Update app.py

app.py

@@ -1,63 +1,158 @@
 import gradio as gr
 
-# Dummy model functions for each version
-def model_v1(image):
-    return "Model V1: Detected disease - [Example Disease]"
+# ===== Dummy Model & Utility Functions =====
+# Model A (for versions 1.x)
+def model_A(image):
+    return "Model A predicts: [Example Disease A]"
 
-def model_v2(image):
-    return "Model V2: Detected disease after validation - [Example Disease]"
+# Model B (for versions 2.x)
+def model_B(image):
+    return "Model B predicts: [Example Disease B]"
 
-def model_v3(image):
-    return "Model V3: Detected disease with advanced analysis - [Example Disease]"
+# Dummy classifier: Checks if image is a tomato leaf.
+def classifier(image):
+    # Replace with actual classification logic.
+    return "Tomato Leaf"
 
-# Main function to select the proper model based on the version choice
-def process_image(image, version):
+# Dummy AI assistants for each model type.
+def ai_assistant_v1(image, prediction):
+    return "Advice from AI Assistant v1: Ensure proper irrigation and pest monitoring."
+
+def ai_assistant_v2(image, prediction):
+    return "Advice from AI Assistant v2: Consider organic pesticides and crop rotation."
+
+# ===== Process Function for Versioned Logic =====
+def process_version(image, version):
     if image is None:
         return "No image provided."
-    model_map = {
-        "Version 1": model_v1,
-        "Version 2": model_v2,
-        "Version 3": model_v3
-    }
-    return model_map.get(version, lambda x: "Invalid version selected.")(image)
+    
+    # --- Version 1.x (Model A) ---
+    if version == "1.1":
+        # Model A only + link to notebook
+        result = model_A(image)
+        return f"Model A Prediction: {result}\n\n[View Model A Training Notebook](https://huggingface.co/your-model-a-notebook)"
+    
+    elif version == "1.2":
+        # Model A + AI Assistant
+        result = model_A(image)
+        advice = ai_assistant_v1(image, result)
+        return f"Model A Prediction: {result}\nAdvice: {advice}"
+    
+    elif version == "1.3":
+        # Classifier + Model A + AI Assistant
+        cls_result = classifier(image)
+        if cls_result != "Tomato Leaf":
+            return "Classifier: The image is not a tomato leaf. Please try again."
+        result = model_A(image)
+        advice = ai_assistant_v1(image, result)
+        return (f"Classifier: {cls_result}\nModel A Prediction: {result}\nAdvice: {advice}\n\n"
+                f"[View Model A & Classifier Training Notebook](https://huggingface.co/your-model-a-classifier-notebook)")
+    
+    # --- Version 2.x (Model B) ---
+    elif version == "2.1":
+        # Model B only + link to notebook
+        result = model_B(image)
+        return f"Model B Prediction: {result}\n\n[View Model B Training Notebook](https://huggingface.co/your-model-b-notebook)"
+    
+    elif version == "2.2":
+        # Model B + AI Assistant
+        result = model_B(image)
+        advice = ai_assistant_v2(image, result)
+        return f"Model B Prediction: {result}\nAdvice: {advice}"
+    
+    elif version == "2.3":
+        # Classifier + Model B + AI Assistant
+        cls_result = classifier(image)
+        if cls_result != "Tomato Leaf":
+            return "Classifier: The image is not a tomato leaf. Please try again."
+        result = model_B(image)
+        advice = ai_assistant_v2(image, result)
+        return (f"Classifier: {cls_result}\nModel B Prediction: {result}\nAdvice: {advice}\n\n"
+                f"[View Model B & Classifier Training Notebook](https://huggingface.co/your-model-b-classifier-notebook)")
+    else:
+        return "Invalid version selected."
 
-# Helper function to select between uploaded image and camera input
+# ===== Helper to Select Between Uploaded & Camera Image =====
 def combine_images(uploaded, camera):
     return camera if camera is not None else uploaded
 
-# Define the Gradio Blocks interface
+# ===== CSS for Theme Switching =====
+light_css = """
+<style>
+  body { background-color: white; color: black; }
+  .gr-button { background-color: #4CAF50; color: white; }
+</style>
+"""
+
+dark_css = """
+<style>
+  body { background-color: #333; color: white; }
+  .gr-button { background-color: #555; color: white; }
+</style>
+"""
+
+def update_css(theme):
+    if theme == "Dark":
+        return dark_css
+    else:
+        return light_css
+
+# ===== Gradio Interface =====
 with gr.Blocks() as demo:
+    # Hidden element for injecting CSS (initially Light theme)
+    css_injector = gr.HTML(update_css("Light"))
+    
     gr.Markdown("# 🌿 FarmVi8ion – AI-powered Crop Monitoring")
-    gr.Markdown("### Detect tomato leaf diseases using AI-powered models.")
-
+    gr.Markdown("Detect tomato leaf diseases and get AI-driven advice for your crops.")
+    
     with gr.Row():
+        # ----- Left Column (≈30%) -----
         with gr.Column(scale=1):
-            version = gr.Radio(
-                ["Version 1", "Version 2", "Version 3"],
-                label="Select Model Version",
-                value="Version 1"
+            version = gr.Dropdown(
+                choices=["1.1", "1.2", "1.3", "2.1", "2.2", "2.3"],
+                label="Select Version",
+                value="1.1",
+                info="Versions 1.x use Model A; Versions 2.x use Model B."
             )
             theme_choice = gr.Radio(
-                ["Light", "Dark"],
+                choices=["Light", "Dark"],
                 label="Select Theme",
                 value="Light"
             )
-
+            gr.Markdown("### Notebook Links")
+            gr.Markdown(
+                """
+                **For Model A:**
+                - Model A Only: [Training Notebook](https://huggingface.co/your-model-a-notebook)
+                - Model A & Classifier: [Training Notebook](https://huggingface.co/your-model-a-classifier-notebook)
+                
+                **For Model B:**
+                - Model B Only: [Training Notebook](https://huggingface.co/your-model-b-notebook)
+                - Model B & Classifier: [Training Notebook](https://huggingface.co/your-model-b-classifier-notebook)
+                """
+            )
+        
+        # ----- Right Column (≈70%) -----
         with gr.Column(scale=2):
             image_input = gr.Image(
-                label="📂 Upload Tomato Leaf Image",
-                type="pil"
+                label="📂 Upload Tomato Leaf Image", 
+                type="pil",
+                tool="editor"  # enables basic image editing (crop, zoom, rotate)
             )
             camera_input = gr.Image(
-                label="📸 Use Camera",
+                label="📸 Use Camera (Live Preview)", 
                 type="pil"
             )
             submit = gr.Button("🔍 Analyze")
-
-    output = gr.Textbox(label="📝 Diagnosis Result")
-
+    
+    output = gr.Textbox(label="📝 Diagnosis & Advice", lines=8)
+    
+    # Update CSS dynamically based on theme choice
+    theme_choice.change(fn=update_css, inputs=theme_choice, outputs=css_injector)
+    
+    # Process image on submit (combining uploaded & camera image)
     submit.click(
-        fn=lambda uploaded, camera, ver: process_image(combine_images(uploaded, camera), ver),
+        fn=lambda uploaded, camera, ver: process_version(combine_images(uploaded, camera), ver),
         inputs=[image_input, camera_input, version],
         outputs=output
     )