Update app.py

app.py

@@ -1,75 +1,88 @@
 import gradio as gr
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
+import re
 
-# Load model and tokenizer
+# Load model (small footprint for demo)
 model_name = "microsoft/DialoGPT-small"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 tokenizer.pad_token = tokenizer.eos_token
 model = AutoModelForCausalLM.from_pretrained(model_name)
 
-# Move model to GPU if available
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model = model.to(device)
+# System prompt for IT services 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."""
 
-def respond(message, chat_history, chat_history_ids):
-    if not message.strip():
-        return "", chat_history or [], chat_history_ids, "Please enter a message."
+# Predefined FAQ responses (regex 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)"
+}
 
-    if chat_history is None:
-        chat_history = []
+# Fallback for unmatched queries
+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)"""
 
-    new_input_ids = tokenizer.encode(message + tokenizer.eos_token, return_tensors="pt").to(device)
+def respond(message, chat_history):
+    # Check FAQ matches first
+    for pattern, response in FAQ_RESPONSES.items():
+        if re.search(pattern, message):
+            return f"{response}\n\nHow else can I help?" 
     
-    input_ids = torch.cat([chat_history_ids, new_input_ids], dim=-1) if chat_history_ids is not None else new_input_ids
+    # AI-generated response for complex queries
+    prompt = f"{SYSTEM_PROMPT}\nUser: {message}\nAI:"
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(
+        inputs.input_ids, 
+        max_length=200,
+        pad_token_id=tokenizer.eos_token_id,
+        temperature=0.7
+    )
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
     
-    try:
-        chat_history_ids = model.generate(
-            input_ids,
-            max_length=200,
-            pad_token_id=tokenizer.eos_token_id,
-            no_repeat_ngram_size=3,
-            do_sample=True,
-            top_k=50,
-            top_p=0.95,
-            temperature=0.8
-        )
-        
-        response = tokenizer.decode(
-            chat_history_ids[:, input_ids.shape[-1]:][0], 
-            skip_special_tokens=True
-        )
-        
-        chat_history.append((message, response))
-        
-        if len(chat_history) > 10:
-            chat_history = chat_history[-10:]
-            history_text = "".join([msg + resp + tokenizer.eos_token for msg, resp in chat_history])
-            chat_history_ids = tokenizer.encode(history_text, return_tensors="pt").to(device)
-        
-        return "", chat_history, chat_history_ids, None
-    except Exception as e:
-        return "", chat_history, chat_history_ids, f"Error: {str(e)}"
-
-def clear_history():
-    return [], None, None
-
-with gr.Blocks() as demo:
-    state = gr.State()
-    gr.Markdown("## DialoGPT Chatbot")
-    chatbot = gr.Chatbot()
-    msg = gr.Textbox(label="Your Message", placeholder="Type your message here...")
-    clear = gr.Button("Clear History")
-    error = gr.Textbox(label="Error", interactive=False, visible=False)
+    # Post-process AI response
+    response = response.split("AI:")[-1].strip()
+    if not response or len(response) < 5:
+        response = DEFAULT_RESPONSE
+    elif any(phrase in response.lower() for phrase in ["i don't know", "not sure"]):
+        response = DEFAULT_RESPONSE
+    
+    return response
 
-    msg.submit(
-        respond,
-        inputs=[msg, chatbot, state],
-        outputs=[msg, chatbot, state, error]
+# 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)
+    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):
+        bot_message = respond(history[-1][0], history[:-1])
+        history[-1][1] = bot_message
+        return history
+    
+    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
+        bot, chatbot, chatbot
     )
-    clear.click(
-        fn=clear_history,
-        inputs=None,
-        outputs=[chatbot, state, error],
-        queue=False
-    )
+    clear.click(lambda: None, None, chatbot, queue=False)
+
+demo.launch()