Update app.py

app.py

@@ -1,33 +1,33 @@
 import gradio as gr
 from transformers import pipeline
-model_id = "knkarthick/TOPIC-DIALOGSUM"
+model_id = "knkarthick/MEETING_SUMMARY"
 
 generator = pipeline(task="text2text-generation", model=model_id)
 
-def split_paragraph(paragraph, max_chunk_size=1024):
-    words = paragraph.split()
-    chunks = []
-    current_chunk = []
-    current_chunk_size = 0
+#def split_paragraph(paragraph, max_chunk_size=1024):
+#    words = paragraph.split()
+#    chunks = []
+#    current_chunk = []
+#    current_chunk_size = 0
 
-    for word in words:
-        word_len = len(word) + 1  # Add 1 for the space
-        if current_chunk_size + word_len <= max_chunk_size:
-            current_chunk.append(word)
-            current_chunk_size += word_len
-        else:
-            chunks.append(' '.join(current_chunk))
-            current_chunk = [word]
-            current_chunk_size = word_len
+#    for word in words:
+#        word_len = len(word) + 1  # Add 1 for the space
+#        if current_chunk_size + word_len <= max_chunk_size:
+#            current_chunk.append(word)
+#            current_chunk_size += word_len
+#        else:
+#            chunks.append(' '.join(current_chunk))
+#            current_chunk = [word]
+#            current_chunk_size = word_len
 
-    if current_chunk:
-        chunks.append(' '.join(current_chunk))
+#    if current_chunk:
+#        chunks.append(' '.join(current_chunk))
 
-    return chunks
+#    return chunks
 
 def launch(input):
-    if len(input) > 1024:
-        return " ".join([res["generated_text"] for res in generator(split_paragraph(input))])
+#    if len(input) > 1024:
+#        return " ".join([res["generated_text"] for res in generator(split_paragraph(input))])
     return generator(input)[0]["generated_text"]
 
 iface = gr.Interface(launch, inputs="text", outputs="text")