Update app.py

app.py

@@ -9,7 +9,6 @@ from geopy.exc import GeocoderTimedOut, GeocoderServiceError
 import time
 import random
 from typing import List, Tuple, Optional
-import tempfile
 import io
 
 # NuExtract API configuration
@@ -108,39 +107,54 @@ def create_location_map(df: pd.DataFrame,
 
 # Processing Functions
 def process_excel(file, places_column):
-    # Read the Excel file
-    df = pd.read_excel(io.BytesIO(file))
+    # Check if file is None
+    if file is None:
+        return None, "No file uploaded", None
     
-    if places_column not in df.columns:
-        return None, f"Column '{places_column}' not found in the Excel file. Available columns: {', '.join(df.columns)}"
-    
-    # Initialize the geocoding service
-    geocoder = GeocodingService(user_agent="gradio_map_visualization_app")
-    
-    # Process locations and add coordinates
-    df['coordinates'] = df[places_column].apply(geocoder.process_locations)
-    
-    # Create the map
-    map_obj = create_location_map(df, coordinates_col='coordinates', places_col=places_column)
-    
-    # Save the map to a temporary HTML file
-    temp_map_path = "temp_map.html"
-    map_obj.save(temp_map_path)
-    
-    # Save the processed DataFrame to Excel
-    processed_file_path = "processed_data.xlsx"
-    df.to_excel(processed_file_path, index=False)
-    
-    # Statistics
-    total_locations = len(df)
-    successful_geocodes = df['coordinates'].apply(lambda x: len([c for c in x if c is not None])).sum()
-    failed_geocodes = df['coordinates'].apply(lambda x: len([c for c in x if c is None])).sum()
-    
-    stats = f"Total locations: {total_locations}\n"
-    stats += f"Successfully geocoded: {successful_geocodes}\n"
-    stats += f"Failed to geocode: {failed_geocodes}"
-    
-    return temp_map_path, stats, processed_file_path
+    try:
+        # Handle various file object types that Gradio might provide
+        if hasattr(file, 'name'):
+            # Gradio file object
+            df = pd.read_excel(file.name)
+        elif isinstance(file, bytes):
+            # Raw bytes
+            df = pd.read_excel(io.BytesIO(file))
+        else:
+            # Assume it's a filepath string
+            df = pd.read_excel(file)
+        
+        if places_column not in df.columns:
+            return None, f"Column '{places_column}' not found in the Excel file. Available columns: {', '.join(df.columns)}", None
+        
+        # Initialize the geocoding service
+        geocoder = GeocodingService(user_agent="gradio_map_visualization_app")
+        
+        # Process locations and add coordinates
+        df['coordinates'] = df[places_column].apply(geocoder.process_locations)
+        
+        # Create the map
+        map_obj = create_location_map(df, coordinates_col='coordinates', places_col=places_column)
+        
+        # Save the map to a temporary HTML file
+        temp_map_path = "temp_map.html"
+        map_obj.save(temp_map_path)
+        
+        # Save the processed DataFrame to Excel
+        processed_file_path = "processed_data.xlsx"
+        df.to_excel(processed_file_path, index=False)
+        
+        # Statistics
+        total_locations = len(df)
+        successful_geocodes = df['coordinates'].apply(lambda x: len([c for c in x if c is not None])).sum()
+        failed_geocodes = df['coordinates'].apply(lambda x: len([c for c in x if c is None])).sum()
+        
+        stats = f"Total locations: {total_locations}\n"
+        stats += f"Successfully geocoded: {successful_geocodes}\n"
+        stats += f"Failed to geocode: {failed_geocodes}"
+        
+        return temp_map_path, stats, processed_file_path
+    except Exception as e:
+        return None, f"Error processing file: {str(e)}", None
 
 # NuExtract Functions
 def extract_info(template, text):
@@ -159,6 +173,13 @@ def extract_info(template, text):
         
         response = requests.post(API_URL, headers=headers, json=payload)
         
+        # If the model is loading, inform the user
+        if response.status_code == 503:
+            response_json = response.json()
+            if "error" in response_json and "loading" in response_json["error"]:
+                estimated_time = response_json.get("estimated_time", "unknown")
+                return f"⏳ Model is loading (ETA: {int(float(estimated_time)) if isinstance(estimated_time, (int, float, str)) else 'unknown'} seconds)", "Please try again in a few minutes"
+        
         if response.status_code != 200:
             return f"❌ API Error: {response.status_code}", response.text
         
@@ -200,12 +221,12 @@ with gr.Blocks() as demo:
                 with gr.Column():
                     template = gr.Textbox(
                         label="JSON Template", 
-                        value='{"earthquake location": "", "dateline location": "", "source origin": ""}',
+                        value='{"name": "", "email": ""}',
                         lines=5
                     )
                     text = gr.Textbox(
                         label="Text to Extract From",
-                        value="Tokio, 16. Jan. Neues Erdbeben in Japan. Nach einer Havasmeldung aus Tokio ist in der Gegend von Tokio und Jokohama ein neues Erdbeben festgestellt worden. Der Umfang des angerichteten Schadens läßt sich im Augenblick noch nicht übersehen. Die telegraphischen und telephonischen Verbindungen zwischen Tokio und Jokohama sind vollständig zerstört. Der Eisenbahnverkehr zwischen den beiden Städten ist vollkommen lahmgelegt. Nach einem späteren Telegramm beträgt die Zahl der Toten in Tokio 50. Die Zahl der Verwundeten steht noch nicht fest. Das Erdbeben dauerte sechs Minuten. In Jokohama gab es sechs Tote und 200 Verwundete. Dazu sind in der letztgenannten Stadt 600 Häuser eingestürzt. In einem Vorort von Tok'o ist ein Brand ausgebrochen. Ein Zug wurde in eine Schlucht geschleudert, sechs andere Züge sind entgleist.",
+                        value="Contact: John Smith (john@example.com)",
                         lines=8
                     )
                     extract_btn = gr.Button("Extract Information", variant="primary")
@@ -232,34 +253,30 @@ with gr.Blocks() as demo:
                 with gr.Column():
                     map_output = gr.HTML(label="Map Visualization")
                     stats_output = gr.Textbox(label="Statistics", lines=3)
-                    download_btn = gr.Button("Download Processed Data")
-                    processed_file = gr.File(label="Processed Data", visible=False)
+                    processed_file = gr.File(label="Processed Data", visible=True, interactive=False)
             
             def process_and_map(file, column):
                 if file is None:
                     return None, "Please upload an Excel file", None
                 
-                map_path, stats, processed_path = process_excel(file, column)
-                
-                if map_path:
-                    with open(map_path, "r") as f:
-                        map_html = f.read()
+                try:
+                    map_path, stats, processed_path = process_excel(file, column)
                     
-                    return map_html, stats, processed_path
-                else:
-                    return None, stats, None
+                    if map_path and processed_path:
+                        with open(map_path, "r") as f:
+                            map_html = f.read()
+                        
+                        return map_html, stats, processed_path
+                    else:
+                        return None, stats, None
+                except Exception as e:
+                    return None, f"Error: {str(e)}", None
             
             process_btn.click(
                 fn=process_and_map,
                 inputs=[excel_file, places_column],
                 outputs=[map_output, stats_output, processed_file]
             )
-            
-            download_btn.click(
-                fn=lambda x: x,
-                inputs=[processed_file],
-                outputs=[processed_file]
-            )
 
 if __name__ == "__main__":
     demo.launch()