439 lines
No EOL
20 KiB
Python
439 lines
No EOL
20 KiB
Python
#!/usr/bin/env python3
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"""
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Complete Plant Prediction Pipeline with Open-Meteo Official Client
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Open source weather + AI image transformation
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"""
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import openmeteo_requests
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import pandas as pd
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import requests_cache
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from retry_requests import retry
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from datetime import datetime, timedelta
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from PIL import Image
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import torch
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from diffusers import StableDiffusionInstructPix2PixPipeline
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import numpy as np
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class PlantPredictor:
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def __init__(self):
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"""Initialize the plant prediction pipeline with Open-Meteo client"""
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# Setup the Open-Meteo API client with cache and retry on error
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cache_session = requests_cache.CachedSession('.cache', expire_after=3600)
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retry_session = retry(cache_session, retries=5, backoff_factor=0.2)
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self.openmeteo = openmeteo_requests.Client(session=retry_session)
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self.image_model = None
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self.device = self._get_device()
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def _get_device(self):
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"""Determine the best available device, preferring RTX 3060"""
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if torch.cuda.is_available():
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# Check all available GPUs
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num_gpus = torch.cuda.device_count()
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print(f"🔍 Found {num_gpus} GPU(s) available:")
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# List all GPUs and find RTX 3060
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rtx_3060_device = None
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for i in range(num_gpus):
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gpu_name = torch.cuda.get_device_name(i)
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gpu_memory = torch.cuda.get_device_properties(i).total_memory / 1024**3
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print(f" GPU {i}: {gpu_name} ({gpu_memory:.1f} GB)")
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# Look for RTX 3060 specifically
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if "3060" in gpu_name or "RTX 3060" in gpu_name:
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rtx_3060_device = i
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print(f" ✅ Found RTX 3060 at device {i}!")
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# Set the device
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if rtx_3060_device is not None:
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device_id = rtx_3060_device
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torch.cuda.set_device(device_id)
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print(f"🎯 Using RTX 3060 (GPU {device_id})")
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else:
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# Fall back to the most powerful GPU (usually the one with most memory)
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device_id = 0
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max_memory = 0
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for i in range(num_gpus):
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memory = torch.cuda.get_device_properties(i).total_memory
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if memory > max_memory:
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max_memory = memory
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device_id = i
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torch.cuda.set_device(device_id)
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print(f"🔄 RTX 3060 not found, using GPU {device_id} with most memory")
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device = f"cuda:{device_id}"
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# Display selected GPU info
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selected_gpu = torch.cuda.get_device_name(device_id)
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selected_memory = torch.cuda.get_device_properties(device_id).total_memory / 1024**3
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print(f"🚀 Selected GPU: {selected_gpu}")
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print(f"💾 GPU Memory: {selected_memory:.1f} GB")
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# Clear any existing GPU cache
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torch.cuda.empty_cache()
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# Set memory allocation strategy for better performance
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torch.cuda.set_per_process_memory_fraction(0.85, device_id) # Use 85% of GPU memory
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print(f"🔧 Set memory fraction to 85% for optimal performance")
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else:
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device = "cpu"
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print("⚠️ No GPU available, using CPU (will be slower)")
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return device
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def load_image_model(self):
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"""Load the image transformation model with RTX 3060 optimization"""
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print("🔄 Loading Stable Diffusion model...")
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print(f"📍 Device: {self.device}")
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try:
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# Load model with appropriate precision based on device
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if "cuda" in self.device:
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print("🚀 Loading model with RTX 3060 GPU acceleration...")
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# For RTX 3060 (8GB VRAM), use optimized settings
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self.image_model = StableDiffusionInstructPix2PixPipeline.from_pretrained(
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"timbrooks/instruct-pix2pix",
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torch_dtype=torch.float16, # Use half precision for RTX 3060
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use_safetensors=True,
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safety_checker=None,
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requires_safety_checker=False,
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variant="fp16" # Specifically request FP16 variant
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)
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# Move model to the specific GPU
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self.image_model = self.image_model.to(self.device)
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# RTX 3060 specific optimizations
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try:
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self.image_model.enable_xformers_memory_efficient_attention()
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print("✅ XFormers memory efficient attention enabled for RTX 3060")
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except Exception as e:
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print(f"⚠️ XFormers not available: {e}")
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print("💡 Consider installing xformers for better RTX 3060 performance")
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# Enable model CPU offload for RTX 3060's 8GB VRAM
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self.image_model.enable_model_cpu_offload()
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print("✅ Model CPU offload enabled (important for RTX 3060's 8GB VRAM)")
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# Enable VAE slicing for lower memory usage
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self.image_model.enable_vae_slicing()
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print("✅ VAE slicing enabled for memory efficiency")
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# Enable attention slicing for RTX 3060
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self.image_model.enable_attention_slicing(1)
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print("✅ Attention slicing enabled for RTX 3060")
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else:
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print("🐌 Loading model for CPU inference...")
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self.image_model = StableDiffusionInstructPix2PixPipeline.from_pretrained(
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"timbrooks/instruct-pix2pix",
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torch_dtype=torch.float32, # Use full precision for CPU
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use_safetensors=True,
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safety_checker=None,
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requires_safety_checker=False
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)
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self.image_model = self.image_model.to(self.device)
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print("✅ Model loaded successfully on RTX 3060!")
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# Display memory usage
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if "cuda" in self.device:
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device_id = int(self.device.split(':')[-1]) if ':' in self.device else 0
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allocated = torch.cuda.memory_allocated(device_id) / 1024**3
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cached = torch.cuda.memory_reserved(device_id) / 1024**3
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print(f"📊 GPU Memory - Allocated: {allocated:.2f} GB, Cached: {cached:.2f} GB")
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except Exception as e:
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print(f"❌ Error loading model: {e}")
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print("💡 This might be due to insufficient GPU memory or missing dependencies")
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print("💡 RTX 3060 has 8GB VRAM - try reducing image size if needed")
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raise e
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def get_weather_forecast(self, lat, lon, days=7):
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"""Get weather forecast from Open-Meteo API using official client"""
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start_date = datetime.now().strftime("%Y-%m-%d")
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end_date = (datetime.now() + timedelta(days=days)).strftime("%Y-%m-%d")
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url = "https://api.open-meteo.com/v1/forecast"
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params = {
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"latitude": lat,
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"longitude": lon,
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"daily": [
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"temperature_2m_max",
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"temperature_2m_min",
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"precipitation_sum",
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"rain_sum",
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"uv_index_max",
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"sunshine_duration"
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],
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"start_date": start_date,
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"end_date": end_date,
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"timezone": "auto"
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}
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try:
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responses = self.openmeteo.weather_api(url, params=params)
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response = responses[0] # Process first location
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print(f"Coordinates: {response.Latitude()}°N {response.Longitude()}°E")
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print(f"Elevation: {response.Elevation()} m asl")
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print(f"Timezone: UTC{response.UtcOffsetSeconds()//3600:+d}")
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# Process daily data
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daily = response.Daily()
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# Extract data as numpy arrays (much faster!)
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daily_data = {
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"date": pd.date_range(
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start=pd.to_datetime(daily.Time(), unit="s", utc=True),
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end=pd.to_datetime(daily.TimeEnd(), unit="s", utc=True),
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freq=pd.Timedelta(seconds=daily.Interval()),
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inclusive="left"
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),
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"temperature_2m_max": daily.Variables(0).ValuesAsNumpy(),
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"temperature_2m_min": daily.Variables(1).ValuesAsNumpy(),
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"precipitation_sum": daily.Variables(2).ValuesAsNumpy(),
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"rain_sum": daily.Variables(3).ValuesAsNumpy(),
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"uv_index_max": daily.Variables(4).ValuesAsNumpy(),
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"sunshine_duration": daily.Variables(5).ValuesAsNumpy()
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}
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# Create DataFrame for easy analysis
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daily_dataframe = pd.DataFrame(data=daily_data)
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return daily_dataframe, response
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except Exception as e:
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print(f"Error fetching weather data: {e}")
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return None, None
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def analyze_weather_for_plants(self, weather_df):
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"""Analyze weather data and create plant-specific metrics"""
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if weather_df is None or weather_df.empty:
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return None
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# Handle NaN values by filling with 0 or mean
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weather_df = weather_df.fillna(0)
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# Calculate plant-relevant metrics using pandas (more efficient)
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plant_conditions = {
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"avg_temp_max": round(weather_df['temperature_2m_max'].mean(), 1),
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"avg_temp_min": round(weather_df['temperature_2m_min'].mean(), 1),
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"total_precipitation": round(weather_df['precipitation_sum'].sum(), 1),
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"total_rain": round(weather_df['rain_sum'].sum(), 1),
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"total_sunshine_hours": round(weather_df['sunshine_duration'].sum() / 3600, 1), # Convert to hours
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"max_uv_index": round(weather_df['uv_index_max'].max(), 1),
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"days_analyzed": len(weather_df),
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"temp_range": round(weather_df['temperature_2m_max'].max() - weather_df['temperature_2m_min'].min(), 1)
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}
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return plant_conditions
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def create_transformation_prompt(self, plant_conditions):
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"""Create a detailed prompt for image transformation based on weather"""
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if not plant_conditions:
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return "Show this plant after one week of growth"
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# Analyze conditions and create descriptive prompt
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temp_avg = (plant_conditions['avg_temp_max'] + plant_conditions['avg_temp_min']) / 2
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# Temperature effects
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if temp_avg > 25:
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temp_effect = "warm weather promoting vigorous growth with larger, darker green leaves"
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elif temp_avg < 10:
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temp_effect = "cool weather slowing growth with smaller, pale leaves"
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else:
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temp_effect = "moderate temperature supporting steady growth with healthy green foliage"
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# Water effects
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if plant_conditions['total_rain'] > 20:
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water_effect = "abundant rainfall keeping leaves lush, turgid and deep green"
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elif plant_conditions['total_rain'] < 5:
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water_effect = "dry conditions causing slight leaf wilting and browning at edges"
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else:
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water_effect = "adequate moisture maintaining crisp, healthy leaf appearance"
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# Sunlight effects
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if plant_conditions['total_sunshine_hours'] > 50:
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sun_effect = "plenty of sunlight encouraging dense, compact foliage growth"
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elif plant_conditions['total_sunshine_hours'] < 20:
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sun_effect = "limited sunlight causing elongated stems and sparse leaf growth"
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else:
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sun_effect = "moderate sunlight supporting balanced, proportional growth"
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# UV effects
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if plant_conditions['max_uv_index'] > 7:
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uv_effect = "high UV causing slight leaf thickening and waxy appearance"
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else:
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uv_effect = "moderate UV maintaining normal leaf texture"
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prompt = f"""Transform this plant showing realistic growth after {plant_conditions['days_analyzed']} days with {temp_effect}, {water_effect}, {sun_effect}, and {uv_effect}. Show natural changes in leaf size, color saturation, stem thickness, and overall plant structure. Weather summary: {plant_conditions['avg_temp_min']}-{plant_conditions['avg_temp_max']}°C, {plant_conditions['total_rain']}mm rain, {plant_conditions['total_sunshine_hours']}h sun"""
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return prompt
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def transform_plant_image(self, image_path, prompt, num_inference_steps=20):
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"""Transform plant image based on weather conditions with GPU acceleration"""
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if self.image_model is None:
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self.load_image_model()
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try:
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# Load and prepare image
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print(f"📸 Loading image: {image_path}")
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image = Image.open(image_path).convert("RGB")
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# Resize if too large (for memory efficiency)
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original_size = image.size
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if max(image.size) > 1024:
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image.thumbnail((1024, 1024), Image.Resampling.LANCZOS)
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print(f"📏 Resized image from {original_size} to {image.size}")
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# Clear GPU cache before generation
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if "cuda" in self.device:
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torch.cuda.empty_cache()
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device_id = int(self.device.split(':')[-1]) if ':' in self.device else 0
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available_memory = torch.cuda.get_device_properties(device_id).total_memory - torch.cuda.memory_reserved(device_id)
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print(f"🧹 GPU memory cleared. Available: {available_memory / 1024**3:.2f} GB")
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# Transform image with optimized settings for RTX 3060
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print(f"🎨 Transforming image with prompt: {prompt[:100]}...")
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# Set generator for reproducible results
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device_for_generator = self.device if "cuda" in self.device else "cpu"
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generator = torch.Generator(device=device_for_generator).manual_seed(42)
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if "cuda" in self.device:
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# Use autocast for mixed precision on RTX 3060
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with torch.autocast(device_type="cuda", dtype=torch.float16):
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result = self.image_model(
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prompt,
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image=image,
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num_inference_steps=num_inference_steps,
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image_guidance_scale=1.5,
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guidance_scale=7.5,
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generator=generator
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).images[0]
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else:
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# CPU inference without autocast
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result = self.image_model(
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prompt,
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image=image,
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num_inference_steps=num_inference_steps,
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image_guidance_scale=1.5,
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guidance_scale=7.5,
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generator=generator
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).images[0]
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# Clean up GPU memory after generation
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if "cuda" in self.device:
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torch.cuda.empty_cache()
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print("🧹 RTX 3060 memory cleaned up after generation")
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print("✅ Image transformation completed!")
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return result
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except torch.cuda.OutOfMemoryError:
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print("❌ RTX 3060 out of memory!")
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print("💡 Try reducing image size or using fewer inference steps")
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print("💡 RTX 3060 has 8GB VRAM - large images may exceed this limit")
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if "cuda" in self.device:
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torch.cuda.empty_cache()
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return None
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except Exception as e:
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print(f"❌ Error transforming image: {e}")
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if "cuda" in self.device:
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torch.cuda.empty_cache()
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return None
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def predict_plant_growth(self, image_path, lat, lon, output_path="predicted_plant.jpg", days=7):
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"""Complete pipeline: weather + image transformation with RTX 3060 acceleration"""
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print(f"🌱 Starting plant prediction for coordinates: {lat}, {lon}")
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print(f"📅 Analyzing {days} days of weather data...")
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print(f"🖥️ Using device: {self.device}")
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# Step 1: Get weather data using official Open-Meteo client
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print("🌤️ Fetching weather data with caching and retry...")
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weather_df, response_info = self.get_weather_forecast(lat, lon, days)
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if weather_df is None:
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print("❌ Failed to get weather data")
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return None
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print(f"✅ Weather data retrieved for {len(weather_df)} days")
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print("\n📊 Weather Overview:")
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print(weather_df[['date', 'temperature_2m_max', 'temperature_2m_min', 'precipitation_sum', 'sunshine_duration']].head())
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# Step 2: Analyze weather for plants
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plant_conditions = self.analyze_weather_for_plants(weather_df)
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print(f"\n🔬 Plant-specific weather analysis: {plant_conditions}")
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# Step 3: Create transformation prompt
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prompt = self.create_transformation_prompt(plant_conditions)
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print(f"\n📝 Generated transformation prompt: {prompt}")
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# Step 4: Transform image with RTX 3060 acceleration
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print(f"\n🎨 Transforming plant image using RTX 3060...")
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import time
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start_time = time.time()
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result_image = self.transform_plant_image(image_path, prompt)
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end_time = time.time()
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generation_time = end_time - start_time
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if result_image:
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result_image.save(output_path)
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print(f"✅ Plant growth prediction saved to: {output_path}")
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print(f"⏱️ Generation time with RTX 3060: {generation_time:.2f} seconds")
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# Show RTX 3060 memory usage if available
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if "cuda" in self.device:
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device_id = int(self.device.split(':')[-1]) if ':' in self.device else 0
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memory_used = torch.cuda.max_memory_allocated(device_id) / 1024**3
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total_memory = torch.cuda.get_device_properties(device_id).total_memory / 1024**3
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print(f"📊 RTX 3060 Peak Memory Usage: {memory_used:.2f} GB / {total_memory:.1f} GB ({memory_used/total_memory*100:.1f}%)")
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torch.cuda.reset_peak_memory_stats(device_id)
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return result_image, plant_conditions, weather_df
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else:
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print("❌ Failed to transform image on RTX 3060")
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return None
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# Example usage
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if __name__ == "__main__":
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# Initialize predictor
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predictor = PlantPredictor()
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# Example coordinates (Milan, Italy)
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latitude = 45.4642
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longitude = 9.1900
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# Predict plant growth
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# Replace 'your_plant_image.jpg' with actual image path
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result = predictor.predict_plant_growth(
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image_path=r"./foto/basilico.jpg",
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lat=latitude,
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lon=longitude,
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output_path=r"./predicted_plant_growth.jpg",
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days=7
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)
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if result:
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image, conditions, weather_data = result
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print("\n" + "="*50)
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print("PLANT PREDICTION COMPLETED SUCCESSFULLY!")
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print("="*50)
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print(f"Weather conditions analyzed: {conditions}")
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print(f"Weather data shape: {weather_data.shape}")
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print(f"Temperature range: {conditions['avg_temp_min']}°C to {conditions['avg_temp_max']}°C")
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print(f"Total precipitation: {conditions['total_rain']}mm")
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print(f"Sunshine hours: {conditions['total_sunshine_hours']}h")
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else:
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print("Plant prediction failed.") |