Duplicate from spuun/dialogsum

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README.md

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+---
+title: Dialogsum
+emoji: 🚀
+colorFrom: pink
+colorTo: yellow
+sdk: gradio
+sdk_version: 3.32.0
+app_file: app.py
+pinned: false
+license: unknown
+duplicated_from: spuun/dialogsum
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+from transformers import pipeline
+model_id = "knkarthick/TOPIC-DIALOGSUM"
+
+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
+
+    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))
+
+    return chunks
+
+def launch(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")
+iface.launch()

requirements.txt

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+transformers
+torch