sent, style="dep", options={"distance": 100})
@@ -206,16 +231,26 @@ def main_app():
                             html = re.sub(r'<svg[^>]*>', lambda m: m.group(0).replace('height="450"', 'height="300"'), html)
                             html = re.sub(r'<g [^>]*transform="translate\((\d+),(\d+)\)"', lambda m: f'<g transform="translate({m.group(1)},50)"', html)
                             st.write(html, unsafe_allow_html=True)
+                            arc_diagrams.append(html)
     
                     # Network graph
                     with st.expander(t['network_diagram'], expanded=True):
                         fig = visualize_syntax(sentence_input, nlp_models[lang_code], lang_code)
                         st.pyplot(fig)
+                        
+                    # Store analysis results
+                    store_analysis_result(
+                        st.session_state.username,
+                        sentence_input,
+                        highlighted_text,
+                        arc_diagrams,
+                        fig
+                    )
 
         elif st.session_state.role == "Profesor":
-            # Código para la interfaz del profesor
+            # Teacher interface code
             st.write("Bienvenido, profesor. Aquí podrás ver el progreso de tus estudiantes.")
-            # Añade aquí la lógica para mostrar el progreso de los estudiantes
+            # Add logic to display student progress
 
 def main():
     if 'logged_in' not in st.session_state: