another test by passing not only a prompt but also the original image

test2/README.md

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+## this app requires:
+- python >= 3.11.0
+
+### How to run:
+    - cd inside the root of the project
+    - install all necessary dependencies from the txt by doing  : "pip install -r requirements.txt"
+    - run by doing python script.py

test2/requirements.txt

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+openmeteo-requests
+pandas
+torch
+diffusers
+transformers
+pillow
+requests-cache
+retry-requests
+numpy
+accelerate
+hf_xet
+geocoder

test2/script.py

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+#!/usr/bin/env python3
+"""
+Complete Plant Prediction Pipeline with Open-Meteo Official Client
+Open source weather + AI image transformation
+"""
+
+import openmeteo_requests
+import pandas as pd
+import requests_cache
+from retry_requests import retry
+from datetime import datetime, timedelta
+from PIL import Image
+import torch
+from diffusers import StableDiffusionInstructPix2PixPipeline
+import numpy as np
+import geocoder
+
+class PlantPredictor:
+    def __init__(self):
+        """Initialize the plant prediction pipeline with Open-Meteo client"""
+        # Setup the Open-Meteo API client with cache and retry on error
+        cache_session = requests_cache.CachedSession('.cache', expire_after=3600)
+        retry_session = retry(cache_session, retries=5, backoff_factor=0.2)
+        self.openmeteo = openmeteo_requests.Client(session=retry_session)
+        
+    def get_current_location(self):
+        """Get current location using IP geolocation"""
+        try:
+            g = geocoder.ip('me')
+            if g.ok:
+                print(f" Location detected: {g.city}, {g.country}")
+                print(f" Coordinates: {g.latlng[0]:.4f}, {g.latlng[1]:.4f}")
+                return g.latlng[0], g.latlng[1]  # lat, lon
+            else:
+                print(" Could not detect location, using default (Milan)")
+            self.image_model = None
+        except Exception as e:
+            print(f" Location detection failed: {e}, using default (Milan)")
+            return 45.4642, 9.1900
+        
+    def load_image_model(self):
+        """Load the image transformation model"""
+        print("Loading Stable Diffusion model...")
+        self.image_model = StableDiffusionInstructPix2PixPipeline.from_pretrained(
+            "timbrooks/instruct-pix2pix",
+            torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
+        )
+        if torch.cuda.is_available():
+            self.image_model = self.image_model.to("cuda")
+        print("Model loaded successfully!")
+        
+    def get_weather_forecast(self, lat, lon, days=7):
+        """Get weather forecast from Open-Meteo API using official client"""
+        
+        start_date = datetime.now().strftime("%Y-%m-%d")
+        end_date = (datetime.now() + timedelta(days=days)).strftime("%Y-%m-%d")
+        
+        url = "https://api.open-meteo.com/v1/forecast"
+        params = {
+            "latitude": lat,
+            "longitude": lon,
+            "daily": [
+                "temperature_2m_max",
+                "temperature_2m_min", 
+                "precipitation_sum",
+                "rain_sum",
+                "uv_index_max",
+                "sunshine_duration"
+            ],
+            "start_date": start_date,
+            "end_date": end_date,
+            "timezone": "auto"
+        }
+        
+        try:
+            responses = self.openmeteo.weather_api(url, params=params)
+            response = responses[0]  # Process first location
+            
+            print(f"Coordinates: {response.Latitude()}°N {response.Longitude()}°E")
+            print(f"Elevation: {response.Elevation()} m asl")
+            print(f"Timezone: UTC{response.UtcOffsetSeconds()//3600:+d}")
+            
+            # Process daily data
+            daily = response.Daily()
+            
+            # Extract data as numpy arrays (much faster!)
+            daily_data = {
+                "date": pd.date_range(
+                    start=pd.to_datetime(daily.Time(), unit="s", utc=True),
+                    end=pd.to_datetime(daily.TimeEnd(), unit="s", utc=True),
+                    freq=pd.Timedelta(seconds=daily.Interval()),
+                    inclusive="left"
+                ),
+                "temperature_2m_max": daily.Variables(0).ValuesAsNumpy(),
+                "temperature_2m_min": daily.Variables(1).ValuesAsNumpy(),
+                "precipitation_sum": daily.Variables(2).ValuesAsNumpy(),
+                "rain_sum": daily.Variables(3).ValuesAsNumpy(),
+                "uv_index_max": daily.Variables(4).ValuesAsNumpy(),
+                "sunshine_duration": daily.Variables(5).ValuesAsNumpy()
+            }
+            
+            # Create DataFrame for easy analysis
+            daily_dataframe = pd.DataFrame(data=daily_data)
+            
+            return daily_dataframe, response
+            
+        except Exception as e:
+            print(f"Error fetching weather data: {e}")
+            return None, None
+    
+    def analyze_weather_for_plants(self, weather_df):
+        """Analyze weather data and create plant-specific metrics"""
+        
+        if weather_df is None or weather_df.empty:
+            return None
+        
+        # Handle NaN values by filling with 0 or mean
+        weather_df = weather_df.fillna(0)
+        
+        # Calculate plant-relevant metrics using pandas (more efficient)
+        plant_conditions = {
+            "avg_temp_max": round(weather_df['temperature_2m_max'].mean(), 1),
+            "avg_temp_min": round(weather_df['temperature_2m_min'].mean(), 1),
+            "total_precipitation": round(weather_df['precipitation_sum'].sum(), 1),
+            "total_rain": round(weather_df['rain_sum'].sum(), 1),
+            "total_sunshine_hours": round(weather_df['sunshine_duration'].sum() / 3600, 1),  # Convert to hours
+            "max_uv_index": round(weather_df['uv_index_max'].max(), 1),
+            "days_analyzed": len(weather_df),
+            "temp_range": round(weather_df['temperature_2m_max'].max() - weather_df['temperature_2m_min'].min(), 1)
+        }
+        
+        return plant_conditions
+    
+    def create_transformation_prompt(self, image_path, plant_conditions):
+        """Create a detailed prompt for image transformation based on weather AND image analysis"""
+        
+        if not plant_conditions:
+            return "Show this plant after one week of growth"
+        
+        #  STEP 3A: Analyze original image
+        try:
+            image = Image.open(image_path).convert("RGB")
+            # Basic image analysis
+            width, height = image.size
+            aspect_ratio = width / height
+            
+            # Simple plant type detection based on image characteristics
+            plant_type = self.detect_plant_type(image)
+            plant_health = self.assess_plant_health(image)
+            
+            print(f"📸 Image Analysis:")
+            print(f"   Plant type detected: {plant_type}")
+            print(f"   Current health: {plant_health}")
+            print(f"   Image size: {width}x{height}")
+            
+        except Exception as e:
+            print(f"Warning: Could not analyze image: {e}")
+            plant_type = "generic plant"
+            plant_health = "healthy"
+            
+        #  STEP 3B: Weather analysis with plant-specific logic
+        temp_avg = (plant_conditions['avg_temp_max'] + plant_conditions['avg_temp_min']) / 2
+        
+        # Temperature effects (adjusted by plant type)
+        if plant_type == "basil" or "herb" in plant_type:
+            if temp_avg > 25:
+                temp_effect = "warm weather promoting vigorous basil growth with larger, aromatic leaves and bushier structure"
+            elif temp_avg < 15:
+                temp_effect = "cool weather slowing basil growth with smaller, less vibrant leaves"
+            else:
+                temp_effect = "optimal temperature for basil supporting steady growth with healthy green foliage"
+        else:
+            if temp_avg > 25:
+                temp_effect = "warm weather promoting vigorous growth with larger, darker green leaves"
+            elif temp_avg < 10:
+                temp_effect = "cool weather slowing growth with smaller, pale leaves"
+            else:
+                temp_effect = "moderate temperature supporting steady growth with healthy green foliage"
+            
+        # Water effects
+        if plant_conditions['total_rain'] > 20:
+            water_effect = "abundant rainfall keeping leaves lush, turgid and deep green"
+        elif plant_conditions['total_rain'] < 5:
+            water_effect = "dry conditions causing slight leaf wilting and browning at edges"
+        else:
+            water_effect = "adequate moisture maintaining crisp, healthy leaf appearance"
+            
+        # Sunlight effects
+        if plant_conditions['total_sunshine_hours'] > 50:
+            sun_effect = "plenty of sunlight encouraging dense, compact foliage growth"
+        elif plant_conditions['total_sunshine_hours'] < 20:
+            sun_effect = "limited sunlight causing elongated stems and sparse leaf growth"
+        else:
+            sun_effect = "moderate sunlight supporting balanced, proportional growth"
+            
+        # UV effects
+        if plant_conditions['max_uv_index'] > 7:
+            uv_effect = "high UV causing slight leaf thickening and waxy appearance"
+        else:
+            uv_effect = "moderate UV maintaining normal leaf texture"
+        
+        #  STEP 3C: Create comprehensive prompt combining image + weather analysis
+        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"""
+        
+    def detect_plant_type(self, image):
+        """Simple plant type detection based on image characteristics"""
+        # This is a simplified version - in a real app you'd use a plant classification model
+        # For now, we'll do basic analysis
+        
+        # Convert to array for analysis
+        img_array = np.array(image)
+        
+        # Analyze color distribution
+        green_pixels = np.sum((img_array[:,:,1] > img_array[:,:,0]) & (img_array[:,:,1] > img_array[:,:,2]))
+        total_pixels = img_array.shape[0] * img_array.shape[1]
+        green_ratio = green_pixels / total_pixels
+        
+        # Simple heuristics (could be improved with ML)
+        if green_ratio > 0.4:
+            return "basil"  # Assume basil for high green content
+        else:
+            return "generic plant"
+    
+    def assess_plant_health(self, image):
+        """Assess basic plant health from image"""
+        img_array = np.array(image)
+        
+        # Analyze brightness and color vibrancy
+        brightness = np.mean(img_array)
+        green_channel = np.mean(img_array[:,:,1])
+        
+        if brightness > 150 and green_channel > 120:
+            return "healthy and vibrant"
+        elif brightness > 100 and green_channel > 80:
+            return "moderately healthy"
+        else:
+            return "showing some stress", plant_type, plant_health
+    
+    def transform_plant_image(self, image_path, prompt):
+        """STEP 4: Generate new image based on analyzed prompt"""
+        
+        if self.image_model is None:
+            self.load_image_model()
+            
+        try:
+            # Load and prepare image
+            image = Image.open(image_path).convert("RGB")
+            
+            # Resize if too large (for memory efficiency)
+            if max(image.size) > 1024:
+                image.thumbnail((1024, 1024), Image.Resampling.LANCZOS)
+            
+            print(f" STEP 4: Generating transformed image...")
+            print(f"   Using prompt: {prompt}")
+            
+            # Transform image
+            result = self.image_model(
+                prompt, 
+                image=image, 
+                num_inference_steps=20,
+                image_guidance_scale=1.5,
+                guidance_scale=7.5
+            ).images[0]
+            
+            return result
+            
+        except Exception as e:
+            print(f"Error transforming image: {e}")
+            return None
+    
+    def predict_plant_growth(self, image_path, lat=None, lon=None, output_path="predicted_plant.jpg", days=7):
+        """Complete pipeline: weather + image transformation"""
+        
+        # Auto-detect location if not provided
+        if lat is None or lon is None:
+            print("Auto-detecting location...")
+            lat, lon = self.get_current_location()
+        
+        print(f"Starting plant prediction for coordinates: {lat:.4f}, {lon:.4f}")
+        print(f"Analyzing {days} days of weather data...")
+        
+        # Step 1: Get weather data using official Open-Meteo client
+        print("Fetching weather data with caching and retry...")
+        weather_df, response_info = self.get_weather_forecast(lat, lon, days)
+        
+        if weather_df is None:
+            print("Failed to get weather data")
+            return None
+            
+        print(f"Weather data retrieved for {len(weather_df)} days")
+        print("\nWeather Overview:")
+        print(weather_df[['date', 'temperature_2m_max', 'temperature_2m_min', 'precipitation_sum', 'sunshine_duration']].head())
+        
+        # Step 2: Analyze weather for plants
+        plant_conditions = self.analyze_weather_for_plants(weather_df)
+        print(f"\nPlant-specific weather analysis: {plant_conditions}")
+        
+        # Step 3: Analyze image + weather to create intelligent prompt
+        print("\nSTEP 3: Analyzing image and creating transformation prompt...")
+        prompt, plant_type, plant_health = self.create_transformation_prompt(image_path, plant_conditions)
+        print(f"Plant identified as: {plant_type}")
+        print(f"Current health: {plant_health}")
+        print(f"Generated transformation prompt: {prompt}")
+        
+        # Step 4: Generate transformed image
+        print("\nSTEP 4: Generating prediction image...")
+        result_image = self.transform_plant_image(image_path, prompt)
+        
+        if result_image:
+            result_image.save(output_path)
+            print(f"Plant growth prediction saved to: {output_path}")
+            return result_image, plant_conditions, weather_df, plant_type, plant_health
+        else:
+            print("Failed to transform image")
+            return None
+
+# Example usage
+if __name__ == "__main__":
+    # Initialize predictor
+    predictor = PlantPredictor()
+    
+    # Predict plant growth
+    result = predictor.predict_plant_growth(
+        image_path="./foto/basilico.jpg",
+        output_path="predicted_plant_growth.jpg",
+        days=7
+    )
+    
+    if result:
+        image, conditions, weather_data = result
+        print("\n" + "="*50)
+        print("PLANT PREDICTION COMPLETED SUCCESSFULLY!")
+        print("="*50)
+        print(f"Weather conditions analyzed: {conditions}")
+        print(f"Weather data shape: {weather_data.shape}")
+        print(f"Temperature range: {conditions['avg_temp_min']}°C to {conditions['avg_temp_max']}°C")
+        print(f"Total precipitation: {conditions['total_rain']}mm")
+        print(f"Sunshine hours: {conditions['total_sunshine_hours']}h")
+    else:
+        print("Plant prediction failed.")