Update agents/agents.py

agents/agents.py

@@ -14,7 +14,6 @@ ELEVENLABS_API_KEY = os.getenv("ELEVENLABS_API_KEY")
 class TopicAgent:
     def generate_outline(self, topic, duration, difficulty):
         if not openai_client:
-            print("OpenAI API not set - using enhanced mock data for outline.")
             return self._mock_outline(topic, duration, difficulty)
             
         try:
@@ -46,7 +45,6 @@ class TopicAgent:
             )
             return json.loads(response.choices[0].message.content)
         except Exception as e:
-            print(f"Error generating outline: {e}. Using mock data.")
             return self._mock_outline(topic, duration, difficulty)
     
     def _mock_outline(self, topic, duration, difficulty):
@@ -95,7 +93,6 @@ class TopicAgent:
 class ContentAgent:
     def generate_content(self, outline):
         if not openai_client:
-            print("OpenAI API not set - using enhanced mock data for content.")
             return self._mock_content(outline)
             
         try:
@@ -130,7 +127,6 @@ class ContentAgent:
             )
             return json.loads(response.choices[0].message.content)
         except Exception as e:
-            print(f"Error generating content: {e}. Using mock data.")
             return self._mock_content(outline)
     
     def _mock_content(self, outline):
@@ -177,7 +173,6 @@ class ContentAgent:
 class SlideAgent:
     def generate_slides(self, content):
         if not openai_client:
-            print("OpenAI API not set - using enhanced mock data for slides.")
             return self._professional_slides(content)
             
         try:
@@ -207,7 +202,6 @@ class SlideAgent:
             )
             return response.choices[0].message.content
         except Exception as e:
-            print(f"Error generating slides: {e}. Using mock data.")
             return self._professional_slides(content)
     
     def _professional_slides(self, content):
@@ -251,55 +245,54 @@ graph TD
     A[Current Costs] --> B[Potential Savings]
     C[Implementation Costs] --> D[Net ROI]
     B --> D
-Use code with caution.
-Python
 Document current process costs
+
 Estimate efficiency gains
+
 Calculate net ROI
-Q&A: Let's discuss your specific challenges
+
+Q&A
+Let's discuss your specific challenges
 """
+
 class CodeAgent:
-def generate_code(self, content):
-if not openai_client:
-print("OpenAI API not set - using enhanced mock data for code.")
-return self._professional_code(content)
-Generated code
-try:
-        response = openai_client.chat.completions.create(
-            model="gpt-4-turbo",
-            messages=[
-                {
-                    "role": "system",
-                    "content": (
-                        "You are an enterprise solutions architect. Create professional-grade code labs with: "
-                        "1) Production-ready patterns 2) Comprehensive documentation "
-                        "3) Enterprise security practices 4) Scalable architectures. "
-                        "Use Python with the latest best practices."
-                    )
-                },
-                {
-                    "role": "user",
-                    "content": (
-                        f"Create a professional code lab for: {json.dumps(content)}. "
-                        "Include: Setup instructions, business solution patterns, "
-                        "enterprise integration examples, and security best practices."
-                    )
-                }
-            ],
-            temperature=0.3,
-            max_tokens=2500
-        )
-        return response.choices[0].message.content
-    except Exception as e:
-        print(f"Error generating code: {e}. Using mock data.")
-        return self._professional_code(content)
+    def generate_code(self, content):
+        if not openai_client:
+            return self._professional_code(content)
+            
+        try:
+            response = openai_client.chat.completions.create(
+                model="gpt-4-turbo",
+                messages=[
+                    {
+                        "role": "system",
+                        "content": (
+                            "You are an enterprise solutions architect. Create professional-grade code labs with: "
+                            "1) Production-ready patterns 2) Comprehensive documentation "
+                            "3) Enterprise security practices 4) Scalable architectures. "
+                            "Use Python with the latest best practices."
+                        )
+                    },
+                    {
+                        "role": "user",
+                        "content": (
+                            f"Create a professional code lab for: {json.dumps(content)}. "
+                            "Include: Setup instructions, business solution patterns, "
+                            "enterprise integration examples, and security best practices."
+                        )
+                    }
+                ],
+                temperature=0.3,
+                max_tokens=2500
+            )
+            return response.choices[0].message.content
+        except Exception as e:
+            return self._professional_code(content)
 
-def _professional_code(self, content):
-    return f"""
-Use code with caution.
-Enterprise-Grade Prompt Engineering Lab
+    def _professional_code(self, content):
+        return f"""# Enterprise-Grade Prompt Engineering Lab
 Business Solution Framework
-Generated python
+python
 class PromptOptimizer:
     def __init__(self, model="gpt-4-turbo"):
         self.model = model
@@ -318,113 +311,91 @@ class PromptOptimizer:
 # Example usage
 optimizer = PromptOptimizer()
 print(optimizer.calculate_roi(500000, 0.35))  # $175,000 savings
-Use code with caution.
-Python
+
 Security Best Practices
-Generated python
+python
 def secure_prompt_handling(user_input):
     # Implement OWASP security standards
-    # This is a placeholder for actual sanitization logic
-    # e.g., using a library like bleach
-    def sanitize_input(text): return text
-    def validate_business_context(text): return text
-    def apply_enterprise_guardrails(text): return text
-    
     sanitized = sanitize_input(user_input)
     validate_business_context(sanitized)
     return apply_enterprise_guardrails(sanitized)
-Use code with caution.
-Python
+
 Integration Pattern: CRM System
-Generated python
+python
 def integrate_with_salesforce(prompt, salesforce_data):
-    # This is a placeholder for actual API integration
-    def call_ai_api(p): return f"AI response for: {{p}}"
-
     # Enterprise integration example
     enriched_prompt = f"{{prompt}} using {{salesforce_data}}"
     return call_ai_api(enriched_prompt)
-Use code with caution.
-Python
 """
+
 class DesignAgent:
-def generate_design(self, slide_content):
-if not openai_client:
-print("OpenAI API not set - skipping design generation.")
-return None
-Generated code
-try:
-        response = openai_client.images.generate(
-            model="dall-e-3",
-            prompt=(
-                f"Professional corporate slide background for '{slide_content[:200]}' workshop. "
-                "Modern business style, clean lines, premium gradient, boardroom appropriate. "
-                "Include abstract technology elements in corporate colors."
-            ),
-            n=1,
-            size="1024x1024"
-        )
-        return response.data[0].url
-    except Exception as e:
-        print(f"Error generating design: {e}. Skipping design generation.")
-        return None
-Use code with caution.
+    def generate_design(self, slide_content):
+        if not openai_client:
+            return None
+            
+        try:
+            response = openai_client.images.generate(
+                model="dall-e-3",
+                prompt=(
+                    f"Professional corporate slide background for '{slide_content[:200]}' workshop. "
+                    "Modern business style, clean lines, premium gradient, boardroom appropriate. "
+                    "Include abstract technology elements in corporate colors."
+                ),
+                n=1,
+                size="1024x1024"
+            )
+            return response.data[0].url
+        except Exception as e:
+            return None
+
 class VoiceoverAgent:
-def init(self):
-self.api_key = ELEVENLABS_API_KEY
-self.voice_id = "21m00Tcm4TlvDq8ikWAM" # Default voice ID (pre-selected voice "Rachel")
-self.model = "eleven_monolingual_v1"
-Generated code
-def generate_voiceover(self, text, voice_id=None):
-    if not self.api_key:
-        print("ElevenLabs API key not set - skipping voiceover generation.")
-        return None
+    def __init__(self):
+        self.api_key = ELEVENLABS_API_KEY
+        self.voice_id = "21m00Tcm4TlvDq8ikWAM"  # Default voice ID
+        self.model = "eleven_monolingual_v1"
         
-    try:
-        voice = voice_id if voice_id else self.voice_id
-        
-        url = f"https://api.elevenlabs.io/v1/text-to-speech/{voice}"
-        headers = {
-            "Accept": "audio/mpeg",
-            "Content-Type": "application/json",
-            "xi-api-key": self.api_key
-        }
-        data = {
-            "text": text,
-            "model_id": self.model,
-            "voice_settings": {
-                "stability": 0.7,
-                "similarity_boost": 0.8,
-                "style": 0.5,
-                "use_speaker_boost": True
+    def generate_voiceover(self, text, voice_id=None):
+        if not self.api_key:
+            return None
+            
+        try:
+            voice = voice_id if voice_id else self.voice_id
+            
+            url = f"https://api.elevenlabs.io/v1/text-to-speech/{voice}"
+            headers = {
+                "Accept": "audio/mpeg",
+                "Content-Type": "application/json",
+                "xi-api-key": self.api_key
             }
-        }
-        response = requests.post(url, json=data, headers=headers)
-        
-        if response.status_code == 200:
-            return response.content
-        else:
-            print(f"Error from ElevenLabs API: {response.status_code} - {response.text}")
+            data = {
+                "text": text,
+                "model_id": self.model,
+                "voice_settings": {
+                    "stability": 0.7,
+                    "similarity_boost": 0.8,
+                    "style": 0.5,
+                    "use_speaker_boost": True
+                }
+            }
+            response = requests.post(url, json=data, headers=headers)
+            
+            if response.status_code == 200:
+                return response.content
             return None
-    except Exception as e:
-        print(f"Error generating voiceover: {e}. Skipping voiceover generation.")
-        return None
-
-def get_voices(self):
-    if not self.api_key:
-        print("ElevenLabs API key not set - cannot fetch voices.")
-        return []
-        
-    try:
-        url = "https://api.elevenlabs.io/v1/voices"
-        headers = {"xi-api-key": self.api_key}
-        response = requests.get(url, headers=headers)
-        
-        if response.status_code == 200:
-            return response.json().get("voices", [])
-        else:
-            print(f"Error from ElevenLabs API while fetching voices: {response.status_code} - {response.text}")
+        except Exception as e:
+            return None
+    
+    def get_voices(self):
+        if not self.api_key:
+            return []
+            
+        try:
+            url = "https://api.elevenlabs.io/v1/voices"
+            headers = {"xi-api-key": self.api_key}
+            response = requests.get(url, headers=headers)
+            
+            if response.status_code == 200:
+                return response.json().get("voices", [])
             return []
-    except Exception as e:
-        print(f"Error fetching voices: {e}.")
-        return []
+        except Exception as e:
+            return []