Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import numpy as np
+from sklearn.ensemble import RandomForestClassifier
+import matplotlib.pyplot as plt
+import seaborn as sns
+from datetime import datetime
+
+# Synthetic Dataset Creation
+def create_synthetic_dataset():
+    # Districts of Andhra Pradesh
+    districts = [
+        'Anantapur', 'Chittoor', 'East Godavari', 'Guntur', 'Krishna',
+        'Kurnool', 'Nellore', 'Prakasam', 'Srikakulam', 'Visakhapatnam',
+        'Vizianagaram', 'West Godavari', 'YSR Kadapa'
+    ]
+    
+    # Common crops in Andhra Pradesh
+    crops = [
+        'Rice', 'Maize', 'Cotton', 'Groundnut', 'Red Gram (Toor Dal)',
+        'Green Gram (Moong Dal)', 'Black Gram (Urad Dal)', 'Sugarcane',
+        'Chilli', 'Turmeric', 'Tobacco', 'Mango', 'Banana', 'Coconut',
+        'Cashew', 'Soybean', 'Sunflower', 'Jowar (Sorghum)', 'Bajra (Pearl Millet)'
+    ]
+    
+    # Months
+    months = ['January', 'February', 'March', 'April', 'May', 'June',
+              'July', 'August', 'September', 'October', 'November', 'December']
+    
+    # Create synthetic data
+    np.random.seed(42)
+    num_samples = 5000
+    
+    data = {
+        'District': np.random.choice(districts, num_samples),
+        'Month': np.random.choice(months, num_samples),
+        'Temperature': np.random.uniform(20, 40, num_samples),
+        'Rainfall': np.random.uniform(0, 300, num_samples),
+        'Soil_Type': np.random.choice(['Black', 'Red', 'Alluvial', 'Laterite'], num_samples),
+        'Crop': np.random.choice(crops, num_samples),
+        'Suitability': np.random.choice([0, 1], num_samples, p=[0.3, 0.7])
+    }
+    
+    # Add some logical patterns based on real-world knowledge
+    for i in range(num_samples):
+        district = data['District'][i]
+        month = data['Month'][i]
+        
+        # Adjust temperature based on month
+        if month in ['December', 'January', 'February']:
+            data['Temperature'][i] = np.random.uniform(15, 28)
+        elif month in ['March', 'April', 'May']:
+            data['Temperature'][i] = np.random.uniform(28, 42)
+        else:
+            data['Temperature'][i] = np.random.uniform(25, 35)
+        
+        # Adjust rainfall based on district and month
+        if district in ['Visakhapatnam', 'Srikakulam', 'Vizianagaram']:
+            if month in ['July', 'August', 'September']:
+                data['Rainfall'][i] = np.random.uniform(150, 300)
+            else:
+                data['Rainfall'][i] = np.random.uniform(50, 150)
+        elif district in ['Anantapur', 'Kurnool', 'YSR Kadapa']:
+            data['Rainfall'][i] = np.random.uniform(0, 100)
+        else:
+            if month in ['July', 'August', 'September']:
+                data['Rainfall'][i] = np.random.uniform(100, 250)
+            else:
+                data['Rainfall'][i] = np.random.uniform(20, 100)
+        
+        # Adjust suitability based on some logical conditions
+        crop = data['Crop'][i]
+        
+        # Rice needs more water
+        if crop == 'Rice' and data['Rainfall'][i] < 100:
+            data['Suitability'][i] = 0
+        
+        # Groundnut grows well in Anantapur
+        if crop == 'Groundnut' and district == 'Anantapur':
+            data['Suitability'][i] = 1
+        
+        # Coconut grows well in coastal areas
+        if crop == 'Coconut' and district in ['East Godavari', 'West Godavari', 'Visakhapatnam']:
+            data['Suitability'][i] = 1
+        
+        # Chilli grows well in Guntur
+        if crop == 'Chilli' and district == 'Guntur':
+            data['Suitability'][i] = 1
+    
+    df = pd.DataFrame(data)
+    return df, crops, districts, months
+
+# Create dataset
+df, crops, districts, months = create_synthetic_dataset()
+
+# Train machine learning model
+def train_model(df):
+    # Convert categorical variables to numerical
+    df_encoded = pd.get_dummies(df, columns=['District', 'Month', 'Soil_Type', 'Crop'])
+    
+    X = df_encoded.drop('Suitability', axis=1)
+    y = df_encoded['Suitability']
+    
+    model = RandomForestClassifier(n_estimators=100, random_state=42)
+    model.fit(X, y)
+    
+    return model
+
+model = train_model(df)
+
+# Crop information and precautions
+crop_info = {
+    'Rice': {
+        'description': 'Staple food crop requiring abundant water',
+        'precautions': [
+            'Ensure proper water management (5-10 cm standing water)',
+            'Use certified seeds for better yield',
+            'Control weeds in early stages',
+            'Monitor for pests like stem borers and leaf folders'
+        ]
+    },
+    'Maize': {
+        'description': 'Versatile cereal crop grown in diverse conditions',
+        'precautions': [
+            'Plant in well-drained soil',
+            'Maintain proper spacing (60x20 cm)',
+            'Apply nitrogen in split doses',
+            'Watch for fall armyworm infestation'
+        ]
+    },
+    'Cotton': {
+        'description': 'Important cash crop known as "white gold"',
+        'precautions': [
+            'Use Bt cotton seeds for pest resistance',
+            'Monitor for pink bollworm',
+            'Practice crop rotation to prevent soil depletion',
+            'Avoid waterlogging in fields'
+        ]
+    },
+    'Groundnut': {
+        'description': 'Oilseed crop important for protein and oil',
+        'precautions': [
+            'Plant in well-drained sandy loam soil',
+            'Apply gypsum at flowering stage',
+            'Harvest at proper maturity to avoid aflatoxin',
+            'Store in dry conditions'
+        ]
+    },
+    'Red Gram (Toor Dal)': {
+        'description': 'Important pulse crop rich in protein',
+        'precautions': [
+            'Drought resistant but needs irrigation at flowering',
+            'Treat seeds with rhizobium culture',
+            'Control pod borer with recommended pesticides',
+            'Harvest when 80% pods are mature'
+        ]
+    },
+    'Green Gram (Moong Dal)': {
+        'description': 'Short duration pulse crop',
+        'precautions': [
+            'Grows well in well-drained soils',
+            'Short duration (60-70 days)',
+            'Susceptible to yellow mosaic virus - use resistant varieties',
+            'Harvest when 80% pods are mature'
+        ]
+    },
+    'Black Gram (Urad Dal)': {
+        'description': 'Important pulse crop for protein',
+        'precautions': [
+            'Grows well in black cotton soils',
+            'Treat seeds with rhizobium culture',
+            'Control leaf spot diseases',
+            'Harvest when pods turn black'
+        ]
+    },
+    'Sugarcane': {
+        'description': 'Important cash crop for sugar production',
+        'precautions': [
+            'Requires heavy irrigation',
+            'Use disease-free setts for planting',
+            'Control early shoot borer',
+            'Harvest at proper maturity (10-12 months)'
+        ]
+    },
+    'Chilli': {
+        'description': 'Important spice crop with high value',
+        'precautions': [
+            'Requires well-drained fertile soil',
+            'Irrigate carefully to avoid flower drop',
+            'Control fruit borer and mites',
+            'Harvest at color break stage'
+        ]
+    },
+    'Turmeric': {
+        'description': 'Important spice crop with medicinal value',
+        'precautions': [
+            'Plant in well-drained fertile soil',
+            'Treat seed rhizomes with fungicide',
+            'Control leaf spot diseases',
+            'Harvest after 8-9 months when leaves dry'
+        ]
+    },
+    'Tobacco': {
+        'description': 'Commercial crop mainly for export',
+        'precautions': [
+            'Requires well-drained sandy loam soils',
+            'Needs careful curing after harvest',
+            'Follow government regulations',
+            'Practice crop rotation'
+        ]
+    },
+    'Mango': {
+        'description': 'Important fruit crop of Andhra Pradesh',
+        'precautions': [
+            'Plant in well-drained deep soils',
+            'Prune for proper canopy management',
+            'Control mango hopper and fruit fly',
+            'Harvest at proper maturity'
+        ]
+    },
+    'Banana': {
+        'description': 'Important fruit crop with high yield',
+        'precautions': [
+            'Requires heavy irrigation and fertilization',
+            'Plant disease-free tissue culture plants',
+            'Control sigatoka leaf spot disease',
+            'Support plants during fruiting'
+        ]
+    },
+    'Coconut': {
+        'description': 'Important plantation crop of coastal areas',
+        'precautions': [
+            'Plant in coastal sandy soils',
+            'Apply balanced fertilizers regularly',
+            'Control rhinoceros beetle',
+            'Intercrop with cocoa or pepper'
+        ]
+    },
+    'Cashew': {
+        'description': 'Important plantation crop for export',
+        'precautions': [
+            'Plant in well-drained sandy soils',
+            'Prune for proper shape',
+            'Control tea mosquito bug',
+            'Harvest nuts when apple turns pink'
+        ]
+    },
+    'Soybean': {
+        'description': 'Oilseed crop rich in protein',
+        'precautions': [
+            'Plant in well-drained soils',
+            'Inoculate seeds with rhizobium',
+            'Control yellow mosaic virus',
+            'Harvest when leaves yellow and drop'
+        ]
+    },
+    'Sunflower': {
+        'description': 'Important oilseed crop',
+        'precautions': [
+            'Plant in well-drained soils',
+            'Provide support if needed',
+            'Control head borer',
+            'Harvest when back of head turns yellow'
+        ]
+    },
+    'Jowar (Sorghum)': {
+        'description': 'Traditional millet crop',
+        'precautions': [
+            'Drought resistant crop',
+            'Control shoot fly in early stages',
+            'Harvest when grains are hard'
+        ]
+    },
+    'Bajra (Pearl Millet)': {
+        'description': 'Traditional drought-resistant crop',
+        'precautions': [
+            'Grows well in poor soils',
+            'Control downy mildew',
+            'Harvest when grains are hard'
+        ]
+    }
+}
+
+# District-wise climate information
+district_climate = {
+    'Anantapur': {
+        'description': 'Hot and dry climate with low rainfall',
+        'soil': 'Red sandy loam soils',
+        'avg_temp': '28-40°C',
+        'avg_rainfall': '500-600 mm'
+    },
+    'Chittoor': {
+        'description': 'Moderate climate with some hilly areas',
+        'soil': 'Red soils and black cotton soils',
+        'avg_temp': '22-38°C',
+        'avg_rainfall': '900-1000 mm'
+    },
+    'East Godavari': {
+        'description': 'Coastal district with high humidity',
+        'soil': 'Alluvial and deltaic soils',
+        'avg_temp': '24-36°C',
+        'avg_rainfall': '1000-1100 mm'
+    },
+    'Guntur': {
+        'description': 'Coastal plains with hot climate',
+        'soil': 'Black cotton soils',
+        'avg_temp': '25-38°C',
+        'avg_rainfall': '800-900 mm'
+    },
+    'Krishna': {
+        'description': 'Coastal district with fertile delta',
+        'soil': 'Alluvial and black soils',
+        'avg_temp': '24-36°C',
+        'avg_rainfall': '900-1000 mm'
+    },
+    'Kurnool': {
+        'description': 'Semi-arid climate with low rainfall',
+        'soil': 'Red soils and black soils',
+        'avg_temp': '26-40°C',
+        'avg_rainfall': '600-700 mm'
+    },
+    'Nellore': {
+        'description': 'Coastal district with moderate rainfall',
+        'soil': 'Red soils and sandy loams',
+        'avg_temp': '24-36°C',
+        'avg_rainfall': '1000-1100 mm'
+    },
+    'Prakasam': {
+        'description': 'Mixed coastal and dry climate',
+        'soil': 'Red soils and sandy loams',
+        'avg_temp': '25-38°C',
+        'avg_rainfall': '800-900 mm'
+    },
+    'Srikakulam': {
+        'description': 'Northern coastal district with good rainfall',
+        'soil': 'Red and alluvial soils',
+        'avg_temp': '22-34°C',
+        'avg_rainfall': '1100-1200 mm'
+    },
+    'Visakhapatnam': {
+        'description': 'Coastal district with hilly terrain',
+        'soil': 'Red and laterite soils',
+        'avg_temp': '22-33°C',
+        'avg_rainfall': '1000-1100 mm'
+    },
+    'Vizianagaram': {
+        'description': 'Coastal district with moderate climate',
+        'soil': 'Red and alluvial soils',
+        'avg_temp': '23-35°C',
+        'avg_rainfall': '1000-1100 mm'
+    },
+    'West Godavari': {
+        'description': 'Fertile delta region with high humidity',
+        'soil': 'Alluvial and black soils',
+        'avg_temp': '24-36°C',
+        'avg_rainfall': '1000-1100 mm'
+    },
+    'YSR Kadapa': {
+        'description': 'Hot and dry climate with low rainfall',
+        'soil': 'Red soils and black soils',
+        'avg_temp': '27-40°C',
+        'avg_rainfall': '600-700 mm'
+    }
+}
+
+# Prediction function
+def predict_crop(district, month, crop_choice=None):
+    # Get current temperature and rainfall based on district and month
+    temp = df[(df['District'] == district) & (df['Month'] == month)]['Temperature'].mean()
+    rainfall = df[(df['District'] == district) & (df['Month'] == month)]['Rainfall'].mean()
+    soil_type = df[df['District'] == district]['Soil_Type'].mode()[0]
+    
+    # Prepare input for model
+    input_data = {
+        'District': district,
+        'Month': month,
+        'Temperature': temp,
+        'Rainfall': rainfall,
+        'Soil_Type': soil_type
+    }
+    
+    # If user has selected a crop
+    if crop_choice and crop_choice != "I don't know":
+        input_data['Crop'] = crop_choice
+        input_df = pd.DataFrame([input_data])
+        input_encoded = pd.get_dummies(input_df, columns=['District', 'Month', 'Soil_Type', 'Crop'])
+        
+        # Ensure all columns are present (add missing with 0)
+        train_columns = pd.get_dummies(df, columns=['District', 'Month', 'Soil_Type', 'Crop']).columns.drop('Suitability')
+        for col in train_columns:
+            if col not in input_encoded.columns:
+                input_encoded[col] = 0
+        
+        input_encoded = input_encoded[train_columns]
+        
+        prediction = model.predict(input_encoded)[0]
+        
+        if prediction == 1:
+            result = f"✅ {crop_choice} is suitable to grow in {district} during {month}."
+            precautions = crop_info[crop_choice]['precautions']
+            precautions_text = "\n".join([f"• {precaution}" for precaution in precautions])
+            output = f"{result}\n\n📌 Precautions:\n{precautions_text}"
+        else:
+            alternatives = get_alternative_crops(district, month)
+            alt_text = "\n".join([f"• {crop}" for crop in alternatives[:3]])
+            output = f"❌ {crop_choice} is not recommended for {district} in {month}.\n\n🌱 Better alternatives:\n{alt_text}"
+    else:
+        # Recommend best crops
+        recommended_crops = get_alternative_crops(district, month)
+        rec_text = "\n".join([f"• {crop}" for crop in recommended_crops[:5]])
+        
+        # Get climate info
+        climate = district_climate[district]
+        climate_text = (
+            f"🌡️ Avg Temperature: {climate['avg_temp']}\n"
+            f"🌧️ Avg Rainfall: {climate['avg_rainfall']}\n"
+            f"🌱 Soil Type: {climate['soil']}"
+        )
+        
+        output = (
+            f"🌾 Recommended crops for {district} in {month}:\n\n{rec_text}\n\n"
+            f"📌 District Climate Info:\n{climate_text}"
+        )
+    
+    return output
+
+def get_alternative_crops(district, month):
+    # Get current temperature and rainfall based on district and month
+    temp = df[(df['District'] == district) & (df['Month'] == month)]['Temperature'].mean()
+    rainfall = df[(df['District'] == district) & (df['Month'] == month)]['Rainfall'].mean()
+    soil_type = df[df['District'] == district]['Soil_Type'].mode()[0]
+    
+    # Test all crops and get suitability scores
+    crop_scores = []
+    for crop in crops:
+        input_data = {
+            'District': district,
+            'Month': month,
+            'Temperature': temp,
+            'Rainfall': rainfall,
+            'Soil_Type': soil_type,
+            'Crop': crop
+        }
+        input_df = pd.DataFrame([input_data])
+        input_encoded = pd.get_dummies(input_df, columns=['District', 'Month', 'Soil_Type', 'Crop'])
+        
+        # Ensure all columns are present (add missing with 0)
+        train_columns = pd.get_dummies(df, columns=['District', 'Month', 'Soil_Type', 'Crop']).columns.drop('Suitability')
+        for col in train_columns:
+            if col not in input_encoded.columns:
+                input_encoded[col] = 0
+        
+        input_encoded = input_encoded[train_columns]
+        
+        # Get probability instead of binary prediction
+        proba = model.predict_proba(input_encoded)[0][1]
+        crop_scores.append((crop, proba))
+    
+    # Sort by probability
+    crop_scores.sort(key=lambda x: x[1], reverse=True)
+    return [crop for crop, score in crop_scores if score > 0.7]
+
+# Custom CSS for styling
+css = """
+.gradio-container {
+    font-family: 'Poppins', sans-serif;
+    background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
+}
+
+.title {
+    text-align: center;
+    color: #2c3e50;
+    font-size: 28px;
+    font-weight: 600;
+    margin-bottom: 20px;
+    background: linear-gradient(90deg, #4b6cb7 0%, #182848 100%);
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+}
+
+.description {
+    text-align: center;
+    color: #4a5568;
+    margin-bottom: 30px;
+    font-size: 16px;
+}
+
+.input-section {
+    background: white;
+    padding: 20px;
+    border-radius: 10px;
+    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
+    margin-bottom: 20px;
+}
+
+.input-label {
+    font-weight: 500;
+    color: #2d3748;
+    margin-bottom: 8px;
+    display: block;
+}
+
+.output-section {
+    background: white;
+    padding: 25px;
+    border-radius: 10px;
+    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
+    min-height: 200px;
+    font-size: 16px;
+    line-height: 1.6;
+    white-space: pre-wrap;
+}
+
+.output-title {
+    color: #2c3e50;
+    font-weight: 600;
+    margin-bottom: 15px;
+    font-size: 20px;
+    border-bottom: 2px solid #e2e8f0;
+    padding-bottom: 8px;
+}
+
+.btn-primary {
+    background: linear-gradient(90deg, #4b6cb7 0%, #182848 100%);
+    border: none;
+    color: white;
+    padding: 12px 24px;
+    border-radius: 8px;
+    font-weight: 500;
+    cursor: pointer;
+    transition: all 0.3s ease;
+    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
+}
+
+.btn-primary:hover {
+    transform: translateY(-2px);
+    box-shadow: 0 7px 14px rgba(0, 0, 0, 0.1);
+}
+
+.select-dropdown, .text-input {
+    width: 100%;
+    padding: 12px;
+    border: 1px solid #e2e8f0;
+    border-radius: 8px;
+    font-size: 16px;
+    transition: all 0.3s ease;
+}
+
+.select-dropdown:focus, .text-input:focus {
+    border-color: #4b6cb7;
+    box-shadow: 0 0 0 3px rgba(75, 108, 183, 0.2);
+    outline: none;
+}
+
+.footer {
+    text-align: center;
+    margin-top: 30px;
+    color: #718096;
+    font-size: 14px;
+}
+
+.success {
+    color: #2e7d32;
+}
+
+.warning {
+    color: #d32f2f;
+}
+
+.recommendation {
+    background: #f0f4f8;
+    padding: 15px;
+    border-radius: 8px;
+    margin-top: 15px;
+    border-left: 4px solid #4b6cb7;
+}
+
+.crop-image {
+    max-width: 100%;
+    border-radius: 8px;
+    margin-top: 15px;
+    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
+}
+"""
+
+# Gradio Interface
+with gr.Blocks(css=css) as demo:
+    gr.Markdown("""
+    <div class="title">🌱 Crop Vision: Andhra Pradesh Farmer Decision Support System</div>
+    <div class="description">
+        Helping farmers in Andhra Pradesh make informed decisions about crop selection based on location and season
+    </div>
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            with gr.Group(visible=True) as input_section:
+                gr.Markdown("### 📍 Enter Your Farming Details")
+                district = gr.Dropdown(
+                    label="Select Your District",
+                    choices=districts,
+                    value="Anantapur",
+                    interactive=True,
+                    elem_classes="select-dropdown"
+                )
+                month = gr.Dropdown(
+                    label="Select Planting Month",
+                    choices=months,
+                    value=datetime.now().strftime("%B"),
+                    interactive=True,
+                    elem_classes="select-dropdown"
+                )
+                crop_choice = gr.Dropdown(
+                    label="Do you have a specific crop in mind? (Select 'I don't know' for recommendations)",
+                    choices=["I don't know"] + sorted(crops),
+                    value="I don't know",
+                    interactive=True,
+                    elem_classes="select-dropdown"
+                )
+                submit_btn = gr.Button("Get Recommendation", variant="primary", elem_classes="btn-primary")
+        
+        with gr.Column():
+            with gr.Group(visible=True) as output_section:
+                gr.Markdown("### 🌾 Recommendation")
+                output = gr.Textbox(
+                    label="",
+                    interactive=False,
+                    lines=15,
+                    elem_classes="output-section"
+                )
+    
+    gr.Markdown("""
+    <div class="footer">
+        Note: This system provides recommendations based on historical data and machine learning predictions. 
+        Always consult with local agricultural experts before making final decisions.
+    </div>
+    """)
+    
+    submit_btn.click(
+        fn=predict_crop,
+        inputs=[district, month, crop_choice],
+        outputs=output
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()