Update app.py

app.py

@@ -5,30 +5,30 @@ from sklearn.model_selection import train_test_split
 import gradio as gr
 import random
 
-# Generate synthetic dataset for Indian crops (focusing on South Indian states)
+# Generate synthetic dataset for Bangladesh crops
 def generate_synthetic_dataset(num_samples=5000):
     np.random.seed(42)
     
-    # Common crops in Andhra Pradesh and Telangana
+    # Common crops in Bangladesh
     crops = [
-        'Rice', 'Maize', 'Cotton', 'Groundnut', 'Red Gram (Toor Dal)', 
-        'Green Gram (Moong Dal)', 'Black Gram (Urad Dal)', 'Sunflower', 
-        'Sugarcane', 'Turmeric', 'Chilli', 'Tomato', 'Onion', 'Mango',
-        'Banana', 'Coconut', 'Soybean', 'Jowar (Sorghum)', 'Bajra (Pearl Millet)'
+        'Rice (Aman)', 'Rice (Boro)', 'Rice (Aus)', 'Jute', 'Wheat', 
+        'Maize', 'Potato', 'Sugarcane', 'Pulses (Mungbean)', 'Pulses (Lentil)', 
+        'Mustard', 'Sesame', 'Sunflower', 'Tea', 'Mango',
+        'Banana', 'Jackfruit', 'Litchi', 'Pineapple', 'Vegetables'
     ]
     
-    # Soil types common in the region
-    soil_types = ['Black Cotton', 'Red Sandy', 'Clayey', 'Loamy', 'Sandy Loam']
+    # Soil types common in Bangladesh
+    soil_types = ['Alluvial', 'Loamy', 'Clayey', 'Peaty', 'Sandy']
     
-    # Seasons in Indian agriculture
-    seasons = ['Kharif (June-Oct)', 'Rabi (Oct-Mar)', 'Zaid (Mar-Jun)', 'Whole Year']
+    # Seasons in Bangladesh agriculture
+    seasons = ['Kharif-1 (Mar-Jun)', 'Kharif-2 (Jul-Oct)', 'Rabi (Nov-Feb)', 'Whole Year']
     
     # Generate synthetic data
     data = {
-        'Temperature (°C)': np.random.uniform(10, 60, num_samples),
-        'Rainfall (mm)': np.random.uniform(0, 300, num_samples),
-        'Humidity (%)': np.random.uniform(20, 100, num_samples),
-        'Soil pH': np.random.uniform(4.5, 9.5, num_samples),
+        'Temperature (°C)': np.random.uniform(10, 40, num_samples),  # Bangladesh has more moderate temperatures
+        'Rainfall (mm)': np.random.uniform(100, 400, num_samples),  # Higher rainfall range
+        'Humidity (%)': np.random.uniform(60, 100, num_samples),    # Generally high humidity
+        'Soil pH': np.random.uniform(5.0, 8.5, num_samples),        # Slightly acidic to neutral
         'Soil Type': np.random.choice(soil_types, num_samples),
         'Nitrogen (N) Level': np.random.uniform(0, 150, num_samples),
         'Phosphorus (P) Level': np.random.uniform(0, 100, num_samples),
@@ -45,52 +45,56 @@ def generate_synthetic_dataset(num_samples=5000):
         crop = row['Crop']
         
         # Temperature adjustments
-        if crop in ['Rice', 'Banana', 'Coconut']:
-            df.at[idx, 'Temperature (°C)'] = np.random.uniform(25, 40)
-            df.at[idx, 'Humidity (%)'] = np.random.uniform(60, 100)
-        elif crop in ['Wheat', 'Barley']:
-            df.at[idx, 'Temperature (°C)'] = np.random.uniform(10, 25)
-        elif crop in ['Chilli', 'Tomato']:
-            df.at[idx, 'Temperature (°C)'] = np.random.uniform(20, 35)
+        if 'Rice' in crop:
+            df.at[idx, 'Temperature (°C)'] = np.random.uniform(25, 35)
+            df.at[idx, 'Humidity (%)'] = np.random.uniform(70, 100)
+        elif crop in ['Wheat', 'Mustard', 'Potato']:
+            df.at[idx, 'Temperature (°C)'] = np.random.uniform(15, 25)
+        elif crop in ['Jute', 'Tea']:
+            df.at[idx, 'Temperature (°C)'] = np.random.uniform(20, 30)
+            df.at[idx, 'Rainfall (mm)'] = np.random.uniform(200, 400)
         
         # Soil type adjustments
-        if crop in ['Cotton', 'Groundnut']:
-            df.at[idx, 'Soil Type'] = 'Black Cotton'
-        elif crop in ['Rice']:
-            df.at[idx, 'Soil Type'] = random.choice(['Clayey', 'Loamy'])
+        if crop in ['Jute']:
+            df.at[idx, 'Soil Type'] = 'Alluvial'
+        elif crop in ['Tea']:
+            df.at[idx, 'Soil Type'] = 'Loamy'
+        elif crop in ['Rice (Boro)']:
+            df.at[idx, 'Soil Type'] = random.choice(['Alluvial', 'Clayey'])
         
         # Season adjustments
-        if crop in ['Rice', 'Maize', 'Cotton', 'Groundnut']:
-            df.at[idx, 'Season'] = 'Kharif (June-Oct)'
-        elif crop in ['Wheat', 'Barley', 'Chickpea']:
-            df.at[idx, 'Season'] = 'Rabi (Oct-Mar)'
-        elif crop in ['Watermelon', 'Cucumber']:
-            df.at[idx, 'Season'] = 'Zaid (Mar-Jun)'
+        if crop in ['Rice (Aman)', 'Jute']:
+            df.at[idx, 'Season'] = 'Kharif-2 (Jul-Oct)'
+        elif crop in ['Rice (Boro)', 'Wheat', 'Mustard', 'Potato']:
+            df.at[idx, 'Season'] = 'Rabi (Nov-Feb)'
+        elif crop in ['Rice (Aus)']:
+            df.at[idx, 'Season'] = 'Kharif-1 (Mar-Jun)'
     
-    # Add profit estimates (in INR per acre)
+    # Add profit estimates (in BDT per acre)
     profit_ranges = {
-        'Rice': (25000, 50000),
-        'Maize': (20000, 45000),
-        'Cotton': (30000, 70000),
-        'Groundnut': (25000, 55000),
-        'Red Gram (Toor Dal)': (28000, 60000),
-        'Green Gram (Moong Dal)': (22000, 50000),
-        'Black Gram (Urad Dal)': (24000, 52000),
-        'Sunflower': (18000, 40000),
-        'Sugarcane': (35000, 75000),
-        'Turmeric': (40000, 90000),
-        'Chilli': (50000, 120000),
-        'Tomato': (30000, 80000),
-        'Onion': (25000, 65000),
-        'Mango': (60000, 150000),
-        'Banana': (50000, 120000),
-        'Coconut': (40000, 100000),
-        'Soybean': (22000, 48000),
-        'Jowar (Sorghum)': (18000, 40000),
-        'Bajra (Pearl Millet)': (15000, 35000)
+        'Rice (Aman)': (30000, 60000),
+        'Rice (Boro)': (35000, 70000),
+        'Rice (Aus)': (25000, 50000),
+        'Jute': (40000, 80000),
+        'Wheat': (25000, 50000),
+        'Maize': (30000, 60000),
+        'Potato': (50000, 100000),
+        'Sugarcane': (60000, 120000),
+        'Pulses (Mungbean)': (20000, 45000),
+        'Pulses (Lentil)': (22000, 48000),
+        'Mustard': (25000, 55000),
+        'Sesame': (18000, 40000),
+        'Sunflower': (20000, 45000),
+        'Tea': (80000, 150000),
+        'Mango': (100000, 250000),
+        'Banana': (80000, 180000),
+        'Jackfruit': (70000, 150000),
+        'Litchi': (90000, 200000),
+        'Pineapple': (60000, 120000),
+        'Vegetables': (50000, 150000)
     }
     
-    df['Profit (INR/acre)'] = df['Crop'].apply(lambda x: random.randint(*profit_ranges[x]))
+    df['Profit (BDT/acre)'] = df['Crop'].apply(lambda x: random.randint(*profit_ranges[x]))
     
     return df
 
@@ -98,7 +102,7 @@ def generate_synthetic_dataset(num_samples=5000):
 df = generate_synthetic_dataset(10000)
 
 # Prepare data for ML model
-X = df.drop(['Crop', 'Profit (INR/acre)'], axis=1)
+X = df.drop(['Crop', 'Profit (BDT/acre)'], axis=1)
 X = pd.get_dummies(X)  # Convert categorical variables to dummy variables
 y = df['Crop']
 
@@ -111,47 +115,53 @@ model.fit(X_train, y_train)
 
 # Crop precautions information
 precautions_db = {
-    'Rice': [
-        "Maintain proper water level (5-10 cm) in the field",
-        "Control weeds through manual weeding or herbicides",
-        "Use balanced fertilizers (N:P:K = 100:50:50 kg/ha)",
-        "Watch for pests like stem borers and leaf folders"
+    'Rice (Aman)': [
+        "Transplant 25-30 day old seedlings",
+        "Maintain 2-3 cm standing water during initial stage",
+        "Apply 60-80 kg N, 15-20 kg P, and 30-40 kg K per hectare",
+        "Control stem borer with proper insecticides"
     ],
-    'Maize': [
-        "Ensure proper spacing (60x20 cm)",
-        "Apply fertilizers in split doses",
-        "Control weeds during first 30-40 days",
-        "Watch for fall armyworm and use pheromone traps"
+    'Rice (Boro)': [
+        "Ensure irrigation availability as it's dry season rice",
+        "Use cold-tolerant varieties in northern regions",
+        "Apply split doses of nitrogen fertilizer",
+        "Control rats and birds during ripening stage"
+    ],
+    'Jute': [
+        "Sow in well-prepared land with proper moisture",
+        "Retting should be done in clean water for quality fiber",
+        "Apply 40-60 kg N, 20-30 kg P, and 20-30 kg K per hectare",
+        "Control jute hairy caterpillar with proper measures"
     ],
-    'Cotton': [
-        "Use drip irrigation for water efficiency",
-        "Monitor for pink bollworm regularly",
-        "Practice crop rotation to prevent pest buildup",
-        "Use recommended spacing (90x60 cm)"
+    'Wheat': [
+        "Sow in rows with 20 cm spacing",
+        "Apply irrigation at crown root initiation and flowering stages",
+        "Use disease-resistant varieties to combat rust",
+        "Harvest when moisture content is 20-25%"
     ],
-    'Groundnut': [
-        "Ensure well-drained soil to prevent fungal diseases",
-        "Apply gypsum at flowering stage (500 kg/ha)",
-        "Control weeds during first 45 days",
-        "Harvest at proper maturity to avoid pod loss"
+    'Maize': [
+        "Sow in rows with 60 cm row to row distance",
+        "Apply 150-180 kg N, 35-40 kg P, and 60-70 kg K per hectare",
+        "Control fall armyworm with integrated pest management",
+        "Harvest when kernels have 20-25% moisture"
     ],
-    'Red Gram (Toor Dal)': [
-        "Sow in rows with 45 cm spacing",
-        "Treat seeds with rhizobium culture",
-        "Provide protective irrigation during flowering",
-        "Watch for pod borer and apply neem oil"
+    'Potato': [
+        "Use disease-free seed tubers",
+        "Apply irrigation at critical growth stages",
+        "Control late blight with fungicides",
+        "Harvest when vines dry up"
     ],
-    'Tomato': [
-        "Use staking for better fruit quality",
-        "Practice crop rotation to avoid soil diseases",
-        "Monitor for fruit borer and whitefly",
-        "Harvest at breaker stage for longer shelf life"
+    'Tea': [
+        "Prune bushes regularly for new flush",
+        "Apply balanced fertilizer with zinc and magnesium",
+        "Control red spider mite with acaricides",
+        "Pluck two leaves and a bud for quality"
     ],
-    'Chilli': [
-        "Raise seedlings in nursery for 35-40 days",
-        "Mulch to conserve soil moisture",
-        "Monitor for thrips and mites regularly",
-        "Harvest at regular intervals for higher yield"
+    'Mango': [
+        "Prune for proper canopy management",
+        "Control mango hopper during flowering",
+        "Apply irrigation during fruit development",
+        "Harvest when shoulders develop"
     ],
     # Default precautions for other crops
     'Default': [
@@ -170,16 +180,16 @@ def get_precautions(crop, temperature, rainfall, humidity, soil_type):
     if temperature > 35:
         precautions.append("Provide mulch to reduce soil temperature")
         precautions.append("Increase irrigation frequency during hot days")
-    if rainfall < 50:
-        precautions.append("Use water conservation techniques like drip irrigation")
-    if humidity > 80:
+    if rainfall > 300:
+        precautions.append("Ensure proper drainage to prevent waterlogging")
+    if humidity > 85:
         precautions.append("Watch for fungal diseases and apply preventive sprays")
     
     # Add soil-specific precautions
-    if soil_type == 'Black Cotton':
-        precautions.append("Practice deep ploughing to break soil hardpans")
-    elif soil_type == 'Sandy Loam':
-        precautions.append("Apply organic manure to improve water retention")
+    if soil_type == 'Alluvial':
+        precautions.append("Apply organic matter to maintain soil fertility")
+    elif soil_type == 'Peaty':
+        precautions.append("Apply lime to reduce acidity if needed")
     
     return precautions[:5]  # Return top 5 precautions
 
@@ -198,11 +208,11 @@ def predict_crop(temperature, rainfall, humidity, soil_ph, soil_type, nitrogen,
     }
     
     # Add soil type columns (one-hot encoding)
-    for st in ['Black Cotton', 'Red Sandy', 'Clayey', 'Loamy', 'Sandy Loam']:
+    for st in ['Alluvial', 'Loamy', 'Clayey', 'Peaty', 'Sandy']:
         input_data[f'Soil Type_{st}'] = [1 if soil_type == st else 0]
     
     # Add season columns (one-hot encoding)
-    for s in ['Kharif (June-Oct)', 'Rabi (Oct-Mar)', 'Zaid (Mar-Jun)', 'Whole Year']:
+    for s in ['Kharif-1 (Mar-Jun)', 'Kharif-2 (Jul-Oct)', 'Rabi (Nov-Feb)', 'Whole Year']:
         input_data[f'Season_{s}'] = [1 if season == s else 0]
     
     input_df = pd.DataFrame(input_data)
@@ -214,7 +224,7 @@ def predict_crop(temperature, rainfall, humidity, soil_ph, soil_type, nitrogen,
     crop = model.predict(input_df)[0]
     
     # Get profit range
-    profit = df[df['Crop'] == crop]['Profit (INR/acre)'].mean()
+    profit = df[df['Crop'] == crop]['Profit (BDT/acre)'].mean()
     
     # Get precautions
     precautions = get_precautions(crop, temperature, rainfall, humidity, soil_type)
@@ -227,7 +237,7 @@ def predict_crop(temperature, rainfall, humidity, soil_ph, soil_type, nitrogen,
     # Prepare output
     output = {
         "Recommended Crop": crop,
-        "Expected Profit (INR per acre)": f"₹{int(profit):,}",
+        "Expected Profit (BDT per acre)": f"৳{int(profit):,}",
         "Top Precautions": precautions,
         "Alternative Crops": similar_crops,
         "Best Season": season
@@ -364,7 +374,7 @@ custom_css = """
 # Function to format outputs
 def format_outputs(output):
     crop_md = f"**Recommended Crop:** {output['Recommended Crop']}"
-    profit_md = f"**Expected Profit (INR per acre):** {output['Expected Profit (INR per acre)']}"
+    profit_md = f"**Expected Profit (BDT per acre):** {output['Expected Profit (BDT per acre)']}"
     season_md = f"**Best Season:** {output['Best Season']}"
     alt_md = f"**Alternative Crops:** {', '.join(output['Alternative Crops'])}"
     
@@ -381,98 +391,98 @@ with gr.Blocks(css=custom_css) as demo:
     with gr.Column(elem_classes="agrismart-container"):
         with gr.Row(elem_classes="agrismart-header"):
             gr.Markdown("""
-            # 🌱 Crop Vision : Smart Crop Advisor for Farmers
-            ### Get personalized crop recommendations based on your farm conditions
+            # 🌱 বাংলাদেশের জন্য ফসল সুপারিশকারী
+            ### আপনার জমির অবস্থা অনুযায়ী উপযুক্ত ফসলের পরামর্শ পান
             """)
         
         with gr.Row():
             with gr.Column(elem_classes="agrismart-input"):
-                gr.Markdown("### 🌦️ Enter Your Farm Conditions", elem_classes="agrismart-label")
+                gr.Markdown("### 🌦️ আপনার জমির তথ্য দিন", elem_classes="agrismart-label")
                 
                 with gr.Row():
-                    temperature = gr.Slider(10, 60, label="1. Temperature (How hot is your area?)", 
-                                          info="Measure the air temperature in shade (°C)", 
+                    temperature = gr.Slider(10, 40, label="1. তাপমাত্রা (°C)", 
+                                          info="ছায়াযুক্ত স্থানে বায়ুর তাপমাত্রা মাপুন", 
                                           elem_classes="agrismart-slider")
-                    rainfall = gr.Slider(0, 300, label="2. Rainfall (How much rain your area gets?)", 
-                                       info="Annual rainfall in your area (mm)", 
+                    rainfall = gr.Slider(100, 400, label="2. বৃষ্টিপাত (mm)", 
+                                       info="আপনার এলাকার বার্ষিক বৃষ্টিপাতের পরিমাণ", 
                                        elem_classes="agrismart-slider")
                 
                 with gr.Row():
-                    humidity = gr.Slider(20, 100, label="3. Humidity (How moist is your air?)", 
-                                       info="Relative humidity percentage (%)", 
+                    humidity = gr.Slider(60, 100, label="3. আর্দ্রতা (%)", 
+                                       info="বাতাসে আর্দ্রতার পরিমাণ", 
                                        elem_classes="agrismart-slider")
-                    soil_ph = gr.Slider(4, 10, label="4. Soil pH (Is your soil acidic or alkaline?)", 
-                                      info="7 is neutral, below 7 is acidic, above 7 is alkaline", 
+                    soil_ph = gr.Slider(5, 8.5, label="4. মাটির pH মান", 
+                                      info="৭ হলো নিরপেক্ষ, ৭ এর নিচে অম্লীয়, ৭ এর উপরে ক্ষারীয়", 
                                       elem_classes="agrismart-slider")
                 
                 with gr.Row():
                     soil_type = gr.Dropdown(
-                        ["Black Cotton", "Red Sandy", "Clayey", "Loamy", "Sandy Loam"], 
-                        label="5. Soil Type (What type of soil do you have?)",
-                        info="Black Cotton is common in Andhra/Telangana",
+                        ["Alluvial", "Loamy", "Clayey", "Peaty", "Sandy"], 
+                        label="5. মাটির ধরন",
+                        info="বাংলাদেশের সাধারণ মাটির ধরন",
                         elem_classes="agrismart-dropdown"
                     )
                     season = gr.Dropdown(
-                        ["Kharif (June-Oct)", "Rabi (Oct-Mar)", "Zaid (Mar-Jun)", "Whole Year"], 
-                        label="6. Season (When will you cultivate?)",
+                        ["Kharif-1 (Mar-Jun)", "Kharif-2 (Jul-Oct)", "Rabi (Nov-Feb)", "Whole Year"], 
+                        label="6. মৌসুম",
                         elem_classes="agrismart-dropdown"
                     )
                 
                 with gr.Row():
-                    nitrogen = gr.Slider(0, 150, label="7. Nitrogen Level (N) in soil", 
-                                        info="Essential for leaf growth (kg/ha)", 
+                    nitrogen = gr.Slider(0, 150, label="7. মাটিতে নাইট্রোজেনের পরিমাণ (N)", 
+                                        info="গাছের পাতার বৃদ্ধির জন্য প্রয়োজনীয় (kg/ha)", 
                                         elem_classes="agrismart-slider")
-                    phosphorus = gr.Slider(0, 100, label="8. Phosphorus Level (P) in soil", 
-                                         info="Important for root development (kg/ha)", 
+                    phosphorus = gr.Slider(0, 100, label="8. মাটিতে ফসফরাসের পরিমাণ (P)", 
+                                         info="শিকড়ের উন্নতির জন্য গুরুত্বপূর্ণ (kg/ha)", 
                                          elem_classes="agrismart-slider")
-                    potassium = gr.Slider(0, 200, label="9. Potassium Level (K) in soil", 
-                                        info="Helps in fruit quality (kg/ha)", 
+                    potassium = gr.Slider(0, 200, label="9. মাটিতে পটাশিয়ামের পরিমাণ (K)", 
+                                        info="ফলের গুণগত মানের জন্য সহায়ক (kg/ha)", 
                                         elem_classes="agrismart-slider")
                 
-                submit_btn = gr.Button("Get Crop Recommendation", elem_classes="agrismart-button")
+                submit_btn = gr.Button("ফসলের সুপারিশ পান", elem_classes="agrismart-button")
             
             with gr.Column(elem_classes="agrismart-output"):
-                gr.Markdown("### 📊 Recommended Crop Details", elem_classes="agrismart-label")
+                gr.Markdown("### 📊 সুপারিশকৃত ফসলের বিবরণ", elem_classes="agrismart-label")
                 
                 with gr.Column(elem_classes="agrismart-result-card"):
-                    crop = gr.Markdown("**Recommended Crop:** ", elem_classes="agrismart-result-value")
-                    profit = gr.Markdown("**Expected Profit (INR per acre):** ", elem_classes="agrismart-result-value")
-                    season_out = gr.Markdown("**Best Season:** ", elem_classes="agrismart-result-value")
-                    alternatives = gr.Markdown("**Alternative Crops:** ", elem_classes="agrismart-result-value")
+                    crop = gr.Markdown("**সুপারিশকৃত ফসল:** ", elem_classes="agrismart-result-value")
+                    profit = gr.Markdown("**আনুমানিক লাভ (প্রতি একরে):** ", elem_classes="agrismart-result-value")
+                    season_out = gr.Markdown("**উপযুক্ত মৌসুম:** ", elem_classes="agrismart-result-value")
+                    alternatives = gr.Markdown("**বিকল্প ফসল:** ", elem_classes="agrismart-result-value")
                 
-                gr.Markdown("### 🛡️ Top Precautions", elem_classes="agrismart-result-title")
+                gr.Markdown("### 🛡️ প্রয়োজনীয় সতর্কতা", elem_classes="agrismart-result-title")
                 precautions = gr.HTML("""
                 <ul class="agrismart-precautions">
-                    <li>Enter your farm details and click the button to get recommendations</li>
+                    <li>আপনার জমির তথ্য প্রদান করে বাটনে ক্লিক করুন</li>
                 </ul>
                 """)
                 
-                # Example images (would need actual images in production)
-                gr.Markdown("### 🌾 Common Crops in Andhra/Telangana")
+                # Example images of common Bangladeshi crops
+                gr.Markdown("### 🌾 বাংলাদেশের প্রধান ফসল")
                 gr.HTML("""
                 <div style="display: flex; flex-wrap: wrap; gap: 10px; justify-content: center;">
                     <div style="text-align: center;">
                         <div style="background: #e3f2fd; padding: 10px; border-radius: 10px; width: 100px;">
                             <div style="font-size: 40px;">🌾</div>
-                            <div>Rice</div>
+                            <div>ধান</div>
                         </div>
                     </div>
                     <div style="text-align: center;">
                         <div style="background: #e8f5e9; padding: 10px; border-radius: 10px; width: 100px;">
-                            <div style="font-size: 40px;">🌽</div>
-                            <div>Maize</div>
+                            <div style="font-size: 40px;">🧶</div>
+                            <div>পাট</div>
                         </div>
                     </div>
                     <div style="text-align: center;">
                         <div style="background: #fff3e0; padding: 10px; border-radius: 10px; width: 100px;">
-                            <div style="font-size: 40px;">🧶</div>
-                            <div>Cotton</div>
+                            <div style="font-size: 40px;">🥔</div>
+                            <div>আলু</div>
                         </div>
                     </div>
                     <div style="text-align: center;">
                         <div style="background: #f3e5f5; padding: 10px; border-radius: 10px; width: 100px;">
-                            <div style="font-size: 40px;">🥜</div>
-                            <div>Groundnut</div>
+                            <div style="font-size: 40px;">🍌</div>
+                            <div>কলা</div>
                         </div>
                     </div>
                 </div>