"Update space" chatgptv1

Signed-off-by: Emmanuel Frimpong Asante <frimpongasante50@gmail.com>

app.py

@@ -1,13 +1,13 @@
 import os
-import openai  # Import the OpenAI library
+import openai
 import tensorflow as tf
-import torch
 from keras.models import load_model
 import gradio as gr
 import cv2
 import numpy as np
 from huggingface_hub import login
-from datetime import datetime, timedelta
+from pymongo import MongoClient
+from datetime import datetime
 import json
 import requests
 
@@ -17,10 +17,15 @@ if tok:
     login(token=tok, add_to_git_credential=True)
 else:
     print("Warning: Hugging Face token not found in environment variables.")
-# gptused
+
 # Set your OpenAI API key
 openai.api_key = os.getenv("OPENAI_API_KEY")
 
+# MongoDB Setup
+MONGO_URI = os.getenv("MONGO_URI", "mongodb://localhost:27017/")
+client = MongoClient(MONGO_URI)
+db = client.poultry_farm  # Database
+
 # Check GPU availability for TensorFlow
 print("TensorFlow version:", tf.__version__)
 print("Eager execution:", tf.executing_eagerly())
@@ -47,18 +52,9 @@ except Exception as e:
     print(f"Error loading models: {e}")
     raise
 
-# Dictionaries for disease names, results, and recommendations
-name_disease = {0: 'Coccidiosis', 1: 'Healthy', 2: 'New Castle Disease', 3: 'Salmonella'}
-result = {0: 'Critical', 1: 'No issue', 2: 'Critical', 3: 'Critical'}
-recommend = {0: 'Panadol', 1: 'You have no need of Medicine', 2: 'Paracetamol', 3: 'Ponston'}
-
-
 class PoultryFarmBot:
     def __init__(self):
-        self.feed_inventory = 1000  # kg
-        self.medicine_inventory = {"Panadol": 100, "Paracetamol": 50}
-        self.chicken_health = {}
-        self.reports = []
+        self.db = db  # MongoDB database
 
     # Health Monitoring and Disease Diagnosis
     def preprocess_image(self, image):
@@ -87,7 +83,7 @@ class PoultryFarmBot:
             return diagnosis, name, status, recom
         else:  # If the image is not recognized as a chicken disease image
             return (
-                "The uploaded image is not recognized as a chicken feaces or does not appear to be related to any known chicken diseases. "
+                "The uploaded image is not recognized as a chicken or does not appear to be related to any known chicken diseases. "
                 "Please ensure the image is clear and shows a chicken or its symptoms to receive a proper diagnosis."
             ), None, None, None
 
@@ -95,51 +91,68 @@ class PoultryFarmBot:
         if image is not None and image.size > 0:  # Ensure image is valid and has elements
             return self.predict(image)
         elif symptoms:
-            # Analyze the symptoms and provide a diagnosis
-            if "coughing" in symptoms.lower() and "sneezing" in symptoms.lower():
-                name = "Newcastle Disease"
-                status = "Critical"
-                recom = "Isolate the bird and consult a veterinarian immediately."
-                diagnosis = f"Based on symptoms, the chicken might have {name}. The condition is {status}. {recom}"
-            elif "diarrhea" in symptoms.lower():
-                name = "Coccidiosis"
-                status = "Critical"
-                recom = "Administer coccidiostat as directed by a veterinarian."
-                diagnosis = f"Based on symptoms, the chicken might have {name}. The condition is {status}. {recom}"
-            else:
-                name = "Unknown Disease"
-                status = "N/A"
-                recom = "Consult a veterinarian for further diagnosis."
-                diagnosis = f"Symptoms are not conclusive. {recom}"
-
-            return diagnosis, name, status, recom
-
+            # Generate diagnosis using ChatGPT based on the provided symptoms
+            context = f"Symptoms: {symptoms}."
+            response = openai.ChatCompletion.create(
+                model="gpt-3.5-turbo",  # Use GPT-4 if you have access
+                messages=[
+                    {"role": "system",
+                     "content": "You are an advanced poultry farm management system, helping poultry farmers manage their flocks efficiently."},
+                    {"role": "user", "content": context}
+                ],
+                max_tokens=150
+            )
+            diagnosis = response['choices'][0]['message']['content'].strip()
+            return diagnosis, None, None, None
         return "Please provide an image or describe the symptoms.", None, None, None
 
     # Inventory Management
     def track_inventory(self, item, usage):
-        if item in self.medicine_inventory:
-            self.medicine_inventory[item] -= usage
-            if self.medicine_inventory[item] < 10:
+        collection = self.db.inventory
+        inventory_item = collection.find_one({"item": item})
+        if inventory_item:
+            new_quantity = inventory_item["quantity"] - usage
+            collection.update_one({"item": item}, {"$set": {"quantity": new_quantity}})
+            if new_quantity < 10:
                 return f"{item} inventory is low, please reorder."
-            return f"{item} inventory updated. Current inventory: {self.medicine_inventory[item]} units."
-        elif item == "feed":
-            self.feed_inventory -= usage
-            if self.feed_inventory < 100:
-                return "Feed inventory is low, please reorder."
-            return f"Feed inventory updated. Current inventory: {self.feed_inventory} kg."
-        return "Item not recognized in inventory."
+            return f"{item} inventory updated. Current inventory: {new_quantity} units."
+        else:
+            return f"Item {item} not recognized in inventory."
+
+    def add_inventory_item(self, item, quantity):
+        collection = self.db.inventory
+        if collection.find_one({"item": item}):
+            collection.update_one({"item": item}, {"$inc": {"quantity": quantity}})
+        else:
+            collection.insert_one({"item": item, "quantity": quantity})
+        return f"Added {quantity} units of {item} to the inventory."
+
+    # Chicken and Egg Management
+    def add_chicken(self, chicken_id, breed, age):
+        collection = self.db.chickens
+        collection.insert_one({"chicken_id": chicken_id, "breed": breed, "age": age})
+        return f"Chicken {chicken_id} added to the database."
+
+    def update_chicken(self, chicken_id, update_data):
+        collection = self.db.chickens
+        collection.update_one({"chicken_id": chicken_id}, {"$set": update_data})
+        return f"Chicken {chicken_id} updated."
+
+    def add_eggs(self, quantity):
+        collection = self.db.eggs
+        collection.insert_one({"date": datetime.now(), "quantity": quantity})
+        return f"Added {quantity} eggs to the database."
 
     # Reporting and Analytics
     def generate_report(self):
         report = {
             "date": str(datetime.now()),
-            "feed_inventory": self.feed_inventory,
-            "medicine_inventory": self.medicine_inventory,
-            "chickens_monitored": len(self.chicken_health),
-            "health_reports": self.chicken_health
+            "feed_inventory": list(self.db.inventory.find({})),
+            "chickens": list(self.db.chickens.find({})),
+            "eggs_collected": list(self.db.eggs.find({})),
+            "health_reports": list(self.db.health_reports.find({}))
         }
-        self.reports.append(report)
+        self.db.reports.insert_one(report)
         return json.dumps(report, indent=4)
 
     # Integration with External Systems
@@ -164,29 +177,8 @@ class PoultryFarmBot:
             return "Unknown emergency type."
 
 
-# # Example usage of the chatbot with integrated Health Monitoring and Disease Diagnosis
-# bot = PoultryFarmBot()
-#
-# # Health Monitoring and Disease Diagnosis
-# image = None  # Replace with actual image input
-# symptoms = "coughing and sneezing"
-# diagnosis_result = bot.diagnose_disease(image=image, symptoms=symptoms)
-# print(diagnosis_result)
-#
-# # Inventory Management
-# print(bot.track_inventory("feed", 50))
-# print(bot.track_inventory("Panadol", 10))
-#
-# # Reporting and Analytics
-# print(bot.generate_report())
-#
-# # Integration with External Systems
-# data_to_send = {"temperature": 32, "humidity": 35}
-# print(bot.integrate_with_external_system("https://api.external-system.com/data", data_to_send))
-#
-# # Emergency Handling
-# print(bot.handle_emergency("disease_outbreak"))
-
+# Initialize the bot instance
+bot = PoultryFarmBot()
 
 # Function to generate a response using OpenAI's GPT model
 def generate_combined_response(image, text):
@@ -199,7 +191,7 @@ def generate_combined_response(image, text):
 
         # Use OpenAI's GPT model to generate additional advice
         response = openai.ChatCompletion.create(
-            model="gpt-4o-mini",  # Use GPT-4 or gpt-3.5-turbo based on your API access
+            model="gpt-3.5-turbo",  # Use GPT-4 or gpt-3.5-turbo based on your API access
             messages=[
                 {"role": "system", "content": "You are an expert poultry farm management assistant."},
                 {"role": "user", "content": context}

requirements.txt

@@ -7,3 +7,4 @@ numpy~=1.23.5
 torchvision
 accelerate
 openai==0.28
+pymongo