Create app.py

app.py

@@ -0,0 +1,59 @@
+import os
+import gradio as gr
+import torch
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+def load_data(path):
+    """
+    Load dataset
+    """
+    input_path = os.path.join(path)
+    with open(input_path, "r") as f:
+        data = f.read()
+
+    return data
+
+def preprocessing(data):      
+    texts = list()
+
+    i = 0
+    if len(data) <= i+4000:
+        texts = data
+    else:
+        while len(data[i:]) != 0:
+            if len(data[i:]) > 4000:
+                string = str(data[i:i+4000])
+                texts.append(string)
+                i = i + 3800
+            else:
+                string = str(data[i:])
+                texts.append(string)
+                break    
+    return texts
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+peft_model_id = "sooolee/flan-t5-base-cnn-samsum-lora"
+config = PeftConfig.from_pretrained(peft_model_id)
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path, device_map='auto') # load_in_8bit=True,
+model = PeftModel.from_pretrained(model, peft_model_id, device_map='auto')
+
+def summarize(path):
+    transcript = load_data(path)
+    texts = preprocessing(transcript)
+    inputs = tokenizer(texts, return_tensors="pt", padding=True, )
+
+    with torch.no_grad():
+        output_tokens = model.generate(input_ids=inputs["input_ids"].to("cuda"), max_new_tokens=60, do_sample=True, top_p=0.9)
+        outputs = tokenizer.batch_decode(output_tokens.detach().cpu().numpy(), skip_special_tokens=True)
+    
+    return outputs
+
+gr.Interface(
+    fn=summarize,
+    title = 'Summarize Transcripts',
+    inputs = gr.File(file_types="text", label="Upload a text file.", interactive=True),
+    outputs = gr.Textbox(label="Summary", max_lines=120, interactive=False),
+).launch()