Update app.py

app.py

@@ -1,61 +1,55 @@
-import gradio as gr
-import zipfile
-import os
-import pandas as pd
-from sentence_transformers import SentenceTransformer
-
-model = SentenceTransformer("csAhmad/zoraiz-model")
-
-# Create output folder
-os.makedirs("temp", exist_ok=True)
-
-def process_zip(zip_file):
-    extract_path = "temp/extracted"
-
-    # clean old files
-    if os.path.exists(extract_path):
-        for f in os.listdir(extract_path):
-            os.remove(os.path.join(extract_path, f))
-    else:
-        os.makedirs(extract_path)
-
-    # extract zip
-    with zipfile.ZipFile(zip_file, 'r') as zip_ref:
-        zip_ref.extractall(extract_path)
-
-    results = []
-
-    # example: read all text files
-    for file in os.listdir(extract_path):
-        file_path = os.path.join(extract_path, file)
-
-        if file.endswith(".txt"):
-            with open(file_path, "r", encoding="utf-8") as f:
-                text = f.read()
-
-            embedding = model.encode(text)
-
-            results.append({
-                "file": file,
-                "embedding_dim": len(embedding),
-                "first_values": str(embedding[:10])
-            })
-
-    # create dataframe
-    df = pd.DataFrame(results)
-
-    output_file = "output.xlsx"
-    df.to_excel(output_file, index=False)
-
-    return output_file
-
-
-demo = gr.Interface(
-    fn=process_zip,
-    inputs=gr.File(file_types=[".zip"]),
-    outputs=gr.File(label="Download Excel"),
-    title="ZIP to Excel Processor",
-    description="Upload ZIP → Extract files → Generate embeddings → Export Excel"
-)
-
-demo.launch(share=True)
+import gradio as gr
+import zipfile
+import os
+import pandas as pd
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("csAhmad/zoraiz-model")
+
+os.makedirs("temp/extracted", exist_ok=True)
+
+def process_zip(zip_file):
+    extract_path = "temp/extracted"
+    os.makedirs(extract_path, exist_ok=True)
+
+    zip_path = zip_file.name  # always works in Spaces
+
+    # Extract ZIP
+    with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+        zip_ref.extractall(extract_path)
+
+    results = []
+
+    # Example: process txt files
+    for file in os.listdir(extract_path):
+        if file.endswith(".txt"):
+            file_path = os.path.join(extract_path, file)
+
+            with open(file_path, "r", encoding="utf-8") as f:
+                text = f.read()
+
+            emb = model.encode(text)
+
+            results.append({
+                "file": file,
+                "embedding_dim": len(emb),
+                "first_10_values": str(emb[:10])
+            })
+
+    df = pd.DataFrame(results)
+
+    output_file = "output.xlsx"
+    df.to_excel(output_file, index=False)
+
+    return output_file
+
+
+demo = gr.Interface(
+    fn=process_zip,
+    inputs=gr.File(file_types=[".zip"]),
+    outputs=gr.File(label="Download Excel"),
+    title="ZIP to Excel Processor",
+    description="Upload ZIP → Extract files → Generate embeddings → Export Excel"
+)
+
+demo.launch()