Create app.py

app.py

@@ -0,0 +1,137 @@
+import streamlit as st
+import pandas as pd
+import re
+from datetime import datetime
+import pdfplumber
+import plotly.graph_objects as go
+import plotly.express as px
+import io
+
+def extract_data_from_pdf(pdf_content):
+    data_list = []
+    current_record = {}
+    
+    for line in pdf_content.split('\n'):
+        # Extract header information
+        if 'Entreprise::' in line:
+            if current_record and 'temperature_data' in current_record:
+                data_list.append(current_record)
+            current_record = {'temperature_data': []}
+        
+        # Extract metadata
+        if 'Date:' in line:
+            try:
+                date_str = re.search(r'Date:\s*(\d{2}[./]\d{2}[./]\d{4})', line).group(1)
+                date_str = date_str.replace('/', '.')
+                current_record['date'] = datetime.strptime(date_str, '%d.%m.%Y').strftime('%Y-%m-%d')
+            except:
+                current_record['date'] = None
+        
+        if 'Produit:' in line:
+            current_record['produit'] = line.split('Produit:')[-1].strip()
+        
+        if 'Utilisateur:' in line:
+            current_record['utilisateur'] = line.split('Utilisateur:')[-1].strip()
+        
+        # Extract temperature data
+        if any(x in line for x in ['Début', '+ ', 'Fin']) and '°C' in line:
+            try:
+                parts = line.strip().split()
+                time = parts[0] if 'Début' in line or 'Fin' in line else parts[1]
+                temp_sterilisateur = float(parts[-3].replace('°C', ''))
+                temp_coeur = float(parts[-2].replace('°C', ''))
+                valeur_f = float(parts[-1])
+                
+                current_record['temperature_data'].append({
+                    'temps': time,
+                    'temp_sterilisateur': temp_sterilisateur,
+                    'temp_coeur': temp_coeur,
+                    'valeur_f': valeur_f
+                })
+            except:
+                continue
+    
+    # Add last record
+    if current_record and 'temperature_data' in current_record:
+        data_list.append(current_record)
+    
+    return data_list
+
+def analyze_sterilization(data):
+    results = []
+    
+    for record in data:
+        temp_data = pd.DataFrame(record['temperature_data'])
+        
+        # Determine product type and required temperature
+        is_nutabreizh = 'NutaBreizh' in record['produit']
+        required_temp = 108 if is_nutabreizh else 103
+        
+        # Count minutes at required temperature
+        minutes_at_temp = len(temp_data[temp_data['temp_coeur'] >= required_temp])
+        
+        # Calculate max temperatures
+        max_temp_sterilisateur = temp_data['temp_sterilisateur'].max()
+        max_temp_coeur = temp_data['temp_coeur'].max()
+        
+        # Determine if criteria met
+        criteria_met = minutes_at_temp >= 30
+        
+        results.append({
+            'Date': record['date'],
+            'Produit': record['produit'],
+            'Utilisateur': record['utilisateur'],
+            'Temperature_Requise': required_temp,
+            'Minutes_Temperature_Requise': minutes_at_temp,
+            'Temperature_Max_Sterilisateur': max_temp_sterilisateur,
+            'Temperature_Max_Coeur': max_temp_coeur,
+            'Criteres_Respectes': criteria_met
+        })
+    
+    return pd.DataFrame(results)
+
+def main():
+    st.title("Analyse des Protocoles de Stérilisation")
+    
+    uploaded_file = st.file_uploader("Choisir un fichier PDF", type="pdf")
+    
+    if uploaded_file is not None:
+        # Read PDF content
+        pdf_text = ""
+        with pdfplumber.open(uploaded_file) as pdf:
+            for page in pdf.pages:
+                pdf_text += page.extract_text() + "\n"
+        
+        # Process data
+        data = extract_data_from_pdf(pdf_text)
+        results_df = analyze_sterilization(data)
+        
+        # Display results
+        st.subheader("Résultats de l'analyse")
+        st.dataframe(results_df)
+        
+        # Create visualization
+        fig = px.scatter(results_df, 
+                        x='Date', 
+                        y='Minutes_Temperature_Requise',
+                        color='Criteres_Respectes',
+                        hover_data=['Produit', 'Temperature_Requise'],
+                        title="Minutes à température requise par production")
+        st.plotly_chart(fig)
+        
+        # Export button
+        if st.button("Exporter en Excel"):
+            output = io.BytesIO()
+            with pd.ExcelWriter(output, engine='xlsxwriter') as writer:
+                results_df.to_excel(writer, index=False)
+            
+            output.seek(0)
+            st.download_button(
+                label="Télécharger l'analyse",
+                data=output,
+                file_name="analyse_sterilisation.xlsx",
+                mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
+            )
+
+if __name__ == "__main__":
+    main()