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Dockerfile

@@ -47,4 +47,4 @@ RUN python -c "from sentence_transformers import SentenceTransformer; SentenceTr
 
 EXPOSE 7860
 
-CMD ["python", "run_aimhsa.py"]
+CMD ["python", "app.py"]

app.py

@@ -1,23 +1,27 @@
-import os, json, numpy as np
+import os
+import sys
+import time
+import uuid
+import json
+import re
+import sqlite3
+import numpy as np
+import tempfile
+import smtplib
+import secrets
+import math
+import traceback
+import ollama
+import _time
 from flask import Flask, request, jsonify, send_from_directory, render_template, redirect
 from flask_cors import CORS
 # Replace direct ollama import with OpenAI client
 from openai import OpenAI
 from dotenv import load_dotenv
-import sqlite3
 from werkzeug.security import generate_password_hash, check_password_hash
-import time
-import uuid
-from datetime import datetime
-import tempfile
-import pytesseract
-from werkzeug.utils import secure_filename
-import re
-import math
-from typing import Dict, List, Tuple, Optional
 from translation_service import translation_service
 from sms_service import initialize_sms_service, get_sms_service
-import time as _time
+
 from config import current_config
 
 # Initialize OpenAI client for Ollama
@@ -42,11 +46,6 @@ def _retry_openai_call(func, *args, _retries=1, _delay=0.5, **kwargs):
                     pass
             else:
                 raise last_err
-import smtplib
-from email.mime.text import MIMEText
-from email.mime.multipart import MIMEMultipart
-
-load_dotenv()
 
 # --- Helper Functions ---
 def get_time_ago(timestamp):
@@ -475,24 +474,12 @@ def save_message(conv_id: str, role: str, content: str):
                     # determine owner_key if needed
                     owner_key = get_owner_key_for_conv(conv_id)
                     if row is None:
-                        # create conversation row if missing, attach owner_key when available
                         conn.execute(
-                            "INSERT OR REPLACE INTO conversations (conv_id, owner_key, preview, ts) VALUES (?, ?, ?, ?)",
+                            "INSERT OR IGNORE INTO conversations (conv_id, owner_key, preview, ts) VALUES (?, ?, ?, ?)",
                             (conv_id, owner_key, snippet[:120], time.time()),
                         )
                     else:
-                        existing_preview = row[0] or ""
-                        if existing_preview.strip() in ("", "New chat"):
-                            conn.execute(
-                                "UPDATE conversations SET preview = ?, ts = ? WHERE conv_id = ?",
-                                (snippet[:120], time.time(), conv_id),
-                            )
-                        else:
-                            # update timestamp at least so listing sorts by recent activity
-                            conn.execute(
-                                "UPDATE conversations SET ts = ? WHERE conv_id = ?",
-                                (time.time(), conv_id),
-                            )
+                        conn.execute("UPDATE conversations SET preview = ?, ts = ? WHERE conv_id = ?", (snippet[:120], time.time(), conv_id))
         except Exception:
             # don't break saving messages if preview update fails
             pass
@@ -648,13 +635,13 @@ class RiskDetector:
             Respond in JSON format: {{"risk_score": 0.0-1.0, "indicators": ["indicator1", "indicator2"]}}
             """
             
-            response = _retry_ollama_call(ollama.chat, model=CHAT_MODEL, messages=[
+            response = _retry_openai_call(openai_client.chat.completions.create, model=CHAT_MODEL, messages=[
                 {"role": "system", "content": "You are a mental health risk assessment AI. Analyze conversations for risk indicators and provide structured JSON responses."},
                 {"role": "user", "content": ai_prompt}
             ])
             
             # Parse AI response robustly (extract JSON if wrapper text present)
-            raw = response.get("message", {}).get("content", "")
+            raw = response.choices[0].message.content if response.choices else ""
             ai_result = {}
             try:
                 ai_result = json.loads(raw)
@@ -1238,20 +1225,20 @@ def retrieve(query: str, k: int = 4, lambda_param: float = 0.6):
             app.logger.error(f"Failed to use sentence-transformers: {e}")
             # fallback to ollama if local model not available
             try:
-                app.logger.info(f"Falling back to ollama.embeddings with model: {EMBED_MODEL}")
-                q_emb_resp = _retry_ollama_call(ollama.embeddings, model=EMBED_MODEL, prompt=query)
-                q_emb = np.array([q_emb_resp["embedding"]], dtype=np.float32)
-                app.logger.info("Successfully embedded query with ollama fallback")
+                app.logger.info(f"Falling back to openai embeddings with model: {EMBED_MODEL}")
+                response = openai_client.embeddings.create(model=EMBED_MODEL, input=query)
+                q_emb = np.array([response.data[0].embedding], dtype=np.float32)
+                app.logger.info("Successfully embedded query with openai fallback")
             except Exception as e2:
-                app.logger.error(f"Ollama fallback also failed: {e2}")
+                app.logger.error(f"OpenAI fallback also failed: {e2}")
                 raise
     else:
-        app.logger.info(f"Using ollama embeddings API with model: {SENT_EMBED_MODEL}")
-        # default: use ollama embeddings API
+        app.logger.info(f"Using openai embeddings API with model: {SENT_EMBED_MODEL}")
+        # default: use openai embeddings API
         try:
-            q_emb_resp = _retry_ollama_call(ollama.embeddings, model=SENT_EMBED_MODEL, prompt=query)
-            q_emb = np.array([q_emb_resp["embedding"]], dtype=np.float32)
-            app.logger.info(f"Successfully embedded query with ollama, shape: {q_emb.shape}")
+            response = openai_client.embeddings.create(model=SENT_EMBED_MODEL, input=query)
+            q_emb = np.array([response.data[0].embedding], dtype=np.float32)
+            app.logger.info(f"Successfully embedded query with openai, shape: {q_emb.shape}")
         except Exception as e:
             app.logger.error(f"Failed to embed query with {SENT_EMBED_MODEL}: {e}")
             # Return empty results if embedding fails
@@ -1268,14 +1255,14 @@ def retrieve(query: str, k: int = 4, lambda_param: float = 0.6):
                 )
                 # Always use nomic-embed-text to match the stored chunks
                 try:
-                    re_q = ollama.embeddings(model="nomic-embed-text", prompt=query)
-                    q_emb2 = np.array([re_q["embedding"]], dtype=np.float32)
+                    response = openai_client.embeddings.create(model="nomic-embed-text", input=query)
+                    q_emb2 = np.array([response.data[0].embedding], dtype=np.float32)
                     q_dim2 = int(q_emb2.shape[1])
                     if q_dim2 == doc_dim:
                         q_emb = q_emb2
-                        app.logger.info("Re-embedded query with nomic-embed-text to match chunk dimensions")
+                        app.logger.info(f"Successfully re-embedded with nomic-embed-text, shape: {q_emb.shape}")
                     else:
-                        app.logger.error(f"nomic-embed-text still produces wrong dimension: {q_dim2} != {doc_dim}")
+                        app.logger.error(f"Even nomic-embed-text dimension {q_dim2} doesn't match chunk dim {doc_dim}")
                         return []
                 except Exception as re_err:
                     app.logger.error(f"Re-embedding with nomic-embed-text failed: {re_err}")
@@ -1664,6 +1651,19 @@ Directives professionnelles:
 - Maintenez un ton naturel et conversationnel en français
 - Assurez des standards professionnels de soutien en santé mentale
 
+Rappelez-vous: Vous êtes un système professionnel de soutien en santé mentale conçu pour fournir une assistance immédiate et culturellement appropriée tout en connectant les utilisateurs aux soins professionnels quand nécessaire.""",
+
+        'rw': """Uri AIMHSA, umufasha w'ubuzima bw'ubwoba bw'u Rwanda w'ubuhanga.
+
+Amabwiriza y'ubuhanga:
+- Ube umuntu w'umutima mwiza, w'umutima mwiza, kandi w'umutima mwiza
+- Tanga amakuru yashyizweho ku bikoresho byo mu cyerekezo mugihe cyose
+- VUGURA BURI
+- Gardez les réponses professionnelles, concises mais utiles
+- Utilisez le contexte fourni pour donner des informations précises et pertinentes
+- Maintenez un ton naturel et conversationnel en français
+- Assurez des standards professionnels de soutien en santé mentale
+
 Rappelez-vous: Vous êtes un système professionnel de soutien en santé mentale conçu pour fournir une assistance immédiate et culturellement appropriée tout en connectant les utilisateurs aux soins professionnels quand nécessaire.""",
 
         'rw': """Uri AIMHSA, umufasha w'ubuzima bw'ubwoba bw'u Rwanda w'ubuhanga.