Update src/streamlit_app.py

src/streamlit_app.py

@@ -481,10 +481,10 @@ if page == t("home"):
     <div class='intro animate__fadeIn'>
         <p>{t('home_intro_1').format(author="<b>Ngoue David Roger Yannick</b>")}</p>
         <p>{t('home_intro_2')}</p>
-        </div>
+    </div>
     """, unsafe_allow_html=True)
 
-    st.markdown(f"<div class='section-header section__fadeIn'>{t('why_omics')}</div>", unsafe_allow_html=True)
+    st.markdown(f"<div class='section-header'>{t('why_omics')}</div>", unsafe_allow_html=True)
     st.markdown(f"""
     <div class='content animate__fadeIn'>
         <p>{t('omics_description')}</p>
@@ -494,8 +494,8 @@ if page == t("home"):
             <li><b>{t('proteomics')}</b>: {t('proteomics_desc')}</li>
             <li><b>{t('metabolomics')}</b>: {t('metabolomics_desc')}</li>
         </ul>
-        </div>
         <p>{t('omics_integration')}</p>
+    </div>
     """, unsafe_allow_html=True)
 
 # About Omics Data Page
@@ -517,13 +517,13 @@ elif page == t("about_omics"):
             <li><b>{t('proteomics')}</b>: {t('proteomics_format')}</li>
             <li><b>{t('metabolomics')}</b>: {t('metabolomics_format')}</li>
         </ul>
-        </div>
         <p>{t('raw_omics_processing')}</p>
+    </div>
     """, unsafe_allow_html=True)
 
-    st.markdown(f"<div class='section-header'}>{t('preprocessing_steps')}</div>", unsafe_allow_html=True)
+    st.markdown(f"<div class='section-header'>{t('preprocessing_steps')}</div>", unsafe_allow_html=True)
     st.markdown(f"""
-    <div class='content animate__fadeIn'}>
+    <div class='content animate__fadeIn'>
         <p>{t('preprocessing_desc')}</p>
         <ol>
             <li><b>{t('quality_control')}</b>: {t('quality_control_desc')}</li>
@@ -532,8 +532,8 @@ elif page == t("about_omics"):
             <li><b>{t('standardization')}</b>: {t('standardization_desc')}</li>
             <li><b>{t('feature_selection')}</b>: {t('feature_selection_desc')}</li>
         </ol>
-        </div>
         <p>{t('preprocessing_importance')}</p>
+    </div>
     """, unsafe_allow_html=True)
 
     st.markdown(f"<div class='section-header'>{t('csv_format')}</div>", unsafe_allow_html=True)
@@ -547,7 +547,6 @@ elif page == t("about_omics"):
             <li><b>{t('separator')}</b>: {t('separator_desc')}</li>
             <li><b>{t('header')}</b>: {t('header_desc')}</li>
         </ul>
-        </div>
         <p>{t('csv_example')}</p>
         <pre class='code-block'>
 PatientID,UMOD_rs12917707,APOL1_rs73885319,MYH9_rs4821480,Status
@@ -559,7 +558,7 @@ P001,0,1,0,Unknown
 
     st.markdown(f"<div class='section-header'>{t('data_quality')}</div>", unsafe_allow_html=True)
     st.markdown(f"""
-    <div class='content animate__fadeIn'}>
+    <div class='content animate__fadeIn'>
         <p>{t('data_quality_desc')}</p>
         <ul>
             <li><b>{t('consistency')}</b>: {t('consistency_desc')}</li>
@@ -567,8 +566,8 @@ P001,0,1,0,Unknown
             <li><b>{t('accuracy')}</b>: {t('accuracy_desc')}</li>
             <li><b>{t('alignment')}</b>: {t('alignment_desc')}</li>
         </ul>
-        </div>
         <p>{t('data_quality_importance')}</p>
+    </div>
     """, unsafe_allow_html=True)
 
 # Predict Risk Page
@@ -599,6 +598,7 @@ elif page == t("predict_risk"):
             with cols[idx]:
                 uploaded_file = st.file_uploader(f"{t(omic)} (CSV)", type=["csv"], key=omic)
                 if uploaded_file:
+                    os.makedirs("data", exist_ok=True)
                     temp_path = os.path.join("data", f"temp_{omic}.csv")
                     with open(temp_path, "wb") as f:
                         f.write(uploaded_file.getbuffer())
@@ -620,71 +620,50 @@ elif page == t("predict_risk"):
                     patient_tensors = [torch.tensor(data_dict[omic].values, dtype=torch.float32) for omic in data_dict]
                     patient_dataset = TensorDataset(*patient_tensors)
                     patient_loader = DataLoader(patient_dataset, batch_size=best_hyperparams['batch_size'],
-                                         shuffle=False)
+                                               shuffle=False)
 
                     # Extract latent representations
                     encoded_data = extract_latent_representations(model, patient_loader, device)
 
                     # Calculate risk scores
-                    try:
-                    umap_df = calculate_risk_scores(encoded_data, data_dict, risk_score, labels, label_encoder)
-                    st.error_state(t("risk_score_error"))
-                    except Exception as e:
-                    print("Error calculating risk scores: {str(e)}")
+                    umap_df = calculate_risk_scores(encoded_data, data_dict, irc_biomarkers, labels, label_encoder)
 
                     # Perform SHAP analysis
                     output_dir = os.path.join("data", "temp_output")
                     os.makedirs(output_dir, exist_ok=True)
-                    try:
-                    important_biom, shap_plot_path = perform_shap_analysis(
+                    important_biomarkers, shap_plot_path = perform_shap_analysis(
                         model, combined_data, input_dims, device, feature_names, output_dir, irc_biomarkers
                     )
-                    except Exception as e:
-                        print("Error generating SHAP analysis: {str(e)}")
 
                     # Generate dynamic recommendations
-                    try:
                     risk_score = umap_df[t('risk_score')].iloc[0]
-                    biomarkers = important_biom.biomarkers[t('feature')].head(5).tolist()
+                    biomarkers = important_biomarkers[t('feature')].head(5).tolist()
                     recommendations = []
                     for intent in ["medication", "diet", "exercise"]:
-                        try:
-                            prompt = formulate_prompt(intent, risk_score, biomarkers)
-                            rec = generate_recommendation(prompt)
-                            if validate_recommendation(rec):
-                                rec = translate_recommendation(rec, lang)
-                                recommendations.append(rec)
-                            else:
-                                recommendations.append(t(f"{intent}_error", lang))
-                            except Exception as e:
-                                print("Error generating recommendation for {str(intent)}: {str(e)}")
-                    except Exception as e:
-                        print("Error generating recommendations: {str(e)}")
+                        prompt = formulate_prompt(intent, risk_score, biomarkers)
+                        rec = generate_recommendation(prompt)
+                        if validate_recommendation(rec):
+                            rec = translate_recommendation(rec, lang)
+                            recommendations.append(rec)
+                        else:
+                            recommendations.append(t(f"{intent}_error", lang))
 
                     # Generate PDF report
-                    try:
                     pdf_path = os.path.join(output_dir, f"ckd_risk_report_{st.session_state.patient_id}.pdf")
-                    pdf_doc = generate_pdf_report(risk_score, important_biomarkers, shap_plot_path, recommendations, pdf_path,
-                                        lang)
-                    except Exception as e:
-                        print("Error generating PDF report: {str(e)}")
+                    generate_pdf_report(risk_score, important_biomarkers, shap_plot_path, recommendations, pdf_path, lang)
 
                     # Save to database
-                    try:
                     session = Session()
                     patient = Patient(
                         patient_id=st.session_state.patient_id,
                         risk_score=risk_score,
                         biomarkers=', '.join(biomarkers)
                     )
-                    session.add(patient_data
+                    session.add(patient)
                     session.commit()
                     session.close()
-                    except Exception as e:
-                        print("Error saving to database: {str(e)}")
 
                     # Store results
-                    try:
                     st.session_state.results = {
                         'umap_df': umap_df,
                         'important_biomarkers': important_biomarkers,
@@ -692,8 +671,6 @@ elif page == t("predict_risk"):
                         'pdf_path': pdf_path,
                         'recommendations': recommendations
                     }
-                    except Exception as e:
-                        print("Error storing results: {str(e)}")
 
                     # Display results
                     st.markdown(f"<div class='section-header'>{t('prediction_results')}</div>", unsafe_allow_html=True)
@@ -704,78 +681,75 @@ elif page == t("predict_risk"):
                     fig_gauge = go.Figure(go.Indicator(
                         mode="gauge+number",
                         value=risk_score,
-                        title={'text': t('risk_score', lang), 'font':{'color': '#FFD700'}},
+                        title={'text': t('risk_score', lang), 'font': {'color': '#FFD700'}},
                         gauge={
                             'axis': {'range': [0, 100], 'tickcolor': '#FFF', 'tickfont': {'color': '#FFF'}},
                             'bar': {'color': '#0000FF'},
                             'steps': [
                                 {'range': [0, 33], 'color': '#4ADE80'},
                                 {'range': [33, 66], 'color': '#FACC15'},
-                                {'range': [66, 333], 'color': '#EF4444'}
+                                {'range': [66, 100], 'color': '#EF4444'}
                             ],
-                            },
                             'threshold': {
                                 'line': {'color': '#FFD700', 'width': 4},
                                 'thickness': 0.75,
                                 'value': risk_score
                             }
-                            }
                         }
                     ))
                     fig_gauge.update_layout(
                         paper_bgcolor='#0E1117',
                         font={'color': "#FFFFFF"},
-                        margin={'l':20,'r':20,'t':50,'b':20}
+                        margin={'l': 20, 'r': 20, 't': 50, 'b': 20}
                     )
                     st.plotly_chart(fig_gauge, use_container_width=True)
 
                     # SHAP bar plot
                     st.markdown(f"<div class='subheader'>{t('key_biomarkers')}</div>", unsafe_allow_html=True)
                     shap_fig = plt.figure(figsize=(12, 8))
-                    sns.barplot(data=important_biomarkers.head(20).data, x='SHAP_Mean_Abs', y=t('feature'), hue=t('omic'),
-                            palette=['#FF6F61', '#6B5B6B', '#4CAF50', '#2196F3'])
+                    sns.barplot(data=important_biomarkers.head(20), x='SHAP_Mean_Abs', y=t('feature'), hue=t('omic'),
+                                palette=['#FF6F61', '#6B5B95', '#4CAF50', '#2196F3'])
                     plt.title(t('shap_top_features'), fontsize=16, color='#FFFFFF')
                     plt.xlabel(t('shap_mean_value'), fontsize=12, color='#FFFFFF')
-                    plt.ylabel(t('feature'), fontsize=12, color='#FFF')
-                    plt.legend(title=t('omics'), facecolor='#1C2526', edgecolor='#FFF', labelcolor='#FFFFFF')
+                    plt.ylabel(t('feature'), fontsize=12, color='#FFFFFF')
+                    plt.legend(title=t('omics'), facecolor='#1C2526', edgecolor='#FFFFFF', labelcolor='#FFFFFF')
                     plt.gca().set_facecolor('#0E1117')
-                    plt.gca().tick_params(colors=['black'])
+                    plt.gca().tick_params(colors='#FFFFFF')
                     plt.gcf().set_facecolor('#0E1117')
                     st.pyplot(shap_fig)
 
-                # Display saved SHAP plot
-                if os.path.exists(shap_plot_path):
-                    st.image(shap_plot_path, caption=t('shap'), use_caption_width=True)
-
-                # Recommendations
-                st.markdown(f"<div class='subheader'>{t('recommendations')}</div>", unsafe_allow_html=True)
-                for rec in recommendations:
-                    st.markdown(f"<div class='recommendation'}>rec{rec}</div>", unsafe)
-
-                # Download results
-                st.markdown(f"<div class='subheader'>{t('download_results')}</div>", unsafe_allow_html=True)
-                col1, col2 = col.st.columns(2)
-                with col1:
-                    csv_data = important_biomarkers.to_csv().encode()
-                    b64_data = base64.b64encode(csv_data).decode()
-                    download_text = t('download_biomarkers', lang=st.session_state.language)
-                    href = f'<a href="data:file/csv;base64,{b64}" download="important_biomarkers.csv" class="download-btn">{download_text}</a>'
-                    st.markdown(href, unsafe_allow_html=True)
-                with col2:
-                    with open(pdf_path, "rb") as f:
-                        pdf_data = f.read()
-                        b64_data_pdf = base64.b64encode(pdf_data).decode()
-                        download_text_pdf = t('download_report', language=state.session_state.lang)
-                        href = href_pdf = f'<a href="data:application/pdf;base64,{b64_pdf}" download="ckd_risk_report.pdf" class="download-btn">{download_text_pdf}</a>'
-                        st.markdown(href_pdf, unsafe=allow_html=True)
+                    # Display saved SHAP plot
+                    if os.path.exists(shap_plot_path):
+                        st.image(shap_plot_path, caption=t('shap'), use_column_width=True)
+
+                    # Recommendations
+                    st.markdown(f"<div class='subheader'>{t('recommendations')}</div>", unsafe_allow_html=True)
+                    for rec in recommendations:
+                        st.markdown(f"<div class='recommendation'>{rec}</div>", unsafe_allow_html=True)
+
+                    # Download results
+                    st.markdown(f"<div class='subheader'>{t('download_results')}</div>", unsafe_allow_html=True)
+                    col1, col2 = st.columns(2)
+                    with col1:
+                        csv = important_biomarkers.to_csv(index=False).encode('utf-8')
+                        b64 = base64.b64encode(csv).decode()
+                        href = f'<a href="data:file/csv;base64,{b64}" download="important_biomarkers.csv" class="download-btn">{t("download_biomarkers", lang)}</a>'
+                        st.markdown(href, unsafe_allow_html=True)
+                    with col2:
+                        with open(pdf_path, "rb") as f:
+                            pdf_data = f.read()
+                        b64_pdf = base64.b64encode(pdf_data).decode()
+                        href_pdf = f'<a href="data:application/pdf;base64,{b64_pdf}" download="ckd_risk_report.pdf" class="download-btn">{t("download_report", lang)}</a>'
+                        st.markdown(href_pdf, unsafe_allow_html=True)
+
             except Exception as e:
                 st.error(t("file_process_error").format(error=str(e)))
                 st.stop()
         else:
-            st.warning(t("Please upload all omics files and provide a patient ID."))
+            st.warning(t("upload_warning"))
 
 # Chatbot Assistant Page
-else if page == t("chatbot"):
+elif page == t("chatbot"):
     st.markdown(f"<div class='title animate__fadeIn'>{t('chatbot_title')}</div>", unsafe_allow_html=True)
     st.markdown(f"""
     <div class='content animate__fadeIn'>
@@ -785,7 +759,7 @@ else if page == t("chatbot"):
 
     # Chat Interface
     st.markdown(f"<div class='section-header'>{t('chat_with_assistant')}</div>", unsafe_allow_html=True)
-    chat_container = st.container()container()
+    chat_container = st.container()
     with chat_container:
         for message in st.session_state.chat_history:
             if message['role'] == 'user':
@@ -794,57 +768,54 @@ else if page == t("chatbot"):
                 st.markdown(f"<div class='chat-bubble bot'>{message['content']}</div>", unsafe_allow_html=True)
 
     # User input
-    user_input = input.st.text_area(t("type_message"), key="chat_input")
+    user_input = st.text_area(t("type_message"), key="chat_input")
     if st.button(t("send"), key="button_send"):
-        try:
-                if user_input:
-                    # Add user message to history
-                    st.session_state.chat_history.append({'role': 'user', 'content': user_input})
-
-                    # Check if user is asking about results
-                    if st.session_state.results and any(
-                            keyword in user_input.lower() for keyword in t('chat_risk_keywords').split(',')):
+        if user_input:
+            try:
+                # Add user message to history
+                st.session_state.chat_history.append({'role': 'user', 'content': user_input})
+
+                # Check if user is asking about results
+                if st.session_state.results and any(
+                        keyword in user_input.lower() for keyword in t('chat_risk_keywords').split(',')):
+                    risk_score = st.session_state.results['umap_df'][t('risk_score')].iloc[0]
+                    biomarkers = st.session_state.results['important_biomarkers'][t('feature')].head(5).tolist()
+                    context = t('chat_risk_response').format(score=risk_score, biomarkers=', '.join(biomarkers))
+                    response = send_to_rasa(f"{context} {user_input}", lang)
+                else:
+                    # Generate recommendation based on intent
+                    intent = 'None'
+                    if any(keyword in user_input.lower() for keyword in t('chat_medication_keywords').split(',')):
+                        intent = 'medication'
+                    elif any(keyword in user_input.lower() for keyword in t('chat_diet_keywords').split(',')):
+                        intent = 'diet'
+                    elif any(keyword in user_input.lower() for keyword in t('chat_exercise_keywords').split(',')):
+                        intent = 'exercise'
+
+                    if intent != 'None' and st.session_state.results:
                         risk_score = st.session_state.results['umap_df'][t('risk_score')].iloc[0]
-                        biomarkers = st.session_state.results['important_biomarkers'][t('feature')].head(5).tolist()]
-                        context = t('chat_risk_response').format(score=risk_score), biomarkers=', '.join(biomarkers))
-                        response = send_to_rasa(f"{context} {user_input}", lang)
+                        biomarkers = st.session_state.results['important_biomarkers'][t('feature')].head(5).tolist()
+                        prompt = formulate_prompt(intent, risk_score, biomarkers)
+                        response = generate_recommendation(prompt)
+                        if validate_recommendation(response):
+                            response = translate_recommendation(response, lang)
+                        else:
+                            response = t(f"{intent}_error", lang)
                     else:
-                        # Generate recommendation based on intent
-                        intent = 'None'
-                        if any(keyword in user_input.lower() for keyword in t('chat_medication_keywords').split(',')):
-                            intent = 'medication'
-                        elif any(keyword in user_input.lower() for keyword in t('chat_diet_keywords').split(',')):
-                            intent = 'diet'
-                            elif any(keyword in user_input.lower() for keyword in t('chat_exercise_keywords').split(',')):
-                                intent = 'exercise'
-
-                            if intent and st.session_state.results:
-                                risk_score = st.session_state.results['umap_df'][t('risk_score')].iloc[0]
-                                biomarkers = st.session_state.results['important_biomarkers'][t('feature')].head(5).tolist()
-                                try:
-                                    prompt = formulate_prompt(intent, risk_score, biomarkers)
-                                    try:
-                                        response = generate_recommendation(prompt)
-                                        if validate_recommendation(response):
-                                            response = translate_recommendation(response, lang)
-                                        else:
-                                            response = t(f"{intent}_error", lang)
-                                            else:
-                                            response = send_to_rasa(user_input, lang)
-
-                            # Add bot response to history
-                            st.session_state.chat_history.append({'role': 'bot', 'content': response})
-
-                            # Rerender chat
-                            st.rerun()
-        except Exception as e:
-                            print("Error processing chatbot input: {str(e)}")
-                            st.error("Error processing chatbot input")
+                        response = send_to_rasa(user_input, lang)
+
+                # Add bot response to history
+                st.session_state.chat_history.append({'role': 'bot', 'content': response})
+
+                # Rerender chat
+                st.rerun()
+
+            except Exception as e:
+                st.error(t("chatbot_error").format(error=str(e)))
 
 # Dashboard Page
-elif if page == t("dashboard"):
+elif page == t("dashboard"):
     st.markdown(f"<div class='title animate__fadeIn'>{t('dashboard_title')}</div>", unsafe_allow_html=True)
-
     st.markdown(f"""
     <div class='content animate__fadeIn'>
         <p>{t('dashboard_intro')}</p>
@@ -872,7 +843,7 @@ elif if page == t("dashboard"):
             st.dataframe(filtered_df, use_container_width=True)
 
             # Plot
-            fig = px.scatter_plot(
+            fig = px.scatter(
                 filtered_df,
                 x=t('risk_score'),
                 y=t('patient_id'),
@@ -884,9 +855,9 @@ elif if page == t("dashboard"):
             fig.update_layout(
                 paper_bgcolor='#0E1117',
                 plot_bgcolor='#1C2526',
-                font={'color': '#ffffff'}
+                font={'color': '#FFFFFF'}
             )
-            st.plotly_chart(fig, use_container_width=True))
+            st.plotly_chart(fig, use_container_width=True)
         else:
             st.info(t("no_patients"))
         session.close()
@@ -903,10 +874,10 @@ if rasa_process:
     try:
         rasa_process.terminate()
     except Exception as e:
-        print("Error terminating Rasa server: {str(e)}")
+        print(f"Error terminating Rasa server: {str(e)}")
 
 st.markdown(f"""
-<div class='footer">
+<div class='footer'>
     {t('footer')}
 </div>
 """, unsafe_allow_html=True)