app.py

import gradio as gr
import pandas as pd

def get_crop_recommendation(soil_type, climate, history):
    # Replace with your actual model inference logic
    recommendations = {
        "soil": soil_type,
        "climate": climate,
        "recommended_crops": ["Pepper", "Tomato", "Chilli"],
        "confidence": [0.85, 0.76, 0.68]
    }
    return pd.DataFrame(recommendations)

def analyze_conditions(query, history):
    # Condition analysis handler (connect to your LLM)
    response = f"🌱 CropAI Analysis: Optimal cultivation patterns suggest {get_crop_recommendation('Loam', 'Tropical', '').iloc[0]['recommended_crops'][0]} for these conditions."
    return response

with gr.Blocks(theme=gr.themes.Soft(), title="CropSeek-LLM") as demo:
    gr.HTML("""
    <div style="text-align:center"; background: linear-gradient(to right, #2c5f2d, #97bc62); padding: 20px; border-radius: 10px; box-shadow: 0 4px 6px rgba(0,0,0,0.1);">
        <img src="https://huggingface.co/spaces/DARJYO/CropSeek-LLM/resolve/main/assets/logo.png" 
             style="height:80px; margin-bottom:20px;">
    </div>
    """)
    
    gr.Markdown("### 🌾 Agricultural Intelligence System")
    
    with gr.Tab("🌱 Field Analysis Console"):
        with gr.Row():
            with gr.Column(scale=2):
                analysis_log = gr.Chatbot(
                    label="Crop Diagnosis History",
                    avatar_images=("🧑🌾", "🤖"),
                    height=400
                )
            with gr.Column(scale=1):
                field_input = gr.Textbox(
                    label="Describe Soil & Climate Conditions:",
                    placeholder="e.g. 'Clay soil in tropical climate with moderate rainfall...'"
                )
                analyze_btn = gr.Button("🌦️ Analyze Agricultural Patterns", variant="primary")
        
        analyze_btn.click(
            analyze_conditions, 
            [field_input, analysis_log], 
            [field_input, analysis_log]
        )
    
    with gr.Tab("📈 Yield Optimization Advisor"):
        with gr.Row():
            soil_dd = gr.Dropdown(
                ["Loamy", "Clay", "Sandy"], 
                label="Soil Composition",
                info="Select predominant soil type"
            )
            climate_dd = gr.Dropdown(
                ["Tropical", "Temperate", "Arid"], 
                label="Climate Profile",
                info="Select regional climate pattern"
            )
        farm_data = gr.File(
            label="Upload Field Sensor Data (CSV)",
            file_types=[".csv"]
        )
        simulate_btn = gr.Button("🚜 Generate Cultivation Plan", variant="primary")
        results = gr.Dataframe(
            headers=["Parameter", "Value", "Recommendation"],
            interactive=False,
            wrap=True,
            datatype=["str", "number", "str"]
        )
        
        simulate_btn.click(
            fn=get_crop_recommendation,
            inputs=[soil_dd, climate_dd, farm_data],
            outputs=results
        )

   gr.Markdown("""
    <div style="text-align: center; padding: 15px; background-color: #f8f9fa; border-radius: 8px; margin-top: 20px;">
        <small>© 2025 DARJYO/CropSeek-LLM</small>
    </div>
    """)


demo.launch()