requirements updated

app_hf.py

@@ -8,13 +8,14 @@ import tarfile
 from typing import List, Tuple
 import boto3
 import logging
+from pathlib import Path
 
 # Set up logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
 class CustomerSupportBot:
-    def __init__(self, model_path="models/customer_support_gpt"):
+    def __init__(self, model_path=None):
         """
         Initialize the customer support bot with the fine-tuned model.
         
@@ -22,40 +23,60 @@ class CustomerSupportBot:
             model_path (str): Path to the saved model and tokenizer
         """
         self.process = psutil.Process(os.getpid())
-        self.model_path = model_path
-        self.model_file_path = os.path.join(self.model_path, "model.tar.gz")
+
+        if model_path is None:
+            self.model_path = os.path.join(os.path.expanduser("~"), "customer_support_gpt")
+        else:
+            self.model_path = model_path
+
+
+        self.model_path = Path(self.model_path)
+        self.model_file_path = self.model_path / "model.tar.gz"
+
         self.s3 = boto3.client("s3")
         self.model_key = "models/model.tar.gz"
         self.bucket_name = "customer-support-gpt"
         
         # Download and load the model
-        self.download_and_load_model()
+        try:
+            self.download_and_load_model()
+        except Exception as e:
+            logger.error(f"Failed to initialize model: {str(e)}")
+            raise
 
     def download_and_load_model(self):
-        # Check if the model directory exists
-        if not os.path.exists(self.model_path):
-            os.makedirs(self.model_path)
-
-        # Download model.tar.gz from S3 if not already downloaded
-        if not os.path.exists(self.model_file_path):
-            print("Downloading model from S3...")
-            self.s3.download_file(self.bucket_name, self.model_key, self.model_file_path)
-            print("Download complete. Extracting model files...")
-
-            # Extract the model files
-            with tarfile.open(self.model_file_path, "r:gz") as tar:
-                tar.extractall(self.model_path)
-
-        # Load the model and tokenizer from extracted files
-        self.tokenizer = AutoTokenizer.from_pretrained(self.model_path)
-        self.model = AutoModelForCausalLM.from_pretrained(self.model_path)
-        print("Model and tokenizer loaded successfully.")
-
-        # Move model to GPU if available
-        self.device = "cpu" #"cuda" if torch.cuda.is_available() else "cpu"
-        self.model = self.model.to(self.device)
-
-        print(f'Model loaded on device: {self.device}')
+        try:
+            # Create model directory if it doesn't exist
+            self.model_path.mkdir(parents=True, exist_ok=True)
+            logger.info(f"Using model directory: {self.model_path}")
+
+            # Download model from S3 if needed
+            if not self.model_file_path.exists():
+                logger.info("Downloading model from S3...")
+                self.s3.download_file(self.bucket_name, self.model_key, str(self.model_file_path))
+                logger.info("Download complete. Extracting model files...")
+
+                # Extract the model files
+                with tarfile.open(self.model_file_path, "r:gz") as tar:
+                    tar.extractall(str(self.model_path))
+
+            # Load the model and tokenizer
+            logger.info("Loading model and tokenizer...")
+            self.tokenizer = AutoTokenizer.from_pretrained(str(self.model_path))
+            self.model = AutoModelForCausalLM.from_pretrained(str(self.model_path))
+            logger.info("Model and tokenizer loaded successfully.")
+
+            # Move model to GPU if available
+            self.device = "cuda" if torch.cuda.is_available() else "cpu"
+            self.model = self.model.to(self.device)
+            logger.info(f'Model loaded on device: {self.device}')
+
+        except PermissionError as e:
+            logger.error(f"Permission error when accessing {self.model_path}: {str(e)}")
+            raise
+        except Exception as e:
+            logger.error(f"Error in download_and_load_model: {str(e)}")
+            raise
 
     def generate_response(self, message: str, max_length=100, temperature=0.7) -> str:
         try:
@@ -94,107 +115,118 @@ class CustomerSupportBot:
 
 
 def create_chat_interface():
-    bot = CustomerSupportBot(model_path="/models")
-
-    def predict(message: str, history: List[Tuple[str, str]]) -> Tuple[str, List[Tuple[str, str]]]:
-        if not message:
-            return "", history
-        
-        bot_response = bot.generate_response(message)
-        
-        # Log resource usage
-        usage = bot.monitor_resources()
-        print("Resource Usage:", usage)
+    try:
+        # Use a user-accessible directory for the model
+        user_model_path = os.path.join(os.path.expanduser("~"), "customer_support_models")
+        bot = CustomerSupportBot(model_path=user_model_path)
         
-        history.append((message, bot_response))
-        return "", history
+        def predict(message: str, history: List[Tuple[str, str]]) -> Tuple[str, List[Tuple[str, str]]]:
+            if not message:
+                return "", history
+            
+            bot_response = bot.generate_response(message)
+            
+            # Log resource usage
+            usage = bot.monitor_resources()
+            print("Resource Usage:", usage)
+            
+            history.append((message, bot_response))
+            return "", history
 
-    # Create the Gradio interface with custom CSS
-    with gr.Blocks(css="""
-        .message-box {
-            margin-bottom: 10px;
-        }
-        .button-row {
-            display: flex;
-            gap: 10px;
-            margin-top: 10px;
-        }
-    """) as interface:
-        gr.Markdown("# Customer Support Chatbot")
-        gr.Markdown("Welcome! How can I assist you today?")
-        
-        chatbot = gr.Chatbot(
-            label="Chat History",
-            height=500,
-            elem_classes="message-box",
-            # type="messages"
-        )
-        
-        with gr.Row():
-            msg = gr.Textbox(
-                label="Your Message",
-                placeholder="Type your message here...",
-                lines=2,
-                elem_classes="message-box"
-            )
-        
-        with gr.Row(elem_classes="button-row"):
-            submit = gr.Button("Send Message", variant="primary")
-            clear = gr.ClearButton([msg, chatbot], value="Clear Chat")
-
-        # Add example queries in a separate row
-        with gr.Row():
-            gr.Examples(
-                examples=[
-                    "How do I reset my password?",
-                    "What are your shipping policies?",
-                    "I want to return a product.",
-                    "How can I track my order?",
-                    "What payment methods do you accept?"
-                ],
-                inputs=msg,
-                label="Example Questions"
+        # Create the Gradio interface with custom CSS
+        with gr.Blocks(css="""
+            .message-box {
+                margin-bottom: 10px;
+            }
+            .button-row {
+                display: flex;
+                gap: 10px;
+                margin-top: 10px;
+            }
+        """) as interface:
+            gr.Markdown("# Customer Support Chatbot")
+            gr.Markdown("Welcome! How can I assist you today?")
+            
+            chatbot = gr.Chatbot(
+                label="Chat History",
+                height=500,
+                elem_classes="message-box",
+                # type="messages"
             )
+            
+            with gr.Row():
+                msg = gr.Textbox(
+                    label="Your Message",
+                    placeholder="Type your message here...",
+                    lines=2,
+                    elem_classes="message-box"
+                )
+            
+            with gr.Row(elem_classes="button-row"):
+                submit = gr.Button("Send Message", variant="primary")
+                clear = gr.ClearButton([msg, chatbot], value="Clear Chat")
+
+            # Add example queries in a separate row
+            with gr.Row():
+                gr.Examples(
+                    examples=[
+                        "How do I reset my password?",
+                        "What are your shipping policies?",
+                        "I want to return a product.",
+                        "How can I track my order?",
+                        "What payment methods do you accept?"
+                    ],
+                    inputs=msg,
+                    label="Example Questions"
+                )
 
-        # Set up event handlers
-        submit_click = submit.click(
-            predict,
-            inputs=[msg, chatbot],
-            outputs=[msg, chatbot]
-        )
-        
-        msg.submit(
-            predict,
-            inputs=[msg, chatbot],
-            outputs=[msg, chatbot]
-        )
-        
-        # Add keyboard shortcut for submit
-        msg.change(lambda x: gr.update(interactive=bool(x.strip())), inputs=[msg], outputs=[submit])
+            # Set up event handlers
+            submit_click = submit.click(
+                predict,
+                inputs=[msg, chatbot],
+                outputs=[msg, chatbot]
+            )
+            
+            msg.submit(
+                predict,
+                inputs=[msg, chatbot],
+                outputs=[msg, chatbot]
+            )
+            
+            # Add keyboard shortcut for submit
+            msg.change(lambda x: gr.update(interactive=bool(x.strip())), inputs=[msg], outputs=[submit])
 
-        print("Interface created successfully.")
+            print("Interface created successfully.")
 
-        # call the initial query function
-        # run a query first how are you and predict the output
-        print(predict("How are you", []))
+            # call the initial query function
+            # run a query first how are you and predict the output
+            print(predict("How are you", []))
 
-        # run a command which checks the resource usage
-        print(f'Bot Resource Usage : {bot.monitor_resources()}')
+            # run a command which checks the resource usage
+            print(f'Bot Resource Usage : {bot.monitor_resources()}')
 
-        # show full system usage
-        print(f'CPU Percentage : {psutil.cpu_percent()}')
-        print(f'RAM Usage : {psutil.virtual_memory()}')
-        print(f'Swap Memory : {psutil.swap_memory()}')
+            # show full system usage
+            print(f'CPU Percentage : {psutil.cpu_percent()}')
+            print(f'RAM Usage : {psutil.virtual_memory()}')
+            print(f'Swap Memory : {psutil.swap_memory()}')
 
-    return interface
+        return interface
+        
+    except Exception as e:
+        logger.error(f"Failed to create chat interface: {str(e)}")
+        raise
 
 if __name__ == "__main__":
-    demo = create_chat_interface()
-    print("Starting Gradio server...")
-    demo.launch(
-        share=False,
-        server_name="0.0.0.0",
-        server_port=7860,  # Changed to 7860 for Gradio
-        debug=True,
-        inline=False
-    )
+    try:
+        logger.info("Starting customer support bot application...")
+        demo = create_chat_interface()
+        demo.launch(
+            share=False,
+            server_name="0.0.0.0",
+            server_port=7860,
+            debug=True,
+            inline=False
+        )
+    except Exception as e:
+        logger.error(f"Application failed to start: {str(e)}")
+        raise