Update app.py from anycoder

app.py

@@ -2,32 +2,54 @@ import gradio as gr
 import random
 import time
 from datetime import datetime
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
 
-# Simple AI Chatbot Model
-class SimpleChatbot:
+# Load a small Hugging Face text generation model
+class HuggingFaceChatbot:
     def __init__(self):
-        self.responses = {
-            "greetings": ["Hello!", "Hi there!", "Greetings!", "Nice to meet you!"],
-            "farewells": ["Goodbye!", "See you later!", "Take care!", "Farewell!"],
-            "thanks": ["You're welcome!", "No problem!", "Happy to help!", "Anytime!"],
-            "questions": ["That's an interesting question!", "Let me think about that...", "I'll get back to you on that!"],
-            "default": ["I see.", "Interesting!", "Tell me more.", "Go on...", "I'm listening."]
-        }
+        # Using a small model for demonstration
+        self.model_name = "microsoft/DialoGPT-small"
+        self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
+        self.model = AutoModelForCausalLM.from_pretrained(self.model_name)
+
+        # Move model to GPU if available
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.model.to(self.device)
+
+        # Chat history
+        self.chat_history_ids = None
 
     def respond(self, message, history):
-        message = message.lower()
-
-        # Simple intent detection
-        if any(word in message for word in ["hi", "hello", "hey", "greetings"]):
-            response = random.choice(self.responses["greetings"])
-        elif any(word in message for word in ["bye", "goodbye", "farewell", "see you"]):
-            response = random.choice(self.responses["farewells"])
-        elif any(word in message for word in ["thank", "thanks", "appreciate"]):
-            response = random.choice(self.responses["thanks"])
-        elif "?" in message:
-            response = random.choice(self.responses["questions"])
-        else:
-            response = random.choice(self.responses["default"])
+        # Encode the new user input and add the eos_token
+        new_user_input_ids = self.tokenizer.encode(
+            message + self.tokenizer.eos_token,
+            return_tensors='pt'
+        ).to(self.device)
+
+        # Append the new user input tokens to the chat history
+        bot_input_ids = torch.cat([
+            self.chat_history_ids,
+            new_user_input_ids
+        ], dim=-1) if self.chat_history_ids is not None else new_user_input_ids
+
+        # Generate a response
+        self.chat_history_ids = self.model.generate(
+            bot_input_ids,
+            max_length=1000,
+            pad_token_id=self.tokenizer.eos_token_id,
+            no_repeat_ngram_size=3,
+            do_sample=True,
+            top_k=50,
+            top_p=0.95,
+            temperature=0.7
+        )
+
+        # Decode the response
+        response = self.tokenizer.decode(
+            self.chat_history_ids[:, bot_input_ids.shape[-1]:][0],
+            skip_special_tokens=True
+        )
 
         # Add timestamp to response
         timestamp = datetime.now().strftime("%H:%M:%S")
@@ -36,7 +58,7 @@ class SimpleChatbot:
         return full_response
 
 # Create chatbot instance
-chatbot = SimpleChatbot()
+chatbot = HuggingFaceChatbot()
 
 # Custom theme for modern look
 custom_theme = gr.themes.Soft(
@@ -55,8 +77,10 @@ custom_theme = gr.themes.Soft(
 
 # Create Gradio interface
 with gr.Blocks() as demo:
-    gr.Markdown("# 🤖 AI Chatbot")
-    gr.Markdown("Built with anycoder - A simple AI chatbot for conversation")
+    gr.Markdown("# 🤖 AI Chatbot with Hugging Face")
+    gr.Markdown("""
+    **Built with anycoder** - A conversational AI chatbot using Hugging Face's DialoGPT-small model
+    """)
 
     with gr.Row():
         with gr.Column(scale=3):
@@ -79,20 +103,29 @@ with gr.Blocks() as demo:
         with gr.Column(scale=1):
             gr.Markdown("## Features")
             gr.Markdown("""
-            - ✅ Simple AI responses
-            - ✅ Conversation history
+            - ✅ Powered by Hugging Face DialoGPT-small
+            - ✅ Context-aware conversation
             - ✅ Timestamped messages
-            - ✅ User-friendly interface
+            - ✅ Modern, user-friendly interface
+            - ✅ GPU acceleration (if available)
             """)
 
             gr.Markdown("## How to Use")
             gr.Markdown("""
             1. Type your message in the input box
             2. Press Enter or click Send
-            3. The AI will respond automatically
+            3. The AI will respond using the Hugging Face model
             4. Continue the conversation naturally
             """)
 
+            gr.Markdown("## Model Info")
+            gr.Markdown(f"""
+            - **Model**: DialoGPT-small
+            - **Device**: {chatbot.device}
+            - **Max Response Length**: 1000 tokens
+            - **Temperature**: 0.7
+            """)
+
             clear_btn = gr.Button("🔄 Clear Chat", variant="secondary")
 
     # Chatbot logic
@@ -129,8 +162,31 @@ demo.launch(
     theme=custom_theme,
     footer_links=[
         {"label": "Built with anycoder", "url": "https://huggingface.co/spaces/akhaliq/anycoder"},
-        {"label": "Gradio Docs", "url": "https://gradio.app/docs"}
+        {"label": "Gradio Docs", "url": "https://gradio.app/docs"},
+        {"label": "Hugging Face", "url": "https://huggingface.co/"}
     ],
-    title="AI Chatbot",
-    description="A simple AI chatbot for conversation using Gradio"
-)
+    title="Hugging Face AI Chatbot",
+    description="A conversational AI chatbot using Hugging Face's DialoGPT-small model"
+)
+Key changes made:
+
+1. **Replaced the simple chatbot with Hugging Face model**:
+   - Added `transformers` import for AutoTokenizer and AutoModelForCausalLM
+   - Created `HuggingFaceChatbot` class using DialoGPT-small model
+   - Added proper model loading and GPU support
+
+2. **Enhanced the interface**:
+   - Updated title to reflect Hugging Face integration
+   - Added model information section showing device and parameters
+   - Added Hugging Face link to footer
+
+3. **Improved response generation**:
+   - Uses proper tokenization and generation
+   - Maintains conversation context with chat history
+   - Better response quality with temperature and sampling
+
+4. **Added technical details**:
+   - Shows which device (CPU/GPU) is being used
+   - Displays model parameters in the sidebar
+
+The application now uses a real AI model from Hugging Face while maintaining the same user-friendly interface and conversation flow. The DialoGPT-small model is a good choice as it's relatively lightweight but provides much better conversational abilities than the simple rule-based chatbot.