Use model

app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load model directly from your Hugging Face repository
+def load_model():    
+
+    tokenizer = AutoTokenizer.from_pretrained("omi-health/sum-small", trust_remote_code=False)
+    model = AutoModelForCausalLM.from_pretrained("omi-health/sum-small", trust_remote_code=False)
+    
+    return model, tokenizer
+    return model, tokenizer
+
+# Function to generate SOAP notes
+def generate_soap_note(doctor_patient_conversation):
+    if not doctor_patient_conversation.strip():
+        return "Please enter a doctor-patient conversation."
+        
+    # Tokenize and generate
+    inputs = tokenizer(doctor_patient_conversation, return_tensors="pt")
+    
+    generate_ids = model.generate(inputs.input_ids, max_length=200, num_beams=5, no_repeat_ngram_size=2, early_stopping=True)
+
+
+    # Decode and extract the response part
+    return tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+    
+# Load model and tokenizer (this will run once when the app starts)
+model, tokenizer = load_model()
+
+# Sample conversation for the example
+sample_conversation = """
+Doctor: Good morning, how are you feeling today?
+Patient: Not so great, doctor. I've had this persistent cough for about two weeks now.
+Doctor: I'm sorry to hear that. Can you tell me more about the cough? Is it dry or are you coughing up anything?
+Patient: It started as a dry cough, but for the past few days I've been coughing up some yellowish phlegm.
+Doctor: Do you have any other symptoms like fever, chills, or shortness of breath?
+Patient: I had a fever of 100.5°F two days ago. I've been feeling more tired than usual, and sometimes it's a bit hard to catch my breath after coughing a lot.
+"""
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=generate_soap_note,
+    inputs=gr.Textbox(
+        lines=15, 
+        placeholder="Enter doctor-patient conversation here...",
+        label="Doctor-Patient Conversation",
+        value=sample_conversation
+    ),
+    outputs=gr.Textbox(
+        label="Generated SOAP Note", 
+        lines=15
+    ),
+    title="Medical SOAP Note Generator",
+    description="Enter a doctor-patient conversation to generate a structured SOAP note using OMI Health's task-specific model.",
+    examples=[[sample_conversation]],
+    allow_flagging="never"
+)
+
+# Launch the app
+demo.launch()

requirements.txt

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+--extra-index-url https://download.pytorch.org/whl/cu113
+torch
+transformers>=4.36.0
+gradio>=3.50.0