Update diagnosis retrieval and embedder

app.py

@@ -9,9 +9,10 @@ from fastapi.responses import JSONResponse
 from pymongo import MongoClient
 from google import genai
 from sentence_transformers import SentenceTransformer
+from sentence_transformers.util import cos_sim
 from memory import MemoryManager
 from translation import translate_query
-from sentence_transformers.util import cos_sim
+
 
 # ✅ Enable Logging for Debugging
 import logging
@@ -183,7 +184,7 @@ def retrieve_diagnosis_from_symptoms(symptom_text, top_k=5, min_sim=0.4):
     global SYMPTOM_VECTORS, SYMPTOM_DOCS
     # Lazy load
     if SYMPTOM_VECTORS is None:
-        all_docs = list(symptom_col.find({}, {"embedding": 1, "answer": 1, "question": 1}))
+        all_docs = list(symptom_col.find({}, {"embedding": 1, "answer": 1, "question": 1, "prognosis": 1}))
         SYMPTOM_DOCS = all_docs
         SYMPTOM_VECTORS = np.array([doc["embedding"] for doc in all_docs], dtype=np.float32)
     # Embed input

diagnosis.py

@@ -0,0 +1,76 @@
+# ✅ Google Colab: SymbiPredict Embedding + Chunking + MongoDB Upload
+
+import pandas as pd
+import numpy as np
+from sentence_transformers import SentenceTransformer
+from pymongo import MongoClient
+from pymongo.errors import BulkWriteError
+import hashlib, os
+from tqdm import tqdm
+
+# ✅ Load model
+model = SentenceTransformer("all-MiniLM-L6-v2")
+
+# ✅ Load SymbiPredict
+df = pd.read_csv("symbipredict_2022.csv")
+
+# ✅ Connect to MongoDB
+mongo_uri = "..."
+client = MongoClient(mongo_uri)
+db = client["MedicalChatbotDB"]
+collection = db["symptom_diagnosis"]
+
+# ✅ Clear old symptom-diagnosis records
+print("🧹 Dropping old 'symptom_diagnosis' collection...")
+collection.drop()
+#  Reconfirm collection is empty
+if collection.count_documents({}) != 0:
+    raise RuntimeError("❌ Collection not empty after drop — aborting!")
+
+# ✅ Convert CSV rows into QA-style records with embeddings
+records = []
+for i, row in tqdm(df.iterrows(), total=len(df)):
+    symptom_cols = df.columns[:-1]
+    label_col = df.columns[-1]
+
+    # Extract symptoms present (value==1)
+    symptoms = [col.replace("_", " ").strip() for col in symptom_cols if row[col] == 1]
+    if not symptoms:
+        continue
+
+    label = row[label_col].strip()
+    question = f"What disease is likely given these symptoms: {', '.join(symptoms)}?"
+    answer = f"The patient is likely suffering from: {label}."
+
+    # Embed question only
+    embed = model.encode(question, convert_to_numpy=True)
+    hashkey = hashlib.md5((question + answer).encode()).hexdigest()
+
+    records.append({
+        "_id": hashkey,
+        "i": int(i),
+        "symptoms": symptoms,
+        "prognosis": label,
+        "question": question,
+        "answer": answer,
+        "embedding": embed.tolist()
+    })
+
+# ✅ Save to MongoDB
+if records:
+    print(f"⬆️ Uploading {len(records)} records to MongoDB...")
+    unique_ids = set()
+    deduped = []
+    for r in records:
+        if r["_id"] not in unique_ids:
+            unique_ids.add(r["_id"])
+            deduped.append(r)
+    try:
+      collection.insert_many(deduped, ordered=False)
+      print(f"✅ Inserted {len(deduped)} records without duplicates.")
+    except BulkWriteError as bwe:
+      inserted = bwe.details.get('nInserted', 0)
+      print(f"⚠️ Inserted with some duplicate skips. Records inserted: {inserted}")
+    print("✅ Upload complete.")
+else:
+    print("⚠️ No records to upload.")