Update app.py

app.py

@@ -9,6 +9,10 @@ from gtts import gTTS
 import speech_recognition as sr
 import os
 import tempfile
+import torch
+from torchvision import models, transforms
+from PIL import Image
+import json
 # from langdetect import detect
 
 
@@ -56,6 +60,7 @@ def transcribe_audio(audio_path):
     except sr.RequestError:
         return "❌ ਗੂਗਲ ਸਪੀਚ ਐਪੀਆਈ ਨਾਲ ਕਨੇਕਟ ਨਹੀਂ ਹੋ ਸਕਿਆ।"
 
+
 # ---------------------------
 # Gemini Response & TTS
 # ---------------------------
@@ -80,7 +85,47 @@ def handle_voice_query(audio_file):
     response = get_gemini_response(query)
     audio_path = text_to_speech(response)
     return query, response, audio_path
-
+# ---------------------------
+# Plant Disease Detection
+# ---------------------------
+# Set device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+# Paths to files
+model_path = "mobilenetv3_plant_disease.pth"
+class_names_path = "class_labels.json"
+
+# Load model
+model_disease = models.mobilenet_v3_small(pretrained=False)
+model_disease.classifier[3] = torch.nn.Linear(model_disease.classifier[3].in_features, 38)
+model_disease.load_state_dict(torch.load(model_path, map_location=device))
+model_disease.to(device)
+model_disease.eval()
+
+# Load and reverse class names
+with open(class_names_path, 'r') as f:
+    label_to_index = json.load(f)
+    index_to_label = {v: k for k, v in label_to_index.items()}
+
+# Define transform
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor()
+])
+
+# Prediction function
+def predict_disease(image_path):
+    try:
+        image = Image.open(image_path).convert("RGB")
+        img_tensor = transform(image).unsqueeze(0).to(device)
+        with torch.no_grad():
+            outputs = model_disease(img_tensor)
+            _, predicted = torch.max(outputs, 1)
+        predicted_class = predicted.item()
+        class_name = index_to_label.get(predicted_class, "Unknown class")
+        return f"🌿 Predicted Disease: *{class_name.replace('_', ' ')}*"
+    except Exception as e:
+        return f"❌ Prediction Failed: {str(e)}"
+        
 # ---------------------------
 # Gradio Interface
 # ---------------------------
@@ -111,5 +156,11 @@ with gr.Blocks() as demo:
             audio_output = gr.Audio(label="🔊 ਆਵਾਜ਼ੀ ਜਵਾਬ")
             submit_btn = gr.Button("➡️ ਜਵਾਬ ਲਵੋ")
             submit_btn.click(fn=handle_voice_query, inputs=[audio_input], outputs=[query_text, gemini_response, audio_output])
-
+            
+        with gr.TabItem("Plant Disease Detection"):
+            gr.Markdown("### Upload a crop leaf image to detect disease")
+            image_input = gr.Image(type="filepath", label="📷 Upload Leaf Image")
+            disease_btn = gr.Button("Detect Disease")
+            disease_output = gr.Markdown()
+            disease_btn.click(fn=predict_disease, inputs=image_input, outputs=disease_output)
 demo.launch()