Update app.py

app.py

@@ -10,6 +10,7 @@ import base64
 import textwrap
 from dotenv import load_dotenv
 from openai import OpenAI  # Updated OpenAI client
+from elevenlabs import ElevenLabs  # Official ElevenLabs SDK import
 
 # Load environment variables
 load_dotenv()
@@ -18,6 +19,8 @@ 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")
 
+eleven_client = ElevenLabs(api_key=ELEVENLABS_API_KEY) if ELEVENLABS_API_KEY else None
+
 # =============================
 # UPDATED AGENT IMPLEMENTATION (OpenAI v1.x compatible)
 # =============================
@@ -26,25 +29,18 @@ 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",
                 messages=[
-                    {
-                        "role": "system",
-                        "content": "You're an expert corporate trainer creating comprehensive AI workshop outlines."
-                    },
-                    {
-                        "role": "user",
-                        "content": (
-                            f"Create a detailed {duration}-hour {difficulty} workshop outline on {topic}. "
-                            "Include: 4-6 modules with specific learning objectives, hands-on exercises, "
-                            "and real-world case studies. Format as JSON with keys: "
-                            "{'topic', 'duration', 'difficulty', 'goals', 'modules': ["
-                            "{'title', 'duration', 'learning_objectives', 'case_study', 'exercises'}]}"
-                        )
-                    }
+                    {"role": "system", "content": "You're an expert corporate trainer creating comprehensive AI workshop outlines."},
+                    {"role": "user", "content": (
+                        f"Create a detailed {duration}-hour {difficulty} workshop outline on {topic}. "
+                        "Include: 4-6 modules with specific learning objectives, hands-on exercises, "
+                        "and real-world case studies. Format as JSON with keys: "
+                        "{'topic', 'duration', 'difficulty', 'goals', 'modules': ["
+                        "{'title', 'duration', 'learning_objectives', 'case_study', 'exercises'}]}"
+                    )}
                 ],
                 temperature=0.3,
                 max_tokens=1500,
@@ -52,9 +48,9 @@ class TopicAgent:
             )
             return json.loads(response.choices[0].message.content)
         except Exception as e:
-            st.error(f"Outline generation error: {str(e)}")
+            st.error(f"Outline generation error: {e}")
             return self._mock_outline(topic, duration, difficulty)
-    
+
     def _mock_outline(self, topic, duration, difficulty):
         return {
             "topic": topic,
@@ -102,25 +98,18 @@ 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",
                 messages=[
-                    {
-                        "role": "system",
-                        "content": "You're a corporate training content developer creating detailed workshop materials."
-                    },
-                    {
-                        "role": "user",
-                        "content": (
-                            f"Expand this workshop outline into comprehensive content: {json.dumps(outline)}. "
-                            "For each module, include: detailed script (3-5 paragraphs), speaker notes (bullet points), "
-                            "3 quiz questions with explanations, and exercise instructions. Format as JSON with keys: "
-                            "{'workshop_title', 'modules': [{'title', 'script', 'speaker_notes', 'quiz': ["
-                            "{'question', 'options', 'answer', 'explanation'}], 'exercise_instructions'}]}"
-                        )
-                    }
+                    {"role": "system", "content": "You're a corporate training content developer creating detailed workshop materials."},
+                    {"role": "user", "content": (
+                        f"Expand this workshop outline into comprehensive content: {json.dumps(outline)}. "
+                        "For each module, include: detailed script (3-5 paragraphs), speaker notes (bullet points), "
+                        "3 quiz questions with explanations, and exercise instructions. Format as JSON with keys: "
+                        "{'workshop_title', 'modules': [{'title', 'script', 'speaker_notes', 'quiz': ["
+                        "{'question', 'options', 'answer', 'explanation'}], 'exercise_instructions'}]}"
+                    )}
                 ],
                 temperature=0.4,
                 max_tokens=2000,
@@ -128,9 +117,9 @@ class ContentAgent:
             )
             return json.loads(response.choices[0].message.content)
         except Exception as e:
-            st.error(f"Content generation error: {str(e)}")
+            st.error(f"Content generation error: {e}")
             return self._mock_content(outline)
-    
+
     def _mock_content(self, outline):
         return {
             "workshop_title": f"Mastering {outline['topic']}",
@@ -144,12 +133,10 @@ class ContentAgent:
                         "Discuss token limitations and their impact"
                     ],
                     "quiz": [
-                        {
-                            "question": "What's the most important element of a good prompt?",
-                            "options": ["Length", "Specificity", "Complexity", "Creativity"],
-                            "answer": "Specificity",
-                            "explanation": "Specific prompts yield more accurate and relevant responses"
-                        }
+                        {"question": "What's the most important element of a good prompt?",
+                         "options": ["Length", "Specificity", "Complexity", "Creativity"],
+                         "answer": "Specificity",
+                         "explanation": "Specific prompts yield more accurate and relevant responses"}
                     ],
                     "exercise_instructions": "Create a prompt that extracts key insights from a financial report..."
                 }
@@ -160,33 +147,26 @@ class SlideAgent:
     def generate_slides(self, content):
         if not openai_client:
             return self._mock_slides(content)
-            
         try:
             response = openai_client.chat.completions.create(
                 model="gpt-4-turbo",
                 messages=[
-                    {
-                        "role": "system",
-                        "content": "You create professional slide decks in Markdown format using Marp syntax."
-                    },
-                    {
-                        "role": "user",
-                        "content": (
-                            f"Create a slide deck for this workshop content: {json.dumps(content)}. "
-                            "Use Marp Markdown format with themes and visual elements. "
-                            "Include: title slide, module slides with key points, case studies, "
-                            "exercise instructions, and summary slides. Make it visually appealing."
-                        )
-                    }
+                    {"role": "system", "content": "You create professional slide decks in Markdown format using Marp syntax."},
+                    {"role": "user", "content": (
+                        f"Create a slide deck for this workshop content: {json.dumps(content)}. "
+                        "Use Marp Markdown format with themes and visual elements. "
+                        "Include: title slide, module slides with key points, case studies, "
+                        "exercise instructions, and summary slides. Make it visually appealing."
+                    )}
                 ],
                 temperature=0.2,
                 max_tokens=2500
             )
             return response.choices[0].message.content
         except Exception as e:
-            st.error(f"Slide generation error: {str(e)}")
+            st.error(f"Slide generation error: {e}")
             return self._mock_slides(content)
-    
+
     def _mock_slides(self, content):
         return f"""---
 marp: true
@@ -221,41 +201,34 @@ backgroundImage: url('https://marp.app/assets/hero-background.svg')
 1. Craft effective prompts for different scenarios
 2. Optimize prompts for specific AI models
 3. Analyze and refine prompt performance
-
 """
 
 class CodeAgent:
     def generate_code(self, content):
         if not openai_client:
             return self._mock_code(content)
-            
         try:
             response = openai_client.chat.completions.create(
                 model="gpt-4-turbo",
                 messages=[
-                    {
-                        "role": "system",
-                        "content": "You create practical code labs for technical workshops."
-                    },
-                    {
-                        "role": "user",
-                        "content": (
-                            f"Create a Jupyter notebook with code exercises for this workshop: {json.dumps(content)}. "
-                            "Include: setup instructions, practical exercises with solutions, "
-                            "and real-world implementation examples. Use Python with popular AI libraries."
-                        )
-                    }
+                    {"role": "system", "content": "You create practical code labs for technical workshops."},
+                    {"role": "user", "content": (
+                        f"Create a Jupyter notebook with code exercises for this workshop: {json.dumps(content)}. "
+                        "Include: setup instructions, practical exercises with solutions, "
+                        "and real-world implementation examples. Use Python with popular AI libraries."
+                    )}
                 ],
                 temperature=0.3,
                 max_tokens=2000
             )
             return response.choices[0].message.content
         except Exception as e:
-            st.error(f"Code generation error: {str(e)}")
+            st.error(f"Code generation error: {e}")
             return self._mock_code(content)
-    
+
     def _mock_code(self, content):
-        return f"""# {content['workshop_title']} - Code Labs
+        return f"""
+# {content['workshop_title']} - Code Labs
 
 import openai
 import pandas as pd
@@ -264,11 +237,10 @@ import pandas as pd
 def generate_response(prompt):
     response = openai.chat.completions.create(
         model="gpt-4",
-        messages=[{{"role": "user", "content": prompt}}]
+        messages=[{"role": "user", "content": prompt}]
     )
     return response.choices[0].message.content
 
-# Test your function
 print(generate_response("Explain quantum computing in simple terms"))
 
 ## Exercise 2: Advanced Prompt Patterns
@@ -283,7 +255,6 @@ class DesignAgent:
     def generate_design(self, slide_content):
         if not openai_client:
             return None
-            
         try:
             response = openai_client.images.generate(
                 prompt=f"Create a professional slide background for a corporate AI workshop about: {slide_content[:500]}",
@@ -292,65 +263,45 @@ class DesignAgent:
             )
             return response.data[0].url
         except Exception as e:
-            st.error(f"Design generation error: {str(e)}")
+            st.error(f"Design generation error: {e}")
             return None
 
 class VoiceoverAgent:
     def __init__(self):
-        self.api_key = ELEVENLABS_API_KEY
-        self.voice_id = "9BWtsMINqrJLrRacOk9x"  # Default voice ID
-        self.model = "eleven_monolingual_v1"
-        
+        self.client = eleven_client
+        self.default_voice_id = "9BWtsMINqrJLrRacOk9x"  # Default fallback voice
+        self.model_id = "eleven_monolingual_v1"
+
+    def get_voices(self):
+        if not self.client:
+            return []
+        try:
+            voices = self.client.voices.list()
+            return [{"voice_id": v.voice_id, "name": v.name} for v in voices]
+        except Exception as e:
+            st.error(f"Voice loading error: {e}")
+            return []
+
     def generate_voiceover(self, text, voice_id=None):
-        if not self.api_key:
+        if not self.client:
             return None
-            
         try:
-            # Use custom voice if provided, otherwise use default
-            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": {
+            vid = voice_id or self.default_voice_id
+            audio = self.client.text_to_speech.convert(
+                text=text,
+                voice_id=vid,
+                model_id=self.model_id,
+                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
-            else:
-                st.error(f"Voiceover API error: {response.status_code} - {response.text}")
-                return None
+            )
+            return audio
         except Exception as e:
-            st.error(f"Voiceover generation error: {str(e)}")
+            st.error(f"Voiceover generation error: {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:
-            st.error(f"Voice loading error: {str(e)}")
-            return []
 
 # Initialize agents
 topic_agent = TopicAgent()
@@ -370,14 +321,13 @@ st.set_page_config(
     initial_sidebar_state="expanded"
 )
 
-# Custom CSS with fixed input styling
+# Custom CSS
 st.markdown("""
 <style>
     .stApp {
         background: linear-gradient(135deg, #6a11cb 0%, #2575fc 100%);
         color: #fff;
     }
-    /* Fix for input text color */
     .stTextInput>div>div>input {
         color: #333 !important;
         background-color: #fff !important;
@@ -413,275 +363,4 @@ st.markdown("""
         display: flex;
         align-items: center;
         margin: 5px 0;
-        padding: 8px;
-        border-radius: 8px;
-        cursor: pointer;
-        transition: background 0.3s;
-    }
-    .voice-option:hover {
-        background: rgba(255,255,255,0.2);
-    }
-    .voice-option.selected {
-        background: rgba(0,180,155,0.3);
-        border: 2px solid #00b09b;
-    }
-    .voice-thumb {
-        width: 40px;
-        height: 40px;
-        border-radius: 50%;
-        margin-right: 10px;
-        object-fit: cover;
-    }
-</style>
-""", unsafe_allow_html=True)
-
-# Header
-col1, col2 = st.columns([1, 3])
-with col1:
-    st.image("https://cdn-icons-png.flaticon.com/512/1995/1995485.png", width=100)
-with col2:
-    st.title("🤖 Workshop in a Box Pro")
-    st.caption("Generate Premium Corporate AI Training Workshops with Voiceovers")
-
-# Initialize session state
-if 'workshop_topic' not in st.session_state:
-    st.session_state.workshop_topic = "Advanced Prompt Engineering"
-if 'generated' not in st.session_state:
-    st.session_state.generated = False
-if 'generating' not in st.session_state:
-    st.session_state.generating = False
-if 'voiceovers' not in st.session_state:
-    st.session_state.voiceovers = {}
-if 'selected_voice' not in st.session_state:
-    st.session_state.selected_voice = "21m00Tcm4TlvDq8ikWAM"  # Default voice ID
-
-# Sidebar configuration
-with st.sidebar:
-    st.header("⚙️ Workshop Configuration")
-    
-    # Workshop topic input with session state
-    st.session_state.workshop_topic = st.text_input(
-        "Workshop Topic", 
-        st.session_state.workshop_topic,
-        key="topic_input",
-        help="Enter the main topic for your workshop"
-    )
-    
-    # Validate topic input
-    if st.session_state.workshop_topic.strip() == "":
-        st.warning("Please enter a workshop topic")
-        st.stop()
-    
-    duration = st.slider("Duration (hours)", 1.0, 8.0, 3.0, 0.5)
-    difficulty = st.selectbox("Difficulty Level", 
-                            ["Beginner", "Intermediate", "Advanced", "Expert"])
-    include_code = st.checkbox("Include Code Labs", True)
-    include_design = st.checkbox("Generate Visual Designs", True)
-    include_voiceover = st.checkbox("Generate Voiceovers", True)
-    
-    # Voice selection
-    if include_voiceover:
-        st.subheader("🎙️ Voice Selection")
-        
-        # Get available voices
-        voices = voiceover_agent.get_voices()
-        
-        # If we have voices, let the user select one
-        if voices:
-            # Create 2 columns for voice selection
-            cols = st.columns(2)
-            for i, voice in enumerate(voices[:4]):  # Show first 4 voices
-                with cols[i % 2]:
-                    # Create a unique key for each voice button
-                    voice_key = f"voice_{voice['voice_id']}"
-                    
-                    # Display voice option
-                    if st.button(
-                        f"🗣️ {voice['name']}",
-                        key=voice_key,
-                        use_container_width=True,
-                        help=f"Select {voice['name']} voice"
-                    ):
-                        st.session_state.selected_voice = voice['voice_id']
-            
-            # Show which voice is currently selected
-            selected_voice_name = next((v['name'] for v in voices if v['voice_id'] == st.session_state.selected_voice), "Default")
-            st.info(f"Selected Voice: **{selected_voice_name}**")
-        else:
-            if ELEVENLABS_API_KEY:
-                st.warning("Couldn't load voices. Using default voice.")
-            else:
-                st.warning("ElevenLabs API key not set. Voiceovers disabled.")
-    
-    if st.button("✨ Generate Workshop", type="primary", use_container_width=True):
-        st.session_state.generating = True
-        st.session_state.voiceovers = {}  # Reset previous voiceovers
-
-# Generation pipeline
-if st.session_state.generating:
-    with st.spinner(f"🚀 Creating your {st.session_state.workshop_topic} workshop..."):
-        start_time = time.time()
-        
-        # Agent pipeline
-        outline = topic_agent.generate_outline(st.session_state.workshop_topic, duration, difficulty)
-        content = content_agent.generate_content(outline)
-        slides = slide_agent.generate_slides(content)
-        code_labs = code_agent.generate_code(content) if include_code else None
-        design_url = design_agent.generate_design(slides) if include_design else None
-        
-        # Generate voiceovers if enabled
-        voiceovers = {}
-        if include_voiceover and ELEVENLABS_API_KEY:
-            for i, module in enumerate(content.get("modules", [])):
-                # Create a short intro for each module
-                intro_text = f"Welcome to Module {i+1}: {module['title']}. " + \
-                             f"In this module, we'll cover: {', '.join(module.get('speaker_notes', []))[:300]}"
-                
-                # Generate voiceover
-                audio_data = voiceover_agent.generate_voiceover(
-                    intro_text,
-                    st.session_state.selected_voice
-                )
-                
-                if audio_data:
-                    voiceovers[f"module_{i+1}_intro.mp3"] = audio_data
-        
-        # Prepare download package
-        zip_buffer = io.BytesIO()
-        with zipfile.ZipFile(zip_buffer, "a") as zip_file:
-            zip_file.writestr("outline.json", json.dumps(outline, indent=2))
-            zip_file.writestr("content.json", json.dumps(content, indent=2))
-            zip_file.writestr("slides.md", slides)
-            if code_labs:
-                zip_file.writestr("code_labs.ipynb", code_labs)
-            if design_url:
-                try:
-                    img_data = requests.get(design_url).content
-                    zip_file.writestr("slide_design.png", img_data)
-                except:
-                    pass
-            # Add voiceovers to ZIP
-            for filename, audio_data in voiceovers.items():
-                zip_file.writestr(f"voiceovers/{filename}", audio_data)
-        
-        # Store results
-        st.session_state.outline = outline
-        st.session_state.content = content
-        st.session_state.slides = slides
-        st.session_state.code_labs = code_labs
-        st.session_state.design_url = design_url
-        st.session_state.voiceovers = voiceovers
-        st.session_state.zip_buffer = zip_buffer
-        st.session_state.gen_time = round(time.time() - start_time, 2)
-        st.session_state.generated = True
-        st.session_state.generating = False
-
-# Results display
-if st.session_state.generated:
-    st.success(f"✅ {st.session_state.workshop_topic} workshop generated in {st.session_state.gen_time} seconds!")
-    
-    # Download button
-    st.download_button(
-        label="📥 Download Workshop Package",
-        data=st.session_state.zip_buffer.getvalue(),
-        file_name=f"{st.session_state.workshop_topic.replace(' ', '_')}_workshop.zip",
-        mime="application/zip",
-        use_container_width=True
-    )
-    
-    # Preview sections
-    with st.expander("📝 Workshop Outline", expanded=True):
-        st.json(st.session_state.outline)
-    
-    with st.expander("📄 Content Script"):
-        st.write(st.session_state.content)
-    
-    with st.expander("🖥️ Slide Deck Preview"):
-        st.markdown("```markdown\n" + textwrap.dedent(st.session_state.slides[:2000]) + "\n```")
-    
-    if st.session_state.code_labs:
-        with st.expander("💻 Code Labs"):
-            st.code(st.session_state.code_labs)
-    
-    if st.session_state.design_url:
-        with st.expander("🎨 Generated Design"):
-            st.image(st.session_state.design_url, caption="Custom Slide Design")
-    
-    # Voiceover player
-    if st.session_state.voiceovers:
-        with st.expander("🔊 Voiceover Previews"):
-            for i, (filename, audio_bytes) in enumerate(st.session_state.voiceovers.items()):
-                module_num = filename.split("_")[1]
-                st.subheader(f"Module {module_num} Introduction")
-                
-                # Create an audio player for each voiceover
-                st.audio(audio_bytes, format="audio/mp3")
-                
-                # Add download button for individual voiceover
-                st.download_button(
-                    label=f"Download Module {module_num} Voiceover",
-                    data=audio_bytes,
-                    file_name=filename,
-                    mime="audio/mpeg",
-                    key=f"voiceover_dl_{i}"
-                )
-    elif include_voiceover and ELEVENLABS_API_KEY:
-        st.warning("Voiceovers not generated. Check your ElevenLabs API key.")
-
-# Sales and booking section
-st.divider()
-st.subheader("🚀 Ready to Deliver This Workshop?")
-st.markdown(f"""
-### Premium {st.session_state.workshop_topic} Training Package
-- **Live Workshop Delivery**: $10,000 per session
-- **On-Demand Course**: $5,000 (unlimited access)
-- **Pilot Program**: $1,000 refundable deposit
-- **Voiceover Add-on**: $500 per module
-
-✨ **All inclusive**: Customization, materials, and follow-up support
-""")
-
-col1, col2 = st.columns(2)
-with col1:
-    st.link_button("📅 Book a Live Workshop", "https://calendly.com/your-link", 
-                  use_container_width=True)
-with col2:
-    st.link_button("💳 Purchase On-Demand Course", "https://your-store.com", 
-                  use_container_width=True)
-
-# Debug info
-with st.sidebar:
-    st.divider()
-    if openai_client:
-        st.success("OpenAI API Connected")
-    else:
-        st.warning("OpenAI API not set - using enhanced mock data")
-    
-    if ELEVENLABS_API_KEY:
-        st.success("ElevenLabs API Key Found")
-    elif include_voiceover:
-        st.warning("ElevenLabs API key not set")
-    
-    st.info(f"""
-    **Current Workshop:**
-    {st.session_state.workshop_topic}
-    
-    **Premium Features:**
-    - AI-generated voiceovers
-    - Professional slide designs
-    - Real-world case studies
-    - Practical code labs
-    """)
-
-# How it works section
-st.divider()
-st.subheader("💡 How It Works")
-st.markdown("""
-1. **Configure** your workshop topic and parameters
-2. **Generate** premium training materials with voiceovers
-3. **Customize** the content to your specific needs
-4. **Deliver** high-value corporate training at $10K/session
-5. **Reuse** the materials for unlimited revenue
-
-*"The voiceover feature helped me create on-demand courses that sold for $5K each"* - Michael L., AI Consultant
-""")
+        padding