new overall train route

ai/profile_manager.py

@@ -74,3 +74,67 @@ def update_profile_components(coding_style: Optional[Dict[str, Any]] = None,
             pass
         raise
     return profile
+
+
+def update_personal_info(personal_data: str) -> Dict[str, Any]:
+    """Store user's personal information in profile.
+    
+    Args:
+        personal_data: Raw text containing user's personal details
+        
+    Returns:
+        Updated profile dictionary
+    """
+    profile = load_profile()
+    
+    # Initialize personal_info if it doesn't exist
+    if "personal_info" not in profile:
+        profile["personal_info"] = {
+            "entries": [],
+            "raw_data": ""
+        }
+    
+    # Append new data with timestamp
+    entry = {
+        "timestamp": datetime.datetime.now().isoformat(),
+        "content": personal_data
+    }
+    profile["personal_info"]["entries"].append(entry)
+    
+    # Also maintain a concatenated version for easy access
+    profile["personal_info"]["raw_data"] = "\n\n".join(
+        [e["content"] for e in profile["personal_info"]["entries"]]
+    )
+    
+    profile["last_updated"] = datetime.datetime.now().isoformat()
+
+    # Atomic write
+    os.makedirs(os.path.dirname(PROFILE_PATH) or ".", exist_ok=True)
+    fd, tmp_path = tempfile.mkstemp(dir=os.path.dirname(PROFILE_PATH) or ".")
+    try:
+        with os.fdopen(fd, "w", encoding="utf-8") as tmp:
+            json.dump(profile, tmp, indent=2, ensure_ascii=False)
+        os.replace(tmp_path, PROFILE_PATH)
+    except Exception:
+        try:
+            os.remove(tmp_path)
+        except Exception:
+            pass
+        raise
+    return profile
+
+
+def get_personal_context() -> str:
+    """Retrieve user's personal information as a formatted string for AI context.
+    
+    Returns:
+        Formatted string of all personal information, or empty string if none exists.
+    """
+    profile = load_profile()
+    personal_info = profile.get("personal_info", {})
+    raw_data = personal_info.get("raw_data", "")
+    
+    if not raw_data:
+        return ""
+    
+    return f"User's Personal Information:\n{raw_data}"

app.py

@@ -6,13 +6,22 @@ import os
 import threading
 import time
 import uuid
+import datetime
+import tempfile
+import json
 from collections import OrderedDict, defaultdict
 
 # Unified LLM import (router selects Groq if available else local llama)
 from ai.llm_router import run_llama, init_model, stream_llama
 
 from ai.code_analyzer import analyze_code
-from ai.profile_manager import update_profile, load_profile, update_profile_components
+from ai.profile_manager import (
+    update_profile, 
+    load_profile, 
+    update_profile_components,
+    update_personal_info,
+    get_personal_context
+)
 from ai.refactor_engine import suggest_refactor
 from ai.prompt_builder import build_persona_prompt
 from ai.code_fingerprint import analyze_fingerprint
@@ -25,6 +34,8 @@ from ai.code_memory import save_to_memory
 LOG_LEVEL = os.environ.get("LOG_LEVEL", "INFO")
 logging.basicConfig(level=LOG_LEVEL)
 
+PROFILE_PATH = os.environ.get("USER_PROFILE_PATH", "./user_data/user_profile.json")
+
 app = Flask(__name__)
 CORS(app)
 Compress(app)
@@ -146,10 +157,15 @@ def ask():
         skill = meta.get("skill", "moderate_learner")
         emotion = meta.get("emotion", "neutral")
 
+        # Get personal context from profile
+        personal_context = get_personal_context()
+        personal_section = f"\n\n{personal_context}" if personal_context else ""
+
         SYSTEM_PROMPT = (
             "You are CodeMate — an empathetic coding tutor. "
             "Explain or answer the question clearly and helpfully. "
             f"User context: skill={skill}, emotion={emotion}"
+            f"{personal_section}"
         )
 
         # Add user message to history
@@ -201,10 +217,15 @@ def ask_stream():
         skill = meta.get("skill", "moderate_learner")
         emotion = meta.get("emotion", "neutral")
 
+        # Get personal context from profile
+        personal_context = get_personal_context()
+        personal_section = f"\n\n{personal_context}" if personal_context else ""
+
         SYSTEM_PROMPT = (
             "You are CodeMate — an empathetic coding tutor. "
             "Explain or answer the question clearly and helpfully. "
             f"User context: skill={skill}, emotion={emotion}"
+            f"{personal_section}"
         )
 
         # Add user message to history
@@ -263,6 +284,10 @@ def code_assist():
         style = profile.get("coding_style", {})
         fingerprint = profile.get("fingerprint", {})
 
+        # Get personal context from profile
+        personal_context = get_personal_context()
+        personal_section = f"\n\n{personal_context}" if personal_context else ""
+
         # Create adaptive persona prompt (this creates a complete prompt string)
         prompt_content = build_persona_prompt(query, emotion, skill, style, fingerprint)
 
@@ -274,6 +299,7 @@ def code_assist():
             "You are CodeMate — an empathetic coding assistant. "
             f"User profile: skill={skill}, emotion={emotion}. "
             "Provide helpful code examples and explanations."
+            f"{personal_section}"
         )
         
         messages = _get_messages(session_id, system_prompt)
@@ -377,6 +403,111 @@ def train():
         return jsonify({"error": str(e)}), 500
 
 
+# ---- /train_overall ----------------------------------------------
+@app.route("/train_overall", methods=["POST"])
+def train_overall():
+    """Store user's personal information for personalized AI responses.
+    
+    This endpoint stores any personal details, preferences, or background information
+    about the user. The AI will use this context in all future conversations.
+    
+    Input JSON:
+      personal_data: string (required) - text containing personal information
+        Examples: "My name is John. I'm 20 years old. I'm studying CS at MIT."
+                  "I prefer Python over JavaScript. I work at Google."
+                  "My hobbies include gaming and reading sci-fi novels."
+    
+    Returns:
+      JSON with success message and updated personal info summary
+    """
+    try:
+        req = request.json or {}
+        personal_data = req.get("personal_data", "").strip()
+        
+        if not personal_data:
+            return jsonify({"error": "personal_data is required"}), 400
+        
+        # Limit size to prevent abuse (max 50KB per entry)
+        if len(personal_data) > 50000:
+            return jsonify({"error": "personal_data too large (max 50KB)"}), 400
+        
+        # Update profile with personal information
+        profile = update_personal_info(personal_data)
+        
+        # Get summary of stored data
+        personal_info = profile.get("personal_info", {})
+        entry_count = len(personal_info.get("entries", []))
+        total_chars = len(personal_info.get("raw_data", ""))
+        
+        return jsonify({
+            "message": "Personal information saved successfully",
+            "entry_count": entry_count,
+            "total_characters": total_chars,
+            "preview": personal_data[:200] + ("..." if len(personal_data) > 200 else "")
+        })
+    except Exception as e:
+        logging.exception("/train_overall failed")
+        return jsonify({"error": str(e)}), 500
+
+
+# ---- /get_personal_info ------------------------------------------
+@app.route("/get_personal_info", methods=["GET"])
+def get_personal_info():
+    """Retrieve all stored personal information.
+    
+    Returns:
+      JSON with all personal info entries and summary
+    """
+    try:
+        profile = load_profile()
+        personal_info = profile.get("personal_info", {})
+        
+        return jsonify({
+            "entries": personal_info.get("entries", []),
+            "raw_data": personal_info.get("raw_data", ""),
+            "entry_count": len(personal_info.get("entries", [])),
+            "total_characters": len(personal_info.get("raw_data", ""))
+        })
+    except Exception as e:
+        logging.exception("/get_personal_info failed")
+        return jsonify({"error": str(e)}), 500
+
+
+# ---- /clear_personal_info ----------------------------------------
+@app.route("/clear_personal_info", methods=["POST"])
+def clear_personal_info():
+    """Clear all stored personal information.
+    
+    Returns:
+      JSON with success message
+    """
+    try:
+        profile = load_profile()
+        if "personal_info" in profile:
+            del profile["personal_info"]
+            profile["last_updated"] = datetime.datetime.now().isoformat()
+            
+            # Save updated profile
+            os.makedirs(os.path.dirname(PROFILE_PATH) or ".", exist_ok=True)
+            import tempfile
+            fd, tmp_path = tempfile.mkstemp(dir=os.path.dirname(PROFILE_PATH) or ".")
+            try:
+                with os.fdopen(fd, "w", encoding="utf-8") as tmp:
+                    json.dump(profile, tmp, indent=2, ensure_ascii=False)
+                os.replace(tmp_path, PROFILE_PATH)
+            except Exception:
+                try:
+                    os.remove(tmp_path)
+                except Exception:
+                    pass
+                raise
+        
+        return jsonify({"message": "Personal information cleared successfully"})
+    except Exception as e:
+        logging.exception("/clear_personal_info failed")
+        return jsonify({"error": str(e)}), 500
+
+
 # ---- /get_history ------------------------------------------------
 @app.route("/get_history", methods=["POST"])
 def get_history():

test.sh

@@ -1,75 +1,103 @@
-#!/bin/bash
-# Test all Human-AI Backend Routes
-# Author: Swayam | AI Partner: GPT-5
+#!/usr/bin/env bash
+set -euo pipefail
 
-BASE_URL="https://swayamshetkar-human-ai-backend.hf.space"
+# Simple integration test script for Human-AI backend routes.
+# Tests: /train, /ask, /refactor, /code_assist
+# Usage:
+#   bash test.sh                # uses default BASE_URL=http://127.0.0.1:7860
+#   BASE_URL=http://localhost:7860 bash test.sh
+#   bash test.sh http://remote-host:7860
+#
+# Outputs are summarized; raw JSON stored in temporary files for inspection.
 
-divider() {
-  echo ""
-  echo "==================== $1 ===================="
+BASE_URL="${1:-${BASE_URL:-https://swayamshetkar-human-aii.hf.space}}"
+echo "Using BASE_URL: $BASE_URL"
+
+TMP_DIR="$(mktemp -d)"
+cleanup() { rm -rf "$TMP_DIR"; }
+trap cleanup EXIT
+
+header() { echo -e "\n==================== $1 ===================="; }
+
+jq_exists() { command -v jq >/dev/null 2>&1; }
+pretty() {
+	if jq_exists; then
+		jq '.'
+	else
+		cat
+	fi
 }
 
-# 1️⃣ Test / (Home)
-divider "Testing /"
-curl -s -X GET "$BASE_URL/" | jq
+fail() { echo "[FAIL] $1" >&2; exit 1; }
 
-# 2️⃣ Test /ask
-divider "Testing /ask (study mode)"
-curl -s -X POST "$BASE_URL/ask" \
-  -H "Content-Type: application/json" \
-  -d '{
-    "query": "Explain recursion in Python",
-    "metadata": {"skill": "moderate_learner", "emotion": "focused"}
-  }' | jq
+check_http() {
+	local code="$1"; local name="$2";
+	if [[ "$code" -ge 200 && "$code" -lt 300 ]]; then
+		echo "[OK] $name (HTTP $code)";
+	else
+		fail "$name returned HTTP $code";
+	fi
+}
 
-# 3️⃣ Test /ask_stream
-divider "Testing /ask_stream (study mode streaming)"
-curl -s -N -X POST "$BASE_URL/ask_stream" \
-  -H "Content-Type: application/json" \
-  -d '{
-    "query": "Explain how binary search works",
-    "metadata": {"skill": "slow_learner", "emotion": "curious"}
-  }'
+# 1. /train (profile training)
+header "POST /train"
+TRAIN_PAYLOAD='{"code": "def greet(name):\n    return f\\"Hello, {name}!\\""}'
+train_resp_file="$TMP_DIR/train.json"
+train_status=$(curl -s -o "$train_resp_file" -w '%{http_code}' \
+	-H 'Content-Type: application/json' \
+	-d "$TRAIN_PAYLOAD" \
+	"$BASE_URL/train")
+check_http "$train_status" "/train"
+echo "Response:"; cat "$train_resp_file" | pretty
 
-# 4️⃣ Test /code_assist
-divider "Testing /code_assist (code generation using trained style)"
-curl -s -X POST "$BASE_URL/code_assist" \
-  -H "Content-Type: application/json" \
-  -d '{
-    "query": "Write a Python function that sorts a list of numbers.",
-    "metadata": {"skill": "fast_learner", "emotion": "focused"}
-  }' | jq
+# 2. /ask (tutoring answer) - capture session_id
+header "POST /ask"
+ASK_PAYLOAD='{"query": "Explain what a Python decorator does in one sentence","metadata": {"skill": "intermediate", "emotion": "focused"}}'
+ask_resp_file="$TMP_DIR/ask.json"
+ask_status=$(curl -s -o "$ask_resp_file" -w '%{http_code}' \
+	-H 'Content-Type: application/json' \
+	-d "$ASK_PAYLOAD" \
+	"$BASE_URL/ask")
+check_http "$ask_status" "/ask"
+echo "Response:"; cat "$ask_resp_file" | pretty
 
-# 5️⃣ Test /analyze
-divider "Testing /analyze (inline code style update)"
-curl -s -X POST "$BASE_URL/analyze" \
-  -H "Content-Type: application/json" \
-  -d '{
-    "code": "def add_numbers(a,b):\\n    return a+b"
-  }' | jq
+if jq_exists; then
+	SESSION_ID=$(jq -r '.session_id // empty' "$ask_resp_file")
+else
+	SESSION_ID=$(grep -o '"session_id":"[^"]*"' "$ask_resp_file" | head -n1 | cut -d'"' -f4 || true)
+fi
+[[ -n "$SESSION_ID" ]] || fail "Could not extract session_id from /ask response"
+echo "Captured session_id: $SESSION_ID"
 
-# 6️⃣ Test /refactor
-divider "Testing /refactor (refactor code in user style)"
-curl -s -X POST "$BASE_URL/refactor" \
-  -H "Content-Type: application/json" \
-  -d '{
-    "code": "def add_numbers(a,b):\\n  c=a+b\\n  return c"
-  }' | jq
+# 3. /refactor
+header "POST /refactor"
+REFAC_PAYLOAD='{"code": "def add(a,b):\n return a+b"}'
+refac_resp_file="$TMP_DIR/refactor.json"
+refac_status=$(curl -s -o "$refac_resp_file" -w '%{http_code}' \
+	-H 'Content-Type: application/json' \
+	-d "$REFAC_PAYLOAD" \
+	"$BASE_URL/refactor")
+check_http "$refac_status" "/refactor"
+echo "Response:"; cat "$refac_resp_file" | pretty
 
-# 7️⃣ Test /train_profile
-divider "Testing /train_profile (train coding style on uploaded files)"
-curl -s -X POST "$BASE_URL/train_profile" \
-  -H "Content-Type: application/json" \
-  -d '{
-    "files": [
-      "def greet(name):\\n    print(f\"Hello {name}\")",
-      "class Person:\\n  def __init__(self, name):\\n    self.name = name"
-    ]
-  }' | jq
+# 4. /code_assist (reuse session history)
+header "POST /code_assist"
+ASSIST_PAYLOAD=$(cat <<EOF
+{"query": "Give me a Python function to compute Fibonacci using iteration","metadata": {"skill": "intermediate", "emotion": "curious"}, "session_id": "$SESSION_ID"}
+EOF
+)
+assist_resp_file="$TMP_DIR/code_assist.json"
+assist_status=$(curl -s -o "$assist_resp_file" -w '%{http_code}' \
+	-H 'Content-Type: application/json' \
+	-d "$ASSIST_PAYLOAD" \
+	"$BASE_URL/code_assist")
+check_http "$assist_status" "/code_assist"
+echo "Response:"; cat "$assist_resp_file" | pretty
 
-# 8️⃣ Test /profile
-divider "Testing /profile (get current trained profile)"
-curl -s -X GET "$BASE_URL/profile" | jq
+header "SUMMARY"
+echo "Train:   $train_status -> $train_resp_file"
+echo "Ask:     $ask_status -> $ask_resp_file (session_id=$SESSION_ID)"
+echo "Refactor:$refac_status -> $refac_resp_file"
+echo "Assist:  $assist_status -> $assist_resp_file"
+echo "All tests completed successfully."
 
-echo ""
-echo "✅ All routes tested successfully!"