Update app.py

app.py

@@ -1,29 +1,99 @@
-
 import torch
-from transformers import AutoImageProcessor, AutoModelForImageClassification
-from PIL import Image
 import gradio as gr
+from PIL import Image
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+from selenium import webdriver
+from selenium.webdriver.chrome.options import Options
+import time
+
+# ========== Configuration ==========
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
+# ========== Predict using MobileNetV2 from Hugging Face ==========
+
 def predict(image):
     processor = AutoImageProcessor.from_pretrained("linkanjarad/mobilenet_v2_1.0_224-plant-disease-identification")
     model = AutoModelForImageClassification.from_pretrained("linkanjarad/mobilenet_v2_1.0_224-plant-disease-identification").to(device)
     model.eval()
 
+    if isinstance(image, str):
+        image = Image.open(image).convert("RGB")
+
     inputs = processor(images=image, return_tensors="pt").to(device)
+
     with torch.no_grad():
         logits = model(**inputs).logits
         predicted_class = logits.argmax(-1).item()
         class_name = model.config.id2label.get(predicted_class, "Unknown")
+
     return class_name
 
-iface = gr.Interface(
-    fn=predict,
-    inputs=gr.Image(type="pil"),
-    outputs=gr.Label(),
-    title="Plant Disease Classifier",
-    description="Upload a leaf image to predict plant disease."
-)
+# ========== Scrape Disease Info ==========
+
+def get_disease_info(disease_name):
+    chrome_options = Options()
+    chrome_options.add_argument("--headless")  # Run headless (no UI)
+    driver = webdriver.Chrome(options=chrome_options)
+
+    # Perform a Google search for the disease
+    search_query = f"{disease_name} plant disease causes symptoms treatment"
+    search_url = f"https://www.google.com/search?q={search_query}"
+
+    try:
+        driver.get(search_url)
+        time.sleep(2)  # Wait for JavaScript to load the results
+
+        # Find the first relevant link
+        results = driver.find_elements_by_xpath("//a[@href]")
+
+        # Find the first valid link that seems to be from a trusted source (like Wikipedia)
+        disease_info_url = None
+        for result in results:
+            href = result.get_attribute("href")
+            if href and "wikipedia" in href:
+                disease_info_url = href
+                break
+
+        if not disease_info_url:
+            return f"No relevant information found for '{disease_name}'."
+
+        # Now, scrape the disease details from the Wikipedia link
+        driver.get(disease_info_url)
+        time.sleep(2)
+
+        # Extract text content from the page (just the first few paragraphs)
+        paragraphs = driver.find_elements_by_tag_name("p")
+        disease_info = []
+
+        for paragraph in paragraphs[:5]:  # Limit to 5 paragraphs for brevity
+            disease_info.append(paragraph.text.strip())
+
+        info_summary = "\n".join(disease_info)
+        return info_summary
+
+    except Exception as e:
+        return f"Could not fetch information for '{disease_name}'. Error: {str(e)}"
+
+    finally:
+        driver.quit()
+
+# ========== Web Interface ==========
+
+def wrapped_predict(image):
+    disease_name = predict(image)
+    disease_info = get_disease_info(disease_name)
+    return disease_name, disease_info
+
+def launch_web():
+    interface = gr.Interface(
+        fn=wrapped_predict,
+        inputs=gr.Image(type="pil", label="Upload Leaf Image"),
+        outputs=[gr.Label(label="Predicted Disease"), gr.Textbox(label="Precautions and Solutions")],
+        title="Plant Disease Detection",
+        description="Upload a leaf image to detect the disease and receive actionable solutions fetched from the web."
+    )
+    interface.launch(share=True, debug=True)
 
-iface.launch()
+if __name__ == "__main__":
+    launch_web()