Update app.py

app.py

@@ -1,79 +1,34 @@
-import pandas as pd
 import gradio as gr
-from google.api_core.client_options import ClientOptions
-from google.cloud import documentai_v1 as documentai
-from google.cloud.documentai_v1.types import RawDocument
-from google.cloud import translate_v2 as translate
 import zipfile
-import os
-import io
-
-os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "herbaria-ai-3c860bcb0f44.json"
-
-# Global DataFrame declaration
-results_df = pd.DataFrame(columns=["Filename", "Extracted Text", "Translated Text"])
-
-# Set your Google Cloud Document AI processor details here
-project_id = "herbaria-ai"
-location = "us"
-processor_id = "4307b078717a399a"
-
-def translate_text(text, target_language="en"):
-    translate_client = translate.Client()
-    result = translate_client.translate(text, target_language=target_language)
-    return result["translatedText"]
-
-def batch_process_documents(file_stream, file_mime_type="image/jpeg") -> tuple:
-    opts = ClientOptions(api_endpoint=f"{location}-documentai.googleapis.com")
-    client = documentai.DocumentProcessorServiceClient(client_options=opts)
-    raw_document = RawDocument(content=file_stream.read(), mime_type=file_mime_type)
-
-    name = client.processor_path(project_id, location, processor_id)
-    request = documentai.ProcessRequest(name=name, raw_document=raw_document)
-    result = client.process_document(request=request)
-
-    extracted_text = result.document.text
-    translated_text = translate_text(extracted_text)
-    return extracted_text, translated_text
-
-def find_images(directory, extensions=('.jpeg', '.jpg')):
-    for root, _, filenames in os.walk(directory):
-        for filename in filenames:
-            if filename.lower().endswith(extensions) and not filename.startswith('.'):
-                yield os.path.join(root, filename)
-
-def process_zip_file(file_info):
-    global results_df
-    results_df = results_df.iloc[0:0]  # Clear the DataFrame if re-running this cell
-
-    with zipfile.ZipFile(io.BytesIO(file_info["content"]), 'r') as zip_ref:
-        zip_ref.extractall("extracted_files")
-
-    image_files = list(find_images("extracted_files"))
-
-    for file_path in image_files:
-        try:
-            extracted_text, translated_text = batch_process_documents(open(file_path, "rb"))
-            new_row = pd.DataFrame([{
-                "Filename": os.path.basename(file_path),
-                "Extracted Text": extracted_text,
-                "Translated Text": translated_text
-            }])
-            results_df = pd.concat([results_df, new_row], ignore_index=True)
-        except Exception as e:
-            return f"An error occurred while processing {file_path}: {e}"
-
-    return results_df
-
-interface = gr.Interface(fn=process_zip_file,
-                         inputs=gr.File(label="Upload ZIP File"),
-                         outputs=gr.Dataframe(label="Processed Results"),
-                         title="Document Processing and Translation")
-
+import pandas as pd
+from io import BytesIO
+
+def extract_image_names(zip_file):
+    # Use a BytesIO object to handle the file in memory
+    zip_bytes = BytesIO(zip_file.read())
+    
+    # Open the zip file
+    with zipfile.ZipFile(zip_bytes, 'r') as z:
+        # List all files in the zip
+        image_names = [info.filename for info in z.infolist() if info.filename.endswith(('.png', '.jpg', '.jpeg'))]
+    
+    # Convert list of names to a DataFrame
+    df = pd.DataFrame(image_names, columns=["Image Name"])
+    return df
+
+# Define the Gradio interface
+interface = gr.Interface(fn=extract_image_names,
+                         inputs=gr.inputs.File(type="file", label="Upload a Zip File"),
+                         outputs="dataframe",
+                         title="Zip Image Extractor",
+                         description="Upload a zip file to extract the names of the images.")
+
+# Run the Gradio app
 interface.launch()
 
 
 
+
 # def greet(name):
    # return "Hello " + name + "!!"