initial commit

app.py

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+import gradio as gr
+import torch
+from transformers import BertTokenizer, EncoderDecoderModel, pipeline
+
+# Load model and tokenizer
+model = EncoderDecoderModel.from_pretrained("imsachinsingh00/bert2bert-mts-summary")
+tokenizer = BertTokenizer.from_pretrained("imsachinsingh00/bert2bert-mts-summary")
+
+# Move to CUDA if available
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(device)
+
+# Summarization function
+def summarize_dialogue(dialogue):
+    inputs = tokenizer(dialogue, return_tensors="pt", padding=True, truncation=True, max_length=512).to(device)
+    summary_ids = model.generate(inputs.input_ids, max_length=64, num_beams=4, early_stopping=True)
+    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+    return summary
+
+# Gradio interface
+demo = gr.Interface(
+    fn=summarize_dialogue,
+    inputs=[
+        gr.Textbox(lines=10, label="Doctor-Patient Dialogue"),
+        gr.Audio(source="microphone", type="filepath", optional=True)
+    ],
+    outputs="text",
+    title="Medical Dialogue Summarizer",
+    description="Enter or speak a conversation. The model will summarize it."
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+transformers 
+datasets 
+evaluate
+rouge_score
+torch