Update app.py

app.py

@@ -1,87 +1,84 @@
 import gradio as gr
 import pandas as pd
+from mock_models.crop_model import MockCropModel
+from mock_models.chat_engine import AgriculturalChatEngine
 
-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)
+# Initialize mock models
+crop_model = MockCropModel()
+chat_engine = AgriculturalChatEngine()
 
-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>
-    """)
+# ----------------------
+# Core functionality
+# ----------------------
+def get_crop_recommendation(soil_type, climate, file_input):
+    """Process inputs and generate recommendations"""
+    df = crop_model.predict(soil_type, climate)
     
-    gr.Markdown("### 🌾 Agricultural Intelligence System")
+    if file_input:
+        try:
+            uploaded_data = pd.read_csv(file_input.name)
+            df['Uploaded Data'] = uploaded_data.mean().to_dict()
+        except:
+            pass
+            
+    return df
+
+def handle_chat(query, history):
+    """Process chat queries"""
+    response = chat_engine.generate_response(query)
+    history.append((query, response))
+    return history, ""
+
+# ----------------------
+# UI Components
+# ----------------------
+with gr.Blocks(theme=gr.themes.Soft(), title="CropSeek LLM") as demo:
+    # Header Section
+    gr.Markdown("# 🌐 CropSeek LLM")
+    gr.Markdown("## AI-Driven Agricultural Decision Support 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]
-        )
+    # Chat Interface
+    with gr.Tab("🗣️ Virtual Agronomist"):
+        chatbot = gr.Chatbot(height=400, label="Farm Assistant")
+        chat_input = gr.Textbox(placeholder="Ask about crops, pests, or farming practices...")
+        chat_input.submit(handle_chat, [chat_input, chatbot], [chatbot, chat_input])
     
-    with gr.Tab("📈 Yield Optimization Advisor"):
+    # Data Analysis Interface
+    with gr.Tab("📊 Crop Advisor"):
         with gr.Row():
-            soil_dd = gr.Dropdown(
-                ["Loamy", "Clay", "Sandy"], 
-                label="Soil Composition",
-                info="Select predominant soil type"
+            soil_type = gr.Dropdown(
+                ["Loamy", "Clay", "Sandy"],
+                label="Soil Type",
+                value="Loamy"
             )
-            climate_dd = gr.Dropdown(
-                ["Tropical", "Temperate", "Arid"], 
-                label="Climate Profile",
-                info="Select regional climate pattern"
+            climate_zone = gr.Dropdown(
+                ["Tropical", "Temperate", "Arid"],
+                label="Climate Zone",
+                value="Tropical"
             )
-        farm_data = gr.File(
-            label="Upload Field Sensor Data (CSV)",
-            file_types=[".csv"]
-        )
-        simulate_btn = gr.Button("🚜 Generate Cultivation Plan", variant="primary")
+        upload_btn = gr.UploadButton("📁 Upload Soil Analysis (CSV)", file_types=[".csv"])
+        analyze_btn = gr.Button("🚜 Analyze Conditions", variant="primary")
         results = gr.Dataframe(
-            headers=["Parameter", "Value", "Recommendation"],
+            headers=["Crop", "Confidence", "Soil Type", "Climate Zone"],
             interactive=False,
-            wrap=True,
-            datatype=["str", "number", "str"]
+            wrap=True
         )
         
-        simulate_btn.click(
+        analyze_btn.click(
             fn=get_crop_recommendation,
-            inputs=[soil_dd, climate_dd, farm_data],
+            inputs=[soil_type, climate_zone, upload_btn],
             outputs=results
         )
+    
+    # Footer
+    gr.Markdown("---")
+    with gr.Row():
+        gr.Markdown("**System Status:** ✅ Operational")
+        gr.Markdown("**Version:** 1.0.0")
+        gr.Markdown("**Data Updated:** 2023-08-15")
 
-   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()
+# ----------------------
+# Launch Application
+# ----------------------
+if __name__ == "__main__":
+    demo.launch()