Update app.py

app.py

@@ -3,13 +3,14 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
 import re
 
-# Load model (small footprint for demo)
+# 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  # Ensure no pad token issues
 
-# System prompt for IT services context
+# 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++)
@@ -19,7 +20,7 @@ Provide concise, technical yet friendly answers about:
 - Contact: +1 (647) 928-8768 | info@mediadecode.com
 If unsure, direct users to contact sales/support."""
 
-# Predefined FAQ responses (regex patterns)
+# 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)",
@@ -28,61 +29,64 @@ FAQ_RESPONSES = {
     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 for unmatched queries
+# 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)"""
 
+# Response generator
 def respond(message, chat_history):
-    # Check FAQ matches first
+    # Check for predefined FAQs
     for pattern, response in FAQ_RESPONSES.items():
         if re.search(pattern, message):
-            return f"{response}\n\nHow else can I help?" 
-    
-    # AI-generated response for complex queries
+            return f"{response}\n\nHow else can I help?"
+
+    # Generate AI response
     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
-    )
+    with torch.no_grad():
+        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)
-    
-    # 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
-    
+
+    # 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
+# 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
+        if not history or not history[-1][0]:
+            return history
+        bot_reply = respond(history[-1][0], history[:-1])
+        history[-1][1] = bot_reply
         return history
-    
+
     msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
         bot, chatbot, chatbot
     )
-    clear.click(lambda: None, None, chatbot, queue=False)
 
-demo.launch()
+    clear.click(lambda: [], None, chatbot, queue=False)
+
+demo.launch()