app.py

from flask import Flask, render_template, request, jsonify
import requests
import google.generativeai as genai
import os
import json
from gtts import gTTS
import io
import base64

app = Flask(__name__)

# Mapping of SoilGrids parameter codes
PARAM_MAP = {
    "bdod": "Bulk Density", "cec": "Cation Exchange Capacity", "cfvo": "Coarse Fragment Volume",
    "clay": "Clay Content", "nitrogen": "Nitrogen Content", "ocd": "Organic Carbon Density",
    "ocs": "Organic Carbon Stock", "phh2o": "Soil pH", "sand": "Sand Content",
    "silt": "Silt Content", "soc": "Soil Organic Carbon", "wv0010": "Water Content (0-10cm)",
    "wv0033": "Water Content (0-33cm)", "wv1500": "Water Content (1500mm)"
}

LANG_MAP = {
    "English": "en", "Hindi": "hi", "Bengali": "bn", "Telugu": "te", "Marathi": "mr",
    "Tamil": "ta", "Gujarati": "gu", "Urdu": "ur", "Kannada": "kn", "Odia": "or",
    "Malayalam": "ml"
}

@app.route('/')
def index():
    return render_template('index.html')

@app.route('/get_soil_report', methods=['POST'])
def get_soil_report():
    data = request.get_json()
    lat, lon = data.get("lat"), data.get("lon")
    if not lat or not lon:
        return jsonify({"error": "Latitude and Longitude are required"}), 400

    headers = {"accept": "application/json"}
    
    # Retry configuration
    max_retries = 3
    retry_delay = 2  # seconds
    
    for attempt in range(max_retries):
        try:
            print(f"Attempt {attempt + 1}/{max_retries} to fetch soil data...")
            
            # Fetch classification data
            class_response = requests.get(
                "https://rest.isric.org/soilgrids/v2.0/classification/query",
                params={"lon": lon, "lat": lat, "number_classes": 5},
                headers=headers,
                timeout=30
            )
            class_response.raise_for_status()
            class_data = class_response.json()
            soil_classification = {
                "soil_type": class_data.get("wrb_class_name", "Unknown"),
                "probabilities": class_data.get("wrb_class_probability", [])
            }
            
            # Fetch properties data
            prop_response = requests.get(
                "https://rest.isric.org/soilgrids/v2.0/properties/query",
                params={
                    "lon": lon, "lat": lat,
                    "property": list(PARAM_MAP.keys()),
                    "depth": "5-15cm",
                    "value": "mean"
                },
                headers=headers,
                timeout=30
            )
            prop_response.raise_for_status()
            prop_data = prop_response.json()
            
            properties_list = []
            for layer in prop_data.get("properties", {}).get("layers", []):
                param_code = layer.get("name")
                name = PARAM_MAP.get(param_code, param_code.upper())
                value = layer.get("depths", [{}])[0].get("values", {}).get("mean")
                unit = layer.get("unit_measure", {}).get("mapped_units", "")
                if value is not None:
                    if param_code == "phh2o":
                        value /= 10.0
                        unit = "pH"
                    elif param_code in ["wv0010", "wv0033", "wv1500"]:
                        value /= 100.0
                        unit = "cm³/cm³"
                properties_list.append({"parameter": name, "value": value, "unit": unit})
            
            print("Successfully fetched soil data from API")
            return jsonify({"classification": soil_classification, "properties": properties_list})
            
        except requests.exceptions.HTTPError as e:
            if e.response.status_code == 502:
                print(f"502 Bad Gateway error on attempt {attempt + 1}")
                if attempt < max_retries - 1:
                    import time
                    time.sleep(retry_delay)
                    retry_delay *= 2  # Exponential backoff
                    continue
                else:
                    # Use fallback mock data after all retries fail
                    print("API unavailable, using fallback mock data")
                    return jsonify({
                        "classification": {
                            "soil_type": "Cambisol (Sample Data - API Unavailable)",
                            "probabilities": [
                                ["Cambisol", 45.2],
                                ["Luvisol", 23.8],
                                ["Vertisol", 18.5],
                                ["Regosol", 8.3],
                                ["Fluvisol", 4.2]
                            ]
                        },
                        "properties": [
                            {"parameter": "Bulk Density", "value": 1.42, "unit": "kg/dm³"},
                            {"parameter": "Cation Exchange Capacity", "value": 18.5, "unit": "cmol/kg"},
                            {"parameter": "Clay Content", "value": 28.3, "unit": "%"},
                            {"parameter": "Sand Content", "value": 42.1, "unit": "%"},
                            {"parameter": "Silt Content", "value": 29.6, "unit": "%"},
                            {"parameter": "Soil pH", "value": 6.8, "unit": "pH"},
                            {"parameter": "Soil Organic Carbon", "value": 12.4, "unit": "g/kg"},
                            {"parameter": "Nitrogen Content", "value": 1.2, "unit": "g/kg"}
                        ],
                        "_note": "⚠️ The ISRIC SoilGrids API is currently unavailable. This is sample data for demonstration purposes only. Please try again later for actual soil data for your location."
                    })
            else:
                raise
                
        except requests.exceptions.RequestException as e:
            print(f"Request error on attempt {attempt + 1}: {e}")
            if attempt < max_retries - 1:
                import time
                time.sleep(retry_delay)
                retry_delay *= 2
                continue
            else:
                # Use fallback mock data after all retries fail
                print("API unavailable due to connection error, using fallback mock data")
                return jsonify({
                    "classification": {
                        "soil_type": "Cambisol (Sample Data - API Unavailable)",
                        "probabilities": [
                            ["Cambisol", 45.2],
                            ["Luvisol", 23.8],
                            ["Vertisol", 18.5],
                            ["Regosol", 8.3],
                            ["Fluvisol", 4.2]
                        ]
                    },
                    "properties": [
                        {"parameter": "Bulk Density", "value": 1.42, "unit": "kg/dm³"},
                        {"parameter": "Cation Exchange Capacity", "value": 18.5, "unit": "cmol/kg"},
                        {"parameter": "Clay Content", "value": 28.3, "unit": "%"},
                        {"parameter": "Sand Content", "value": 42.1, "unit": "%"},
                        {"parameter": "Silt Content", "value": 29.6, "unit": "%"},
                        {"parameter": "Soil pH", "value": 6.8, "unit": "pH"},
                        {"parameter": "Soil Organic Carbon", "value": 12.4, "unit": "g/kg"},
                        {"parameter": "Nitrogen Content", "value": 1.2, "unit": "g/kg"}
                    ],
                    "_note": "⚠️ The ISRIC SoilGrids API is currently unavailable (connection timeout). This is sample data for demonstration purposes only. Please try again later for actual soil data for your location."
                })

@app.route('/analyze_soil', methods=['POST'])
def analyze_soil():
    """Enhanced soil analysis with NVIDIA and Gemini fallback support."""
    try:
        data = request.get_json()
        soil_report = data.get("soil_report")
        language = data.get("language", "English")
        
        if not soil_report:
            return jsonify({"error": "Soil report data is missing"}), 400

        prompt = f"""

        Analyze the following soil report and provide recommendations. The response MUST be a single, valid JSON object, without any markdown formatting, comments, or surrounding text like ```json. The user wants the analysis in this language: {language}. Soil Report Data: {json.dumps(soil_report, indent=2)}

        JSON Structure to follow: {{"soilType": "Primary soil type", "generalInsights": ["Insight 1", "Insight 2"], "parameters": [{{"parameter": "Parameter Name", "value": "Value with Unit", "range": "Low/Normal/High", "comment": "Brief comment."}}], "cropRecommendations": [{{"crop": "Crop Name", "reason": "Brief reason."}}], "managementRecommendations": {{"fertilization": "Recommendation.", "irrigation": "Recommendation."}}}}

        """

        analysis_json = None
        last_error = None
        model_used = None

        # Try NVIDIA models first (from config.env)
        try:
            from image_summarizer import ModelConfig
            from openai import OpenAI
            
            config = ModelConfig()
            nvidia_api_key = config.get('nvidia_api_key')
            nvidia_models = config.get('nvidia_models', [])
            
            if nvidia_api_key and nvidia_models:
                nvidia_client = OpenAI(
                    base_url="https://integrate.api.nvidia.com/v1",
                    api_key=nvidia_api_key
                )
                
                for model_name in nvidia_models:
                    try:
                        print(f"Attempting NVIDIA model: {model_name}")
                        response = nvidia_client.chat.completions.create(
                            model=model_name,
                            messages=[{"role": "user", "content": prompt}],
                            max_tokens=config.get('max_tokens', 1000),
                            temperature=config.get('temperature', 0.2)
                        )
                        
                        cleaned_text = response.choices[0].message.content.strip()
                        json_start_index = cleaned_text.find('{')
                        json_end_index = cleaned_text.rfind('}') + 1

                        if json_start_index != -1 and json_end_index > json_start_index:
                            json_str = cleaned_text[json_start_index:json_end_index]
                            analysis_json = json.loads(json_str)
                            model_used = f"NVIDIA: {model_name}"
                            print(f"✅ Successfully used NVIDIA model: {model_name}")
                            break
                    except Exception as e:
                        print(f"NVIDIA model {model_name} failed: {e}")
                        last_error = e
                        continue
        except ImportError:
            print("⚠️ image_summarizer module not available, skipping NVIDIA models")
        except Exception as e:
            print(f"⚠️ NVIDIA fallback error: {e}")
            last_error = e

        # Fallback to Gemini models if NVIDIA failed
        if not analysis_json:
            print("Falling back to Gemini models...")
            api_key = os.getenv("GEMINI_API", "AIzaSyDkiYr-eSkqIXpZ1fHlik_YFsFtfQoFi0w")
            genai.configure(api_key=api_key)
            
            # Load Gemini models from config (all 7 models)
            models_to_try = config.get('gemini_models', ['gemini-2.5-flash', 'gemini-2.0-flash', 'gemini-3.0-flash'])

            for model_name in models_to_try:
                try:
                    print(f"Attempting Gemini model: {model_name}")
                    model = genai.GenerativeModel(model_name)
                    response = model.generate_content(prompt)
                    
                    cleaned_text = response.text.strip()
                    json_start_index = cleaned_text.find('{')
                    json_end_index = cleaned_text.rfind('}') + 1

                    if json_start_index != -1 and json_end_index > json_start_index:
                        json_str = cleaned_text[json_start_index:json_end_index]
                        analysis_json = json.loads(json_str)
                        model_used = f"Gemini: {model_name}"
                        print(f"✅ Successfully used Gemini model: {model_name}")
                        break
                    else:
                        raise ValueError("No valid JSON object found in the response.")

                except Exception as e:
                    print(f"Gemini model {model_name} failed: {e}")
                    last_error = e
                    continue
        
        if not analysis_json:
            raise Exception("All AI models (NVIDIA + Gemini) failed to generate a valid JSON response.") from last_error

        # Add metadata about which model was used
        analysis_json['_model_used'] = model_used

        # Generate TTS audio
        print("Generating audio summary...")
        summary_text = f"Soil analysis complete. The soil type is {analysis_json.get('soilType', 'not specified')}. "
        summary_text += "Recommended crops include: " + ", ".join([c['crop'] for c in analysis_json.get('cropRecommendations', [])]) + ". "
        summary_text += "For fertilization, " + analysis_json.get('managementRecommendations', {}).get('fertilization', "no recommendation was given.")
        
        lang_code = LANG_MAP.get(language, 'en')
        tts = gTTS(text=summary_text, lang=lang_code, slow=False)
        mp3_fp = io.BytesIO()
        tts.write_to_fp(mp3_fp)
        mp3_fp.seek(0)
        base64_audio = base64.b64encode(mp3_fp.read()).decode('utf-8')
        analysis_json['audioContent'] = f"data:audio/mp3;base64,{base64_audio}"
        print("Audio generation complete.")

        return jsonify(analysis_json)

    except Exception as e:
        print(f"!!! AN UNHANDLED ERROR OCCURRED in /analyze_soil: {e}")
        return jsonify({"error": f"An unexpected server error occurred: {str(e)}"}), 500


@app.route('/analyze_image', methods=['POST'])
def analyze_image():
    """New endpoint for image analysis with NVIDIA fallback."""
    try:
        # Check if image file is provided
        if 'image' not in request.files:
            return jsonify({"error": "No image file provided"}), 400
        
        image_file = request.files['image']
        if image_file.filename == '':
            return jsonify({"error": "No image selected"}), 400
        
        # Get optional prompt from form data
        prompt = request.form.get('prompt', 'Please analyze this image and provide detailed insights.')
        
        # Save uploaded image temporarily
        temp_image_path = os.path.join('temp_uploads', image_file.filename)
        os.makedirs('temp_uploads', exist_ok=True)
        image_file.save(temp_image_path)
        
        try:
            # Use ImageAnalyzer for analysis
            from image_summarizer import ImageAnalyzer
            analyzer = ImageAnalyzer()
            result = analyzer.analyze_image(temp_image_path, prompt)
            
            # Clean up temp file
            os.remove(temp_image_path)
            
            if result['success']:
                return jsonify({
                    'success': True,
                    'analysis': result['response'],
                    'model_used': result['model_used'],
                    'provider': result['provider']
                })
            else:
                return jsonify({
                    'success': False,
                    'error': result['error'],
                    'suggestions': result.get('suggestions', [])
                }), 500
                
        except Exception as e:
            # Clean up temp file on error
            if os.path.exists(temp_image_path):
                os.remove(temp_image_path)
            raise e
            
    except Exception as e:
        print(f"!!! ERROR in /analyze_image: {e}")
        return jsonify({"error": f"Image analysis failed: {str(e)}"}), 500

if __name__ == '__main__':
    app.run(debug=True, port=7860)