update space

app.py

@@ -1,4 +1,4 @@
-# File 1: main.py
+# File: main.py
 
 # Import necessary libraries
 import os
@@ -12,7 +12,6 @@ from utils import PoultryFarmBot, llama3_response
 from chatbot_interface import build_chatbot_interface  # Importing chatbot interface function
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-
 # Setup logging for better monitoring
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 logger = logging.getLogger(__name__)

chatbot_interface.py

@@ -1,7 +1,8 @@
-# File 2: chatbot_interface.py
+# File: chatbot_interface.py
 
 import gradio as gr
 import logging
+from utils import chatbot_response  # Import chatbot response function from utils
 
 logger = logging.getLogger(__name__)
 

utils.py

@@ -72,6 +72,44 @@ def llama3_response(user_input, tokenizer, model):
         logger.error(f"Error generating response: {str(e)}")
         return f"Error generating response: {str(e)}"
 
+def chatbot_response(image, text, username, password):
+    """
+    Handle user input and generate appropriate responses.
+
+    Args:
+        image (numpy.ndarray): Image input for disease detection.
+        text (str): Text input for general queries.
+        username (str): Username for authentication.
+        password (str): Password for authentication.
+
+    Returns:
+        str: Response generated by the chatbot.
+    """
+    logger.info("Handling user input for chatbot response.")
+    user = bot.authenticate_user(username, password)
+    if not user:
+        return "Authentication failed. Please check your username and password."
+
+    user_id = user['_id']
+    # If an image is provided, diagnose the disease
+    if image is not None:
+        logger.info("Image input detected. Proceeding with disease diagnosis.")
+        diagnosis, name, status, recom = bot.diagnose_disease(image)
+        if name and status and recom:
+            logger.info("Diagnosis complete.")
+            bot.log_enquiry("image", "Image Enquiry", diagnosis, user_id)
+            return diagnosis
+        else:
+            logger.warning("Diagnosis incomplete.")
+            bot.log_enquiry("image", "Image Enquiry", diagnosis, user_id)
+            return diagnosis
+    else:
+        # Generate a response using Llama 3.2 for general text input
+        logger.info("Text input detected. Generating response.")
+        response = llama3_response(text, tokenizer, model)
+        bot.log_enquiry("text", text, response, user_id)
+        return response
+
 class PoultryFarmBot:
     def __init__(self, db):
         """