Update app.py

app.py

@@ -6,7 +6,8 @@ from tensorflow.keras.models import load_model
 from PIL import Image
 
 # ===== Hugging Face API Token =====
-HF_API_TOKEN = os.getenv("HF_API_TOKEN")  # Ensure this is set in your environment
+# Set HF_API_TOKEN in your environment. For local testing, you can create a .env file.
+HF_API_TOKEN = os.getenv("HF_API_TOKEN")  # e.g., "hf_xxx..."
 
 # ===== Load Trained Models =====
 model_a = load_model("Tomato_Leaf_Disease_Model.h5")
@@ -24,27 +25,39 @@ def preprocess_image(image, target_size=(224, 224)):
     img_array = np.expand_dims(img_array, axis=0)
     return img_array
 
+# ===== Disease Label Mappings =====
+# Update these labels to match your model training.
+disease_labels = {
+    0: "No disease",
+    1: "Early Blight",
+    2: "Late Blight",
+    3: "Septoria Leaf Spot",
+    4: "Bacterial Spot",
+    5: "Mosaic Virus"
+}
+
 # ===== Prediction Functions =====
 def predict_model_a(image):
     img = preprocess_image(image)
     pred = model_a.predict(img)
-    # Replace with your own mapping from predictions to disease names.
-    return "Disease A detected" if np.argmax(pred) == 1 else "No disease"
+    predicted_class = np.argmax(pred)
+    return disease_labels.get(predicted_class, "Unknown result")
 
 def predict_model_b(image):
     img = preprocess_image(image)
     pred = model_b.predict(img)
-    return "Disease B detected" if np.argmax(pred) == 1 else "No disease"
+    predicted_class = np.argmax(pred)
+    return disease_labels.get(predicted_class, "Unknown result")
 
 def predict_classifier(image):
     img = preprocess_image(image)
     pred = classifier_model.predict(img)
-    # Assume classifier returns class 1 for a tomato leaf.
+    # Here we assume the classifier returns class 1 for "Tomato Leaf"
     return "Tomato Leaf" if np.argmax(pred) == 1 else "Not Tomato Leaf"
 
 # ===== Hugging Face Inference API Calls =====
 def call_llama2(prompt):
-    """Call the Llama 2-7B Chat model on Hugging Face for conversational advice."""
+    """Call Llama 2-7B Chat model on Hugging Face for conversational advice."""
     headers = {"Authorization": f"Bearer {HF_API_TOKEN}"}
     payload = {"inputs": prompt, "parameters": {"max_new_tokens": 100}}
     url = "https://api-inference.huggingface.co/models/meta-llama/Llama-2-7b-chat-hf"
@@ -75,23 +88,31 @@ def call_openassistant(prompt):
 
 # ===== AI Assistant Functions =====
 def ai_assistant_v1(image, prediction):
-    # Use Llama 2-7B Chat (Model A version)
+    # Use Llama 2-7B Chat (Model A versions)
     if "No disease" in prediction:
-        prompt = ("You are an agricultural advisor. The tomato crop appears healthy. "
-                  "Provide additional preventive tips and good practices for maintaining crop health.")
+        prompt = (
+            "You are an agricultural advisor. The tomato crop appears healthy. "
+            "Provide additional preventive tips and best practices for maintaining crop health."
+        )
     else:
-        prompt = (f"You are an agricultural advisor. A disease has been detected: {prediction}. "
-                  "Provide detailed advice on how to manage and curb this disease, and explain more about it in simple terms.")
+        prompt = (
+            f"You are an agricultural advisor. A disease has been detected: {prediction}. "
+            "Provide detailed advice on how to manage and curb this disease, explaining it in simple terms."
+        )
     return call_llama2(prompt)
 
 def ai_assistant_v2(image, prediction):
-    # Use OpenAssistant (Model B version)
+    # Use OpenAssistant (Model B versions)
     if "No disease" in prediction:
-        prompt = ("You are an agricultural advisor. The tomato crop appears healthy. "
-                  "Offer additional preventive tips and guidelines for maintaining a healthy crop.")
+        prompt = (
+            "You are an agricultural advisor. The tomato crop appears healthy. "
+            "Offer additional preventive tips and guidelines for maintaining a healthy crop."
+        )
     else:
-        prompt = (f"You are an agricultural advisor. A disease has been detected: {prediction}. "
-                  "Provide actionable steps and detailed advice on how to control and manage this disease in tomato crops.")
+        prompt = (
+            f"You are an agricultural advisor. A disease has been detected: {prediction}. "
+            "Provide actionable steps and detailed advice on how to control and manage this disease in tomato crops."
+        )
     return call_openassistant(prompt)
 
 # ===== Process Function Based on Version =====
@@ -115,8 +136,10 @@ def process_version(image, version):
             return "Classifier: The image is not a tomato leaf. Please try again."
         result = predict_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)")
+        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":
@@ -134,8 +157,10 @@ def process_version(image, version):
             return "Classifier: The image is not a tomato leaf. Please try again."
         result = predict_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)")
+        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."
@@ -149,13 +174,15 @@ light_css = """
 <style>
   body { background-color: white; color: black; }
   .gr-button { background-color: #4CAF50; color: white; }
+  .gr-input, .gr-textbox, .gr-dropdown, .gr-radio, .gr-markdown, .gr-container { background-color: white; color: black; }
 </style>
 """
 
 dark_css = """
 <style>
-  body { background-color: #333; color: white; }
-  .gr-button { background-color: #555; color: white; }
+  body { background-color: #121212 !important; color: #e0e0e0 !important; }
+  .gr-button { background-color: #555 !important; color: white !important; }
+  .gr-input, .gr-textbox, .gr-dropdown, .gr-radio, .gr-markdown, .gr-container { background-color: #333 !important; color: #e0e0e0 !important; }
 </style>
 """
 
@@ -201,14 +228,8 @@ with gr.Blocks() as demo:
             )
         # ----- Right Column (≈70%) -----
         with gr.Column(scale=2):
-            image_input = gr.Image(
-                label="📂 Upload Tomato Leaf Image", 
-                type="pil"
-            )
-            camera_input = gr.Image(
-                label="📸 Use Camera (Live Preview)", 
-                type="pil"
-            )
+            image_input = gr.Image(label="📂 Upload Tomato Leaf Image", type="pil")
+            camera_input = gr.Image(label="📸 Use Camera (Live Preview)", type="pil")
             submit = gr.Button("🔍 Analyze")
     
     output = gr.Textbox(label="📝 Diagnosis & Advice", lines=8)