Update app.py

app.py

@@ -1,27 +1,32 @@
+import torch
+from torchvision import models, transforms
+from torch import nn, optim
+from PIL import Image
 import gradio as gr
-import pandas as pd
 
-# Load the dataset from the uploaded CSV file
-df = pd.read_csv("thyroid_disease_data.csv")
+# Load a pre-trained model (e.g., ResNet50)
+model = models.resnet50(pretrained=True)
+model.fc = nn.Linear(model.fc.in_features, 2)  # For binary classification (Thyroid: Positive/Negative)
 
-# Define the function to classify the text
-def classify_thyroid_disease(text):
-    # Search the dataset for a matching symptom text (basic string matching)
-    result = df[df['text'].str.contains(text, case=False, na=False)]
-    
-    # Return a diagnosis based on the found label
-    if not result.empty:
-        label = result['label'].iloc[0]
-        diagnosis = "Hypothyroidism" if label == 0 else "Hyperthyroidism"
-        return diagnosis
-    return "No matching diagnosis found."
+# Define image transformation
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+
+# Example function to classify images
+def classify_thyroid_image(image):
+    image = Image.open(image).convert("RGB")
+    image = transform(image).unsqueeze(0)  # Add batch dimension
+    model.eval()  # Set the model to evaluation mode
 
-# Create the Gradio interface
-interface = gr.Interface(
-    fn=classify_thyroid_disease,
-    inputs="text",  # User input will be a text field
-    outputs="text"  # Output will be a text diagnosis
-)
+    with torch.no_grad():
+        output = model(image)
+        _, predicted = torch.max(output, 1)
+    
+    diagnosis = "Thyroid Disease Detected" if predicted.item() == 1 else "No Thyroid Disease"
+    return diagnosis
 
-# Launch the Gradio app
-interface.launch()
+# Create a Gradio interface for image upload and camera input
+gr.Interface(fn=classify_thyroid_image, inputs="image", outputs="text").launch()