Update app.py

app.py

@@ -1,143 +1,143 @@
-import gradio as gr
-import zipfile
-import os
-import tempfile
-import pandas as pd
-
-# Function to process the zip file, get document names, and calculate word counts (without keywords initially)
-def process_zip_initial(zip_file):
-    # Create a temporary directory to extract files
-    with tempfile.TemporaryDirectory() as temp_dir:
-        # Extract the zip file
-        with zipfile.ZipFile(zip_file.name, 'r') as zip_ref:
-            zip_ref.extractall(temp_dir)
-        
-        # Recursively get list of all .txt files in all directories and calculate word count
-        txt_files = []
-        word_counts = []
-        for root, dirs, files in os.walk(temp_dir):
-            for file in files:
-                if file.endswith('.txt'):
-                    file_path = os.path.join(root, file)
-                    txt_files.append(os.path.basename(file_path))  # Only the file name
-                    
-                    # Calculate word count
-                    with open(file_path, 'r', encoding='utf-8') as f:
-                        text = f.read()
-                        word_count = len(text.split())  # Split text by spaces to count words
-                        word_counts.append(word_count)
-        
-        # Create a DataFrame with document names and word counts
-        df = pd.DataFrame({"Nom du document": txt_files, "N. mots": word_counts})
-        
-        return df
-
-# Function to process the zip file and search for keywords (while preserving Word Count)
-def process_zip_and_search(zip_file, keywords_text):
-    # Create a temporary directory to extract files
-    with tempfile.TemporaryDirectory() as temp_dir:
-        # Extract the zip file
-        with zipfile.ZipFile(zip_file.name, 'r') as zip_ref:
-            zip_ref.extractall(temp_dir)
-        
-        # Recursively get list of all .txt files in all directories
-        txt_files = []
-        word_counts = []
-        base_names = []
-        for root, dirs, files in os.walk(temp_dir):
-            for file in files:
-                if file.endswith('.txt'):
-                    file_path = os.path.join(root, file)  # Full path to the file
-                    txt_files.append(file_path)  # Store the full path
-                    base_names.append(os.path.basename(file_path))  # Store just the base name for display
-                    
-                    # Calculate word count
-                    with open(file_path, 'r', encoding='utf-8') as f:
-                        text = f.read()
-                        word_count = len(text.split())  # Split text by spaces to count words
-                        word_counts.append(word_count)
-        
-        # Read keywords (split by line, supports multiword expressions)
-        keywords = [keyword.strip().lower() for keyword in keywords_text.strip().split("\n") if keyword.strip()]  # Convert keywords to lowercase
-        
-        # Prepare a dictionary to store the results (initialize with Document Name and Word Count)
-        results = {base_name: {keyword: "" for keyword in keywords} for base_name in base_names}
-        
-        # Search for keyword frequencies in each text file (support multiword)
-        for i, txt_file in enumerate(txt_files):
-            with open(txt_file, 'r', encoding='utf-8') as file:
-                text = file.read().lower()  # Convert to lowercase for case-insensitive search
-                
-                # Count occurrences of each keyword (multiword expressions supported)
-                for keyword in keywords:
-                    keyword_count = text.count(keyword.lower())  # Use count() to get occurrences (both text and keywords are lowercased)
-                    if keyword_count > 0:
-                        results[base_names[i]][keyword] = keyword_count
-        
-        # Convert the results dictionary to a DataFrame
-        df_keywords = pd.DataFrame(results).T  # Transpose to have files as rows and keywords as columns
-        
-        # Reset index to make the document names a column
-        df_keywords.reset_index(inplace=True)
-        
-        # Rename the first column to 'Document Name'
-        df_keywords.rename(columns={"index": "Nom du document"}, inplace=True)
-        
-        # Replace 0 frequencies with empty strings
-        df_keywords.replace(0, "", inplace=True)
-        
-        # Create a DataFrame with document names and word counts
-        df_word_counts = pd.DataFrame({"Nom du document": base_names, "N. mots": word_counts})
-        
-        # Merge the Word Count DataFrame with the Keywords DataFrame on "Document Name"
-        final_df = pd.merge(df_word_counts, df_keywords, on="Nom du document")
-        
-        return final_df
-
-
-
-# Function to export the DataFrame to Excel
-def export_to_excel(df):
-    # Create a temporary directory for storing the Excel file
-    with tempfile.NamedTemporaryFile(suffix=".xlsx", delete=False) as tmp:
-        excel_path = tmp.name
-        # Save the DataFrame to Excel
-        df.to_excel(excel_path, index=False)
-    return excel_path
-
-# Create Gradio interface with one results table and export functionality
-with gr.Blocks() as demo:
-    gr.Markdown("# Recherche simple par mots-clés")  # This line adds the title
-
-    with gr.Row():
-        # File upload and initial table with document names
-        zip_file_input = gr.File(label="Téléversez votre dossier .zip contenant les fichiers texte (format .txt)")
-    
-    with gr.Row():
-        # Textbox for entering keywords
-        keywords_input = gr.Textbox(label="Entrez les mots clés (un par ligne, peuvent contenir plus d'un mot)", placeholder="mots-clés...", lines=10)
-    
-    with gr.Row():
-        # Button to trigger keyword search
-        search_button = gr.Button("Recherche")
-    
-    # Output the final results table after the search button
-    with gr.Row():
-        result_table = gr.DataFrame(label="Résultats", col_count = (1, "dynamic"), interactive=False, max_height=600)  # Disable renaming/editing
-    
-    # Button to trigger the Excel export
-    with gr.Row():
-        export_button = gr.Button("Exporter vers Excel (.xlsx)")
-        download_link = gr.File(label="Télécharger le fichier")
-
-    # Action to display document names upon ZIP upload
-    zip_file_input.change(fn=process_zip_initial, inputs=zip_file_input, outputs=result_table)
-    
-    # Action to update the table with keywords and results
-    search_button.click(fn=process_zip_and_search, inputs=[zip_file_input, keywords_input], outputs=result_table)
-    
-    # Action to export the results to Excel
-    export_button.click(fn=export_to_excel, inputs=result_table, outputs=download_link)
-
-# Launch the app
+import gradio as gr
+import zipfile
+import os
+import tempfile
+import pandas as pd
+
+# Function to process the zip file, get document names, and calculate word counts (without keywords initially)
+def process_zip_initial(zip_file):
+    # Create a temporary directory to extract files
+    with tempfile.TemporaryDirectory() as temp_dir:
+        # Extract the zip file
+        with zipfile.ZipFile(zip_file.name, 'r') as zip_ref:
+            zip_ref.extractall(temp_dir)
+        
+        # Recursively get list of all .txt files in all directories and calculate word count
+        txt_files = []
+        word_counts = []
+        for root, dirs, files in os.walk(temp_dir):
+            for file in files:
+                if file.endswith('.txt'):
+                    file_path = os.path.join(root, file)
+                    txt_files.append(os.path.basename(file_path))  # Only the file name
+                    
+                    # Calculate word count
+                    with open(file_path, 'r', encoding='utf-8') as f:
+                        text = f.read()
+                        word_count = len(text.split())  # Split text by spaces to count words
+                        word_counts.append(word_count)
+        
+        # Create a DataFrame with document names and word counts
+        df = pd.DataFrame({"Nom du document": txt_files, "N. mots": word_counts})
+        
+        return df
+
+# Function to process the zip file and search for keywords (while preserving Word Count)
+def process_zip_and_search(zip_file, keywords_text):
+    # Create a temporary directory to extract files
+    with tempfile.TemporaryDirectory() as temp_dir:
+        # Extract the zip file
+        with zipfile.ZipFile(zip_file.name, 'r') as zip_ref:
+            zip_ref.extractall(temp_dir)
+        
+        # Recursively get list of all .txt files in all directories
+        txt_files = []
+        word_counts = []
+        base_names = []
+        for root, dirs, files in os.walk(temp_dir):
+            for file in files:
+                if file.endswith('.txt'):
+                    file_path = os.path.join(root, file)  # Full path to the file
+                    txt_files.append(file_path)  # Store the full path
+                    base_names.append(os.path.basename(file_path))  # Store just the base name for display
+                    
+                    # Calculate word count
+                    with open(file_path, 'r', encoding='utf-8') as f:
+                        text = f.read()
+                        word_count = len(text.split())  # Split text by spaces to count words
+                        word_counts.append(word_count)
+        
+        # Read keywords (split by line, supports multiword expressions)
+        keywords = [keyword.strip().lower() for keyword in keywords_text.strip().split("\n") if keyword.strip()]  # Convert keywords to lowercase
+        
+        # Prepare a dictionary to store the results (initialize with Document Name and Word Count)
+        results = {base_name: {keyword: "" for keyword in keywords} for base_name in base_names}
+        
+        # Search for keyword frequencies in each text file (support multiword)
+        for i, txt_file in enumerate(txt_files):
+            with open(txt_file, 'r', encoding='utf-8') as file:
+                text = file.read().lower()  # Convert to lowercase for case-insensitive search
+                
+                # Count occurrences of each keyword (multiword expressions supported)
+                for keyword in keywords:
+                    keyword_count = text.count(keyword.lower())  # Use count() to get occurrences (both text and keywords are lowercased)
+                    if keyword_count > 0:
+                        results[base_names[i]][keyword] = keyword_count
+        
+        # Convert the results dictionary to a DataFrame
+        df_keywords = pd.DataFrame(results).T  # Transpose to have files as rows and keywords as columns
+        
+        # Reset index to make the document names a column
+        df_keywords.reset_index(inplace=True)
+        
+        # Rename the first column to 'Document Name'
+        df_keywords.rename(columns={"index": "Nom du document"}, inplace=True)
+        
+        # Replace 0 frequencies with empty strings
+        df_keywords.replace(0, "", inplace=True)
+        
+        # Create a DataFrame with document names and word counts
+        df_word_counts = pd.DataFrame({"Nom du document": base_names, "N. mots": word_counts})
+        
+        # Merge the Word Count DataFrame with the Keywords DataFrame on "Document Name"
+        final_df = pd.merge(df_word_counts, df_keywords, on="Nom du document")
+        
+        return final_df
+
+
+
+# Function to export the DataFrame to Excel
+def export_to_excel(df):
+    # Create a temporary directory for storing the Excel file
+    with tempfile.NamedTemporaryFile(suffix=".xlsx", delete=False) as tmp:
+        excel_path = tmp.name
+        # Save the DataFrame to Excel
+        df.to_excel(excel_path, index=False)
+    return excel_path
+
+# Create Gradio interface with one results table and export functionality
+with gr.Blocks() as demo:
+    gr.Markdown("# Recherche simple par mots-clés")  # This line adds the title
+
+    with gr.Row():
+        # File upload and initial table with document names
+        zip_file_input = gr.File(label="Téléversez votre dossier .zip contenant les fichiers texte (format .txt)")
+    
+    with gr.Row():
+        # Textbox for entering keywords
+        keywords_input = gr.Textbox(label="Entrez les mots clés (un par ligne, peuvent contenir plus d'un mot)", placeholder="mots-clés...", lines=10)
+    
+    with gr.Row():
+        # Button to trigger keyword search
+        search_button = gr.Button("Recherche")
+    
+    # Output the final results table after the search button
+    with gr.Row():
+        result_table = gr.DataFrame(label="Résultats", col_count = (1, "dynamic"), interactive=False)  
+    
+    # Button to trigger the Excel export
+    with gr.Row():
+        export_button = gr.Button("Exporter vers Excel (.xlsx)")
+        download_link = gr.File(label="Télécharger le fichier")
+
+    # Action to display document names upon ZIP upload
+    zip_file_input.change(fn=process_zip_initial, inputs=zip_file_input, outputs=result_table)
+    
+    # Action to update the table with keywords and results
+    search_button.click(fn=process_zip_and_search, inputs=[zip_file_input, keywords_input], outputs=result_table)
+    
+    # Action to export the results to Excel
+    export_button.click(fn=export_to_excel, inputs=result_table, outputs=download_link)
+
+# Launch the app
 demo.launch()