Update app.py

app.py

@@ -32,14 +32,14 @@ def analyze_location(location_name):
         temp_score = np.clip((historical['temperature'].mean() - 15) / (30 - 15), 0, 1)
         humidity_score = np.clip((historical['humidity'].mean() - 50) / (80 - 50), 0, 1)
         rainfall_score = np.clip(historical['rainfall'].mean() / 5, 0, 1)
-        ndvi_score = np.clip((historical['estimated_ndvi'].mean() + 1) / 2, 0, 1)  # Convert from -1:1 to 0:1
+        ndvi_score = np.clip((historical['estimated_ndvi'].mean() + 1) / 2, 0, 1)
         
         # Get trends analysis
         trends = analyzer.analyze_trends(df)
         if trends is None:
             return None, None, "Error calculating trends.", None
         
-        # Calculate overall score
+        # Calculate overall score with weights
         weights = {
             'temperature': 0.3,
             'humidity': 0.2,
@@ -72,11 +72,11 @@ def analyze_location(location_name):
         Humidity: {historical['humidity'].mean():.1f}% (±{historical['humidity'].std():.1f}%)
         Rainfall: {historical['rainfall'].mean():.1f}mm/day (±{historical['rainfall'].std():.1f}mm)
         
-        📈 Weather Trends (Change per day):
-        Temperature: {trends['historical']['temperature']['trend']:.3f}°C/day (R²={trends['historical']['temperature']['trend_r2']:.2f})
-        Humidity: {trends['historical']['humidity']['trend']:.3f}%/day (R²={trends['historical']['humidity']['trend_r2']:.2f})
-        Rainfall: {trends['historical']['rainfall']['trend']:.3f}mm/day (R²={trends['historical']['rainfall']['trend_r2']:.2f})
-        Vegetation: {trends['historical']['ndvi']['trend']:.3f} NDVI/day (R²={trends['historical']['ndvi']['trend_r2']:.2f})
+        🌿 Vegetation Analysis:
+        Current NDVI: {historical['estimated_ndvi'].mean():.2f}
+        Minimum NDVI: {historical['estimated_ndvi'].min():.2f}
+        Maximum NDVI: {historical['estimated_ndvi'].max():.2f}
+        Vegetation Status: {get_vegetation_status(historical['estimated_ndvi'].mean())}
         
         🔮 Forecast Analysis (Next 90 Days):
         Temperature: {forecast['temperature'].mean():.1f}°C (±{forecast['temperature'].std():.1f}°C)
@@ -103,13 +103,32 @@ def analyze_location(location_name):
         • Long-term Outlook: {get_long_term_outlook(trends)}
         """
         
-        return time_series_plot, gauge_plot, analysis_text, location_map
+        return location_map, analysis_text, time_series_plot, gauge_plot
         
     except Exception as e:
         error_message = f"An error occurred: {str(e)}"
         print(f"Error details: {e}")
         return None, None, error_message, None
 
+def get_vegetation_status(ndvi):
+    """Get detailed vegetation status based on NDVI value"""
+    if ndvi < 0:
+        return "Very Low - Bare soil or water bodies"
+    elif ndvi < 0.1:
+        return "Low - Very sparse vegetation"
+    elif ndvi < 0.2:
+        return "Sparse - Stressed vegetation"
+    elif ndvi < 0.3:
+        return "Moderate - Typical agricultural land"
+    elif ndvi < 0.4:
+        return "Good - Healthy vegetation"
+    elif ndvi < 0.5:
+        return "High - Very healthy vegetation"
+    elif ndvi < 0.6:
+        return "Very High - Dense vegetation"
+    else:
+        return "Dense - Very dense, healthy vegetation"
+
 def get_recommendations(score, ndvi):
     """Get detailed recommendations based on scores"""
     if score >= 0.8 and ndvi >= 0.6:
@@ -195,28 +214,62 @@ def get_long_term_outlook(trends):
         return "Deteriorating conditions"
     return "Mixed conditions - monitor closely"
 
-# Create Gradio interface
-demo = gr.Interface(
-    fn=analyze_location,
-    inputs=[
-        gr.Textbox(label="Enter Location", placeholder="e.g., Tabora, Tanzania")
-    ],
-    outputs=[
-        gr.Plot(label="Weather Analysis"),
-        gr.Plot(label="Growing Conditions Score"),
-        gr.Textbox(label="Analysis Results", lines=25),
-        gr.HTML(label="Location Analysis")
-    ],
-    title="🌱 Agricultural Credit Risk Assessment System",
-    description="Comprehensive Weather, Vegetation, and Credit Scoring Analysis for Tobacco Farming",
-    examples=[
-        ["Tabora, Tanzania"],
-        ["Urambo, Tabora, Tanzania"],
-        ["Sikonge, Tabora, Tanzania"],
-        ["Nzega, Tabora, Tanzania"]
-    ],
-    cache_examples=True
-)
+# Create Gradio interface with enhanced layout
+with gr.Blocks(theme=gr.themes.Base()) as demo:
+    gr.Markdown(
+        """
+        # 🌱 Agricultural Credit Risk Assessment System
+        Comprehensive Weather, Vegetation, and Credit Scoring Analysis for Tobacco Farming
+        """
+    )
+    
+    # Input Section
+    with gr.Row():
+        location_input = gr.Textbox(
+            label="Enter Location",
+            placeholder="e.g., Tabora, Tanzania",
+            scale=4
+        )
+        analyze_button = gr.Button("Analyze", variant="primary", scale=1)
+
+    # Map and Analysis Section
+    with gr.Row():
+        with gr.Column(scale=2):  # Larger map
+            location_map = gr.HTML(label="NDVI Analysis Map")
+        with gr.Column(scale=1):
+            analysis_text = gr.Textbox(
+                label="Analysis Results",
+                lines=25,
+                show_label=False
+            )
+    
+    # Plots Section
+    with gr.Row():
+        weather_plot = gr.Plot(label="Weather Analysis")
+    
+    with gr.Row():
+        score_gauge = gr.Plot(label="Growing Conditions Score")
+    
+    # Examples
+    gr.Examples(
+        examples=[
+            ["Tabora, Tanzania"],
+            ["Urambo, Tabora, Tanzania"],
+            ["Sikonge, Tabora, Tanzania"],
+            ["Nzega, Tabora, Tanzania"]
+        ],
+        inputs=location_input,
+        outputs=[location_map, analysis_text, weather_plot, score_gauge],
+        fn=analyze_location,
+        cache_examples=True
+    )
+    
+    # Set up the click event
+    analyze_button.click(
+        fn=analyze_location,
+        inputs=[location_input],
+        outputs=[location_map, analysis_text, weather_plot, score_gauge]
+    )
 
 # Launch the app
 if __name__ == "__main__":