Update agents/agents.py

agents/agents.py

@@ -11,11 +11,12 @@ load_dotenv()
 openai_client = OpenAI(api_key=os.getenv("OPENAI_API_KEY")) if os.getenv("OPENAI_API_KEY") else None
 ELEVENLABS_API_KEY = os.getenv("ELEVENLABS_API_KEY")
 
+
 class TopicAgent:
     def generate_outline(self, topic, duration, difficulty):
         if not openai_client:
             return self._mock_outline(topic, duration, difficulty)
-            
+
         try:
             response = openai_client.chat.completions.create(
                 model="gpt-4-turbo",
@@ -44,9 +45,9 @@ class TopicAgent:
                 response_format={"type": "json_object"}
             )
             return json.loads(response.choices[0].message.content)
-        except Exception as e:
+        except Exception:
             return self._mock_outline(topic, duration, difficulty)
-    
+
     def _mock_outline(self, topic, duration, difficulty):
         return {
             "title": f"Mastering {topic} for Business Impact",
@@ -90,11 +91,12 @@ class TopicAgent:
             ]
         }
 
+
 class ContentAgent:
     def generate_content(self, outline):
         if not openai_client:
             return self._mock_content(outline)
-            
+
         try:
             response = openai_client.chat.completions.create(
                 model="gpt-4-turbo",
@@ -126,9 +128,9 @@ class ContentAgent:
                 response_format={"type": "json_object"}
             )
             return json.loads(response.choices[0].message.content)
-        except Exception as e:
+        except Exception:
             return self._mock_content(outline)
-    
+
     def _mock_content(self, outline):
         return {
             "workshop_title": outline.get("title", "Premium AI Workshop"),
@@ -170,11 +172,12 @@ class ContentAgent:
             ]
         }
 
+
 class SlideAgent:
     def generate_slides(self, content):
         if not openai_client:
             return self._professional_slides(content)
-            
+
         try:
             response = openai_client.chat.completions.create(
                 model="gpt-4-turbo",
@@ -201,9 +204,9 @@ class SlideAgent:
                 max_tokens=2500
             )
             return response.choices[0].message.content
-        except Exception as e:
+        except Exception:
             return self._professional_slides(content)
-    
+
     def _professional_slides(self, content):
         return f"""---
 marp: true
@@ -256,146 +259,156 @@ 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:
+return self._professional_code(content)
+
+python
+Copy
+Edit
+    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:
+        return self._professional_code(content)
 
-    def _professional_code(self, content):
-        return f"""# Enterprise-Grade Prompt Engineering Lab
+def _professional_code(self, content):
+    return f"""# Enterprise-Grade Prompt Engineering Lab
 Business Solution Framework
 python
 class PromptOptimizer:
-    def __init__(self, model="gpt-4-turbo"):
-        self.model = model
-        self.pattern_library = {{
-            "financial_analysis": "Extract key metrics from financial reports",
-            "customer_service": "Resolve tier-2 support tickets"
-        }}
-    
-    def optimize_prompt(self, business_case):
-        # Implement enterprise optimization logic
-        return f"Business-optimized prompt for {{business_case}}"
-    
-    def calculate_roi(self, current_cost, expected_efficiency):
-        return current_cost * expected_efficiency
-
-# Example usage
+def init(self, model="gpt-4-turbo"):
+self.model = model
+self.pattern_library = {{
+"financial_analysis": "Extract key metrics from financial reports",
+"customer_service": "Resolve tier-2 support tickets"
+}}
+
+ruby
+Copy
+Edit
+def optimize_prompt(self, business_case):
+    # Implement enterprise optimization logic
+    return f"Business-optimized prompt for {{business_case}}"
+
+def calculate_roi(self, current_cost, expected_efficiency):
+    return current_cost * expected_efficiency
+Example usage
 optimizer = PromptOptimizer()
-print(optimizer.calculate_roi(500000, 0.35))  # $175,000 savings
+print(optimizer.calculate_roi(500000, 0.35)) # $175,000 savings
 
 Security Best Practices
 python
 def secure_prompt_handling(user_input):
-    # Implement OWASP security standards
-    sanitized = sanitize_input(user_input)
-    validate_business_context(sanitized)
-    return apply_enterprise_guardrails(sanitized)
+# Implement OWASP security standards
+sanitized = sanitize_input(user_input)
+validate_business_context(sanitized)
+return apply_enterprise_guardrails(sanitized)
 
 Integration Pattern: CRM System
 python
 def integrate_with_salesforce(prompt, salesforce_data):
-    # Enterprise integration example
-    enriched_prompt = f"{{prompt}} using {{salesforce_data}}"
-    return call_ai_api(enriched_prompt)
+# Enterprise integration example
+enriched_prompt = f"{{prompt}} using {{salesforce_data}}"
+return call_ai_api(enriched_prompt)
 """
 
 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:
+return None
 
+kotlin
+Copy
+Edit
+    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:
+        return None
 class VoiceoverAgent:
-    def __init__(self):
-        self.api_key = ELEVENLABS_API_KEY
-        self.voice_id = "21m00Tcm4TlvDq8ikWAM"  # Default voice ID
-        self.model = "eleven_monolingual_v1"
-        
-    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
-                }
+def init(self):
+self.api_key = ELEVENLABS_API_KEY
+self.voice_id = "21m00Tcm4TlvDq8ikWAM" # Default voice ID
+self.model = "eleven_monolingual_v1"
+
+python
+Copy
+Edit
+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
             }
-            response = requests.post(url, json=data, headers=headers)
-            
-            if response.status_code == 200:
-                return response.content
-            return None
-        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:
-            return []
+        }
+        response = requests.post(url, json=data, headers=headers)
+
+        if response.status_code == 200:
+            return response.content
+        return None
+    except Exception:
+        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:
+        return []