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| import gradio as gr | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| import torch | |
| import re | |
| # Load model and tokenizer | |
| model_name = "microsoft/DialoGPT-small" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| tokenizer.pad_token = tokenizer.eos_token | |
| model = AutoModelForCausalLM.from_pretrained(model_name) | |
| model.config.pad_token_id = tokenizer.eos_token_id | |
| # System prompt for company context | |
| SYSTEM_PROMPT = """You are MediaDecode's AI assistant, a professional IT services and software development company. | |
| Provide concise, technical yet friendly answers about: | |
| - Web/Mobile Development (Java, PHP, .NET, C++) | |
| - Product Design & UI/UX | |
| - Staff Augmentation | |
| - Pricing Plans ($129.99-$189.99/month) | |
| - Contact: +1 (647) 928-8768 | info@mediadecode.com | |
| If unsure, direct users to contact sales/support.""" | |
| # FAQ patterns | |
| FAQ_RESPONSES = { | |
| r"(?i)(contact|phone|email|address)": "📞 **Contact Us:**\n- Canada: +1 (647) 928-8768\n- Pakistan: +92 307 5090706\n✉️ **Email:** info@mediadecode.com\n📍 **Addresses:** [See our website](https://mediadecode.com)", | |
| r"(?i)(service|offer|provide)": "🛠 **Our Services:**\n1. Custom Software Development\n2. Web/Mobile Apps (Java/PHP/.NET)\n3. UI/UX Design\n4. Staff Augmentation\n5. AI/ML Solutions\n\n💡 See all: [Services Page](https://mediadecode.com/services)", | |
| r"(?i)(price|plan|cost)": "💰 **Pricing Plans:**\n- Basic: $129.99/mo\n- Economy: $159.99/mo\n- Premium: $189.99/mo\n\nIncludes consulting, 24/7 monitoring, and security. [Learn more](https://mediadecode.com/pricing)", | |
| r"(?i)(portfolio|projects|showcase)": "🎨 **Portfolio:** We've delivered 100+ projects in healthcare, e-commerce, and more. Explore: [Our Showcase](https://mediadecode.com/showcase)", | |
| r"(?i)(technology|tech stack|tools)": "⚙️ **Technologies:**\n- Web: Java, PHP, .NET, C++\n- Mobile: iOS, Android\n- AI/ML, IoT, Wearables\n\n🔗 Full list: [Tech Stack](https://mediadecode.com/technology)" | |
| } | |
| # Fallback message | |
| DEFAULT_RESPONSE = """🤖 I couldn't find a specific answer. For detailed inquiries: | |
| - Call: +1 (647) 928-8768 | |
| - Email: info@mediadecode.com | |
| - Visit: [Support Page](https://mediadecode.com/contact)""" | |
| def respond(message, chat_history): | |
| # Clean the input message | |
| message = message.strip().lower() | |
| # Check for empty message | |
| if not message: | |
| return "Please enter a valid question or query." | |
| # Check for greeting | |
| if any(greeting in message for greeting in ["hi", "hello", "hey"]): | |
| return "Hello! Welcome to MediaDecode support. How can I help you today?" | |
| # Check for predefined FAQs | |
| for pattern, response in FAQ_RESPONSES.items(): | |
| if re.search(pattern, message): | |
| return response | |
| # Generate AI response | |
| prompt = f"{SYSTEM_PROMPT}\nUser: {message}\nAI:" | |
| inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512) | |
| with torch.no_grad(): | |
| outputs = model.generate( | |
| inputs.input_ids, | |
| max_length=200, | |
| pad_token_id=tokenizer.eos_token_id, | |
| temperature=0.7, | |
| do_sample=True | |
| ) | |
| response = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| response = response.split("AI:")[-1].strip() | |
| # Check for fallback conditions | |
| if not response or len(response) < 5 or "i don't know" in response.lower(): | |
| return DEFAULT_RESPONSE | |
| return response | |
| # Gradio interface | |
| with gr.Blocks(theme=gr.themes.Soft()) as demo: | |
| gr.Markdown(""" | |
| # 🚀 MediaDecode Support Bot | |
| **Ask about:** Services | Pricing | Technologies | Contact | |
| *Powered by AI + Company Knowledge Base* | |
| """) | |
| chatbot = gr.Chatbot(height=400, label="Chat History") | |
| msg = gr.Textbox(label="Your Query", placeholder="e.g., What's your pricing for Java development?") | |
| clear = gr.Button("Clear Chat") | |
| def user(user_message, history): | |
| return "", history + [[user_message, None]] | |
| def bot(history): | |
| user_message = history[-1][0] | |
| bot_response = respond(user_message, history[:-1]) | |
| history[-1][1] = bot_response | |
| return history | |
| msg.submit(user, [msg, chatbot], [msg, chatbot]).then( | |
| bot, chatbot, chatbot | |
| ) | |
| clear.click(lambda: None, None, chatbot, queue=False) | |
| demo.launch() |