Add exceptiion handling

app.py

@@ -10,43 +10,46 @@ model = AutoModelForSeq2SeqLM.from_pretrained(MODEL, token = TOKEN)
 tokenizer = AutoTokenizer.from_pretrained(MODEL, token = TOKEN)
 
 def summarize_text(text):
-    chunk_size = MAX_TOKENS * 4
-    overlap = chunk_size // 4 
-    step = chunk_size - overlap
-    chunks = [text[i:i + chunk_size] for i in range(0, len(text), step)]
+    try:
+        chunk_size = MAX_TOKENS * 4
+        overlap = chunk_size // 4 
+        step = chunk_size - overlap
+        chunks = [text[i:i + chunk_size] for i in range(0, len(text), step)]
 
-    summaries = []
-    for chunk in chunks:
-        inputs = tokenizer(chunk, return_tensors = "pt", truncation = True, max_length = 1024, padding = True)
-        with torch.no_grad():
-            summary_ids = model.generate(
-                **inputs,
-                max_length = 1500,
-                length_penalty = 2.0,
-                num_beams = 4,
-                early_stopping = True
-            )
-        summary = tokenizer.decode(summary_ids[0], skip_special_tokens = True)
-        summaries.append(summary)
+        summaries = []
+        for chunk in chunks:
+            inputs = tokenizer(chunk, return_tensors = "pt", truncation = True, max_length = 1024, padding = True)
+            with torch.no_grad():
+                summary_ids = model.generate(
+                    **inputs,
+                    max_length = 1500,
+                    length_penalty = 2.0,
+                    num_beams = 4,
+                    early_stopping = True
+                )
+            summary = tokenizer.decode(summary_ids[0], skip_special_tokens = True)
+            summaries.append(summary)
 
-    final_text = " ".join(summaries)
-    summarization = final_text
-    if len(final_text) > MAX_TOKENS:
-        inputs = tokenizer(final_text, return_tensors = "pt", truncation = True, max_length = 1024, padding = True)
-        with torch.no_grad():
-            summary_ids = model.generate(
-                **inputs,
-                min_length = 300,
-                max_length = 1500,
-                length_penalty = 2.0,
-                num_beams = 4,
-                early_stopping = True
-            )
-        summarization = tokenizer.decode(summary_ids[0], skip_special_tokens = True)
-    else:
-        summarization = final_text 
-        
-    return summarization
+        final_text = " ".join(summaries)
+        summarization = final_text
+        if len(final_text) > MAX_TOKENS:
+            inputs = tokenizer(final_text, return_tensors = "pt", truncation = True, max_length = 1024, padding = True)
+            with torch.no_grad():
+                summary_ids = model.generate(
+                    **inputs,
+                    min_length = 300,
+                    max_length = 1500,
+                    length_penalty = 2.0,
+                    num_beams = 4,
+                    early_stopping = True
+                )
+            summarization = tokenizer.decode(summary_ids[0], skip_special_tokens = True)
+        else:
+            summarization = final_text 
+            
+        return summarization
+    except Exception as e:
+        return e
 
 demo = gr.Interface(
     fn = summarize_text,