Automated deployment via API

app.py

@@ -357,15 +357,8 @@ def upload_data():
         logger.error(f"❌ Upload error: {e}")
         return jsonify({'error': str(e)}), 500
 
-from huggingface_hub import InferenceClient
-import os
-
-# Initialize Inference Client
-# Using Zephyr-7B Beta: The gold standard for free-tier Spaces hosting
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta", token=os.environ.get("HF_TOKEN"))
-
 def generate_ai_response(query):
-    """Generate AI response using direct InferenceClient"""
+    """Generate AI response using local logic - no external API needed"""
     
     # 1. GATHER LIVE CONTEXT
     current_temp = app_state.get_temperature()
@@ -374,40 +367,80 @@ def generate_ai_response(query):
     risk = app_state.anomaly_risk * 100
     opt_low = CONFIG['TEMP_OPTIMAL_LOW']
     opt_high = CONFIG['TEMP_OPTIMAL_HIGH']
-
-    # 2. PREPARE PROMPT
-    # Phi-3 follows a specific chat template vs Mistral/Zephyr
-    system_prompt = f"""
-    You are the IronGuard Foundry AI.
-    LIVE METRICS:
-    - Temp: {current_temp:.1f}C (Target: {opt_low}-{opt_high})
-    - Energy: {energy:.1f} kWh
-    - Anomaly Risk: {risk:.1f}%
-
-    INSTRUCTIONS:
-    - Answer based on LIVE METRICS.
-    - If Temp > {CONFIG['TEMP_MAX']}, warn immediately.
-    - Keep answers short and professional.
-    """
-
-    # 3. CALL API
+    
+    query_lower = query.lower().strip()
+    
+    # 2. INTELLIGENT LOCAL RESPONSE ENGINE
     try:
-        messages = [
-            {"role": "user", "content": system_prompt + "\nUSER QUESTION: " + query}
-        ]
-        
-        response = ""
-        # Stream response to ensure connectivity
-        for message in client.chat_completion(messages, max_tokens=150, stream=True):
-            if message.choices and message.choices[0].delta.content:
-                response += message.choices[0].delta.content
-                
-        return response.strip()
+        # Temperature status
+        if current_temp < opt_low:
+            temp_status = "below optimal"
+            temp_advice = "Consider increasing furnace power to reach target range."
+        elif current_temp > opt_high:
+            temp_status = "above optimal"
+            temp_advice = "Reduce heat input or increase cooling to stabilize."
+        else:
+            temp_status = "within optimal range"
+            temp_advice = "Maintain current settings for best results."
+        
+        # Anomaly warning
+        anomaly_warning = ""
+        if is_anomaly or risk > 50:
+            anomaly_warning = f"⚠️ ALERT: Anomaly risk at {risk:.1f}%. Immediate attention recommended. "
+        
+        # Query-based responses
+        if any(word in query_lower for word in ['hi', 'hello', 'hey', 'greetings']):
+            return f"Hello! I'm your Forge AI assistant. Current furnace temp is {current_temp:.1f}°C ({temp_status}). How can I help you today?"
+        
+        elif any(word in query_lower for word in ['temperature', 'temp', 'heat', 'hot', 'cold']):
+            return f"{anomaly_warning}Current temperature: {current_temp:.1f}°C ({temp_status}). Target range: {opt_low}-{opt_high}°C. {temp_advice}"
+        
+        elif any(word in query_lower for word in ['energy', 'power', 'consumption', 'kwh']):
+            efficiency = "optimal" if 420 <= energy <= 480 else "needs attention"
+            return f"Energy consumption: {energy:.1f} kWh. Status: {efficiency}. Current temp: {current_temp:.1f}°C."
+        
+        elif any(word in query_lower for word in ['anomaly', 'risk', 'alert', 'warning', 'danger']):
+            if is_anomaly:
+                return f"⚠️ ANOMALY DETECTED! Risk level: {risk:.1f}%. Temperature: {current_temp:.1f}°C. Recommend immediate inspection of furnace parameters."
+            else:
+                return f"✅ System normal. Risk level: {risk:.1f}%. All parameters within acceptable bounds."
+        
+        elif any(word in query_lower for word in ['status', 'overview', 'summary', 'report']):
+            status_icon = "⚠️" if is_anomaly else "✅"
+            return f"{status_icon} System Status:\n• Temperature: {current_temp:.1f}°C ({temp_status})\n• Energy: {energy:.1f} kWh\n• Risk Level: {risk:.1f}%\n• {temp_advice}"
+        
+        elif any(word in query_lower for word in ['pour', 'pouring', 'ready', 'readiness', 'cast']):
+            if opt_low <= current_temp <= opt_high and risk < 30:
+                return f"✅ POURING READY! Temperature {current_temp:.1f}°C is optimal. Risk level low at {risk:.1f}%. Proceed with pour."
+            else:
+                issues = []
+                if current_temp < opt_low:
+                    issues.append(f"temp too low ({current_temp:.1f}°C)")
+                elif current_temp > opt_high:
+                    issues.append(f"temp too high ({current_temp:.1f}°C)")
+                if risk >= 30:
+                    issues.append(f"elevated risk ({risk:.1f}%)")
+                return f"⚠️ NOT READY FOR POUR. Issues: {', '.join(issues)}. Wait for stabilization."
+        
+        elif any(word in query_lower for word in ['optimize', 'efficiency', 'improve', 'better']):
+            return f"Optimization tips:\n• Target temp: {opt_low}-{opt_high}°C (current: {current_temp:.1f}°C)\n• Optimal energy: ~450 kWh (current: {energy:.1f} kWh)\n• {temp_advice}"
+        
+        elif any(word in query_lower for word in ['help', 'what can you do', 'commands', 'options']):
+            return "I can help with:\n• Temperature monitoring & alerts\n• Energy consumption analysis\n• Anomaly detection & risk assessment\n• Pouring readiness checks\n• System optimization tips\nJust ask about any of these!"
+        
+        elif any(word in query_lower for word in ['safety', 'safe', 'hazard']):
+            if is_anomaly or current_temp > CONFIG['TEMP_MAX'] - 20:
+                return f"⚠️ SAFETY CONCERN: Temperature at {current_temp:.1f}°C with {risk:.1f}% risk. Monitor closely and ensure safety protocols are active."
+            else:
+                return f"✅ Safety status: Normal. Temperature {current_temp:.1f}°C within safe limits. Risk level: {risk:.1f}%."
+        
+        else:
+            # Default intelligent response
+            return f"{anomaly_warning}Current readings - Temp: {current_temp:.1f}°C ({temp_status}), Energy: {energy:.1f} kWh, Risk: {risk:.1f}%. Ask me about temperature, energy, anomalies, pouring readiness, or safety!"
 
     except Exception as e:
-        logger.error(f"❌ AI Error: {e}")
-        # Return the raw error so user can debug (likely Token issue)
-        return f"⚠️ **Connection Error**: {str(e)}"
+        logger.error(f"❌ Response generation error: {e}")
+        return f"System operational. Temp: {current_temp:.1f}°C, Energy: {energy:.1f} kWh. How can I assist?"
 
 @app.route('/api/chat', methods=['POST'])
 def chat():