updated code working
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3 changed files with 50 additions and 32 deletions
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test2/predicted_plant_growth.jpg
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test2/predicted_plant_growth.jpg
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@ -1,9 +1,3 @@
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#!/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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@ -22,21 +16,24 @@ class PlantPredictor:
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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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def get_current_location(self):
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"""Get current location using IP geolocation"""
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try:
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g = geocoder.ip('me')
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if g.ok:
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print(f" Location detected: {g.city}, {g.country}")
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print(f" Coordinates: {g.latlng[0]:.4f}, {g.latlng[1]:.4f}")
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print(f"📍 Location detected: {g.city}, {g.country}")
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print(f"📍 Coordinates: {g.latlng[0]:.4f}, {g.latlng[1]:.4f}")
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return g.latlng[0], g.latlng[1] # lat, lon
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else:
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print(" Could not detect location, using default (Milan)")
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print("⚠️ Could not detect location, using default (Milan)")
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self.image_model = None
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except Exception as e:
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print(f" Location detection failed: {e}, using default (Milan)")
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return 45.4642, 9.1900
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print(f"⚠️ Location detection failed: {e}, using default (Milan)")
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self.image_model = None
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def load_image_model(self):
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"""Load the image transformation model"""
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@ -135,9 +132,12 @@ class PlantPredictor:
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"""Create a detailed prompt for image transformation based on weather AND image analysis"""
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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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return "Show this plant after one week of growth", "generic plant", "unknown health"
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# STEP 3A: Analyze original image
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plant_type = "generic plant"
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plant_health = "unknown health"
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# STEP 3A: Analyze original image
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try:
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image = Image.open(image_path).convert("RGB")
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# Basic image analysis
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@ -158,7 +158,7 @@ class PlantPredictor:
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plant_type = "generic plant"
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plant_health = "healthy"
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# STEP 3B: Weather analysis with plant-specific logic
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# STEP 3B: Weather analysis with plant-specific logic
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temp_avg = (plant_conditions['avg_temp_max'] + plant_conditions['avg_temp_min']) / 2
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# Temperature effects (adjusted by plant type)
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@ -201,6 +201,7 @@ class PlantPredictor:
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# STEP 3C: Create comprehensive prompt combining image + weather analysis
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prompt = f"""Transform this {plant_type} showing realistic growth after {plant_conditions['days_analyzed']} days. Current state: {plant_health}. Apply these weather effects: {temp_effect}, {water_effect}, {sun_effect}, and {uv_effect}. Show natural changes in leaf size, color saturation, stem thickness, and overall plant structure while maintaining the original composition and lighting. 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, plant_type, plant_health
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def detect_plant_type(self, image):
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"""Simple plant type detection based on image characteristics"""
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@ -234,7 +235,7 @@ class PlantPredictor:
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elif brightness > 100 and green_channel > 80:
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return "moderately healthy"
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else:
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return "showing some stress", plant_type, plant_health
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return "showing some stress"
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def transform_plant_image(self, image_path, prompt):
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"""STEP 4: Generate new image based on analyzed prompt"""
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@ -273,11 +274,11 @@ class PlantPredictor:
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# Auto-detect location if not provided
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if lat is None or lon is None:
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print("Auto-detecting location...")
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print(" Auto-detecting location...")
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lat, lon = self.get_current_location()
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print(f"Starting plant prediction for coordinates: {lat:.4f}, {lon:.4f}")
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print(f"Analyzing {days} days of weather data...")
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print(f" Starting plant prediction for coordinates: {lat:.4f}, {lon:.4f}")
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print(f" Analyzing {days} days of weather data...")
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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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@ -296,15 +297,23 @@ class PlantPredictor:
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print(f"\nPlant-specific weather analysis: {plant_conditions}")
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# Step 3: Analyze image + weather to create intelligent prompt
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print("\nSTEP 3: Analyzing image and creating transformation prompt...")
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prompt, plant_type, plant_health = self.create_transformation_prompt(image_path, plant_conditions)
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print(f"Plant identified as: {plant_type}")
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print(f"Current health: {plant_health}")
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print(f"Generated transformation prompt: {prompt}")
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print("\n STEP 3: Analyzing image and creating transformation prompt...")
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try:
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prompt, plant_type, plant_health = self.create_transformation_prompt(image_path, plant_conditions)
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print(f" Plant identified as: {plant_type}")
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print(f" Current health: {plant_health}")
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print(f" Generated transformation prompt: {prompt}")
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except Exception as e:
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print(f" Error in Step 3: {e}")
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return None
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# Step 4: Generate transformed image
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print("\nSTEP 4: Generating prediction image...")
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result_image = self.transform_plant_image(image_path, prompt)
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try:
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result_image = self.transform_plant_image(image_path, prompt)
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except Exception as e:
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print(f" Error in Step 4: {e}")
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return None
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if result_image:
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result_image.save(output_path)
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@ -319,22 +328,31 @@ 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="./foto/basilico.jpg",
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output_path="predicted_plant_growth.jpg",
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lat=latitude,
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lon=longitude,
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output_path="./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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image, conditions, weather_data, plant_type, plant_health = result
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print("\n" + "="*50)
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print("PLANT PREDICTION COMPLETED SUCCESSFULLY!")
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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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print(f" Plant type: {plant_type}")
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print(f" Plant health: {plant_health}")
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print(f" Weather conditions: {conditions}")
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print(f" Data points: {weather_data.shape}")
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print(f" Temperature: {conditions['avg_temp_min']}°C to {conditions['avg_temp_max']}°C")
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print(f" Total rain: {conditions['total_rain']}mm")
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print(f" Sunshine: {conditions['total_sunshine_hours']}h")
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else:
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print("Plant prediction failed.")
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