Update app.py to run computation

.vscode/settings.json

@@ -0,0 +1,6 @@
+{
+  "[python]": {
+    "editor.defaultFormatter": "ms-python.black-formatter"
+  },
+  "python.formatting.provider": "none"
+}

app.py

@@ -1,7 +1,79 @@
 import gradio as gr
+import json
+from pathlib import Path
+
+from sklearn.metrics.pairwise import cosine_similarity
+
+from InstructorEmbedding import INSTRUCTOR
+
+model = INSTRUCTOR("hkunlp/instructor-large")
+
+EMBED_FILE_PATH = Path(__file__).parent / "depressive_disorders_embed.json"
+case_note_instruction = (
+    "Represent the case note for a possible DSM-5 mental health diagnosis:"
+)
+
+
+def read_json_file(file_path):
+    with open(file_path, "r", encoding="utf-8") as file:
+        data = json.load(file)
+    return data
+
+
+def get_case_note_embedding(case_note):
+    case_note_emb = model.encode([[case_note_instruction, case_note]])
+    return case_note_emb
+
+
+def get_top_n_diagnoses(case_note):
+    top_n = 3
+
+    case_note_emb = get_case_note_embedding(case_note)
+    diagnoses_embed = read_json_file(EMBED_FILE_PATH)["diagnoses"]
+
+    diagnosis_list = []
+    for diagnosis in diagnoses_embed:
+        diagnosis_name = diagnosis["name"]
+        criteria_scores = (
+            []
+        )  # Store criterion descriptions and scores for each diagnosis
+
+        count = 0
+        sum_score = 0
+        for criterion in diagnosis["criteria"]:
+            count += 1
+            score = cosine_similarity(criterion["embedding"], case_note_emb)
+            sum_score += score
+            criteria_scores.append(
+                {"description": criterion["description"], "score": float(score)}
+            )
+
+        sum_score /= count
+        diagnosis_list.append(
+            {
+                "name": diagnosis_name,
+                "sum_score": float(sum_score),
+                "criteria_scores": criteria_scores,
+            }
+        )
+
+    # Sort the diagnoses based on the sum_score in descending order
+    sorted_diagnoses = sorted(
+        diagnosis_list, key=lambda x: x["sum_score"], reverse=True
+    )
+
+    # Select the top n diagnoses
+    top_n_diagnoses = sorted_diagnoses[:top_n]
+
+    # Convert the top_n_diagnoses to JSON format
+    top_n_diagnoses_json = json.dumps(top_n_diagnoses, indent=2)
+
+    return top_n_diagnoses_json
+
 
 def greet(name):
     return "Hello " + name + "!!"
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+
+iface = gr.Interface(fn=get_top_n_diagnoses, inputs="text", outputs="text")
 iface.launch()