Add app.py

app.py

@@ -0,0 +1,41 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModel
+
+# Load Med42
+med42_model_name = "m42-health/med42-70b"
+med42_tokenizer = AutoTokenizer.from_pretrained(med42_model_name)
+med42_model = AutoModelForCausalLM.from_pretrained(med42_model_name)
+
+# Load ClinicalBERT
+clinicalbert_model_name = "medicalai/ClinicalBERT"
+clinicalbert_tokenizer = AutoTokenizer.from_pretrained(clinicalbert_model_name)
+clinicalbert_model = AutoModel.from_pretrained(clinicalbert_model_name)
+
+# Define functions
+def med42_qa(question):
+    inputs = med42_tokenizer(question, return_tensors="pt")
+    outputs = med42_model.generate(**inputs, max_length=200)
+    return med42_tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+def analyze_ehr(text):
+    inputs = clinicalbert_tokenizer(text, return_tensors="pt")
+    embeddings = clinicalbert_model(**inputs).last_hidden_state
+    return f"ClinicalBERT generated embeddings with shape: {embeddings.shape}"
+
+# Combine Gradio Interface
+def combined_tool(input_text):
+    qa_result = med42_qa(input_text)
+    ehr_result = analyze_ehr(input_text)
+    return f"Med42 Answer:\n{qa_result}\n\nClinicalBERT Analysis:\n{ehr_result}"
+
+# Build Gradio UI
+interface = gr.Interface(
+    fn=combined_tool,
+    inputs="text",
+    outputs="text",
+    title="Healthcare AI Tool",
+    description="Use Med42 for medical Q&A and ClinicalBERT for EHR analysis."
+)
+
+if __name__ == "__main__":
+    interface.launch()