Update app.py

app.py

@@ -343,8 +343,6 @@ elif page== "📊 Tabular Data":
         placeholder = st.empty()
         
         for i in range(40):  # 4 segundos (40 iterações de 0.1s)
-            spider_position = " " * random.randint(1, 20) + "🕷️"
-            placeholder.markdown(f"<h2 style='text-align: center;'>{spider_position}</h2>", unsafe_allow_html=True)
             time.sleep(0.1)
         placeholder.empty()
         
@@ -352,31 +350,30 @@ elif page== "📊 Tabular Data":
             time.sleep(2)  # Simular carregamento
             try:
                 ai_output = """
-                **Based on the provided feature impacts, here's an interpretation of the model's decision-making process:**
-        
-                ### **Most influential factors:**
-                - **previous_stays (1.4 impact)**: The model assigns a significant weight to the number of previous stays, suggesting that patients with a history of multiple hospitalizations are more likely to be readmitted. This is a common predictor of readmission, as patients with frequent hospitalizations may have chronic conditions or complex health issues that increase their risk of requiring further care.
-                - **time_since_last_stay (-0.18 impact)**: The negative impact of time since last stay indicates that patients who have been discharged recently are more likely to be readmitted. This could be due to incomplete recovery, inadequate post-discharge care, or unresolved underlying conditions.
-        
-                ### **Moderately influential factors:**
-                - **n_meds (0.14 impact)**: The number of medications a patient is taking has a moderate positive impact, suggesting that patients with complex medication regimens may be at higher risk of readmission due to potential side effects, interactions, or non-adherence.
-                - **real_age (0.12 impact)**: The patient's age has a moderate positive impact, indicating that older patients may be more likely to be readmitted due to age-related health decline, comorbidities, or decreased physiological reserve.
-        
-                ### **Less influential factors:**
-                - **creatine (0.05 impact)** and **cci_score (0.05 impact)**: These two factors have relatively small positive impacts, suggesting that kidney function (creatinine) and comorbidity burden (cci_score) may contribute to the readmission risk, but to a lesser extent.
-                - **drg_severity (-0.04 impact)**: The negative impact of DRG (Diagnosis-Related Group) severity suggests that patients with less severe diagnoses may be more likely to be readmitted, potentially due to inadequate treatment or incomplete recovery.
-                - **blood_cells (0.03 impact)**: The small positive impact of blood cell count may indicate that patients with abnormal blood cell counts (e.g., anemia) may be at slightly higher risk of readmission.
-                - **discharge_location_CHRONIC/LONG TERM ACUTE CARE (-0.03 impact)** and **race_WHITE (-0.03 impact)**: These two factors have small negative impacts, suggesting that patients discharged to chronic or long-term acute care facilities and white patients may be less likely to be readmitted.
-        
-                ### **Key characteristics of the patient or admission:**
-                Based on the feature impacts, the model is likely to predict readmission for patients with:
-                - A history of multiple hospitalizations (**previous_stays**)
-                - Recent discharge (**time_since_last_stay**)
-                - Complex medication regimens (**n_meds**)
-                - Older age (**real_age**)
-                - Potential kidney function issues (**creatine**) or comorbidities (**cci_score**)
-        
-                These characteristics may indicate that the patient has chronic or complex health issues, incomplete recovery, or inadequate post-discharge care, increasing their risk of readmission.
+                To understand why the model made a certain prediction, let's analyze the feature impacts provided. The feature impacts represent the change in the predicted outcome (patient readmission) for a one-unit change in the feature, while holding all other features constant. The magnitude and direction of the impact indicate how much each feature contributes to the prediction.
+
+                Key Characteristics Influencing the Prediction:
+                Previous Stays (1.56 impact): This feature has the highest positive impact, suggesting that patients with a history of previous stays at the hospital are more likely to be readmitted. The large impact indicates that the model places significant weight on this factor, likely because repeated hospital visits can signify chronic conditions or complications that are not fully resolved.
+                
+                Number of Medications (n_meds) (0.17 impact): Patients taking a higher number of medications are at a higher risk of readmission. This could be due to the complexity of their medical conditions, potential side effects, or interactions between medications that may lead to further health issues.
+                
+                Time Since Last Stay (0.16 impact): The time elapsed since the patient's last hospital stay also positively influences the prediction of readmission. This might indicate that patients who have been discharged recently are at a higher risk of returning, possibly due to incomplete recovery or the nature of their condition requiring ongoing care.
+                
+                Real Age (0.14 impact): The patient's age is another factor that increases the likelihood of readmission. Older patients may have more complex health issues, diminished physiological reserve, and a higher likelihood of comorbid conditions, all of which can contribute to the need for repeat hospitalizations.
+                
+                Length of Stay (los_days) (0.07 impact): Although less influential than the top factors, a longer hospital stay during the current or previous admission(s) slightly increases the risk of readmission. This could be indicative of more severe illness, complications, or the need for prolonged recovery periods.
+                
+                Less Influential but Still Relevant Factors:
+                CCI Score (0.05 impact): The Charlson Comorbidity Index (CCI) score, which predicts the ten-year mortality for a patient who may have a range of comorbid conditions, such as heart disease, diabetes, or cancer. A higher score indicates a higher risk of mortality and, by extension, potentially a higher risk of readmission due to the complexity of the patient's health conditions.
+                
+                DRG Severity (-0.04 impact): The Diagnosis-Related Group (DRG) severity, which categorizes hospital cases into one or more groups that are expected to have similar hospital resource use, has a negative impact. This suggests that higher severity cases, as categorized by DRG, might actually have a slightly lower risk of readmission, possibly due to more intensive treatment and monitoring during their initial stay.
+                
+                Race (race_WHITE: -0.04 impact, race_UNKNOWN: 0.03 impact): The impacts of race are relatively small and might reflect underlying socio-economic or healthcare access disparities rather than direct biological factors. However, interpreting these impacts requires caution due to the potential for confounding variables and the ethical considerations surrounding race in healthcare outcomes.
+                
+                Blood Cells (0.03 impact): This factor, likely referring to some measure of blood cell count or health, has a minor positive impact, suggesting that abnormalities in blood cell counts could slightly increase the risk of readmission, potentially due to underlying conditions affecting the blood or bone marrow.
+                
+                Conclusion:
+                The model's prediction of patient readmission is most strongly influenced by the patient's history of previous hospital stays, indicating a potential for chronic or recurring health issues. The number of medications, time since the last stay, and the patient's age are also significant factors, highlighting the complexity of the patient's health condition and the potential for ongoing care needs. While other factors such as CCI score, DRG severity, race, and blood cell health play a role, their impacts are less pronounced. Understanding these factors can help healthcare providers identify high-risk patients and implement targeted interventions to reduce the likelihood of readmission.
                 """
         
                 # 🎭 **Show AI Response in a Stylish Chat Format**