Update agents/agents.py

agents/agents.py

@@ -14,6 +14,7 @@ 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:
@@ -45,6 +46,7 @@ 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):
@@ -93,6 +95,7 @@ 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:
@@ -127,6 +130,7 @@ 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):
@@ -173,6 +177,7 @@ 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:
@@ -202,6 +207,7 @@ 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):
@@ -245,54 +251,55 @@ 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:
-            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 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 _professional_code(self, content):
-        return f"""# Enterprise-Grade Prompt Engineering Lab
+def _professional_code(self, content):
+    return f"""
+Use code with caution.
+Enterprise-Grade Prompt Engineering Lab
 Business Solution Framework
-python
+Generated python
 class PromptOptimizer:
     def __init__(self, model="gpt-4-turbo"):
         self.model = model
@@ -311,91 +318,113 @@ class PromptOptimizer:
 # Example usage
 optimizer = PromptOptimizer()
 print(optimizer.calculate_roi(500000, 0.35))  # $175,000 savings
-
+Use code with caution.
+Python
 Security Best Practices
-python
+Generated 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
-python
+Generated 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:
-            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
-
+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.
 class VoiceoverAgent:
-    def __init__(self):
-        self.api_key = ELEVENLABS_API_KEY
-        self.voice_id = "21m00Tcm4TlvDq8ikWAM"  # Default voice ID
-        self.model = "eleven_monolingual_v1"
+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 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
-            }
-            data = {
-                "text": text,
-                "model_id": self.model,
-                "voice_settings": {
-                    "stability": 0.7,
-                    "similarity_boost": 0.8,
-                    "style": 0.5,
-                    "use_speaker_boost": True
-                }
+    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
             }
-            response = requests.post(url, json=data, headers=headers)
-            
-            if response.status_code == 200:
-                return response.content
-            return None
-        except Exception as e:
+        }
+        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}")
             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", [])
+    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}")
             return []
-        except Exception as e:
-            return []
+    except Exception as e:
+        print(f"Error fetching voices: {e}.")
+        return []