Merged models folder from training branch.

models/archive/test3.py

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+from PIL import Image
+import torch
+import numpy as np
+from transformers import CLIPModel, CLIPTokenizer
+
+# Load the model
+model_name = "TonyStarkD99/CLIP-Crop_Disease-Large"
+model = CLIPModel.from_pretrained(model_name)
+
+# Load your image
+image_path = "/home/overnion/Status200/rice.png"  # Replace with your image path
+image = Image.open(image_path)
+
+# Define the class labels (text prompts)
+class_labels = [
+    "healthy plant",
+    "powdery mildew",
+    "leaf rust",
+    "stem rust",
+    "fusarium head blight",
+    "gray leaf spot",
+    "bacterial blight",
+    "downy mildew",
+    "aphid infestation",
+    "white mold",
+    "black rot",
+    "root rot",
+    "yellow leaf curl",
+    "blight",
+    "necrotic spots",
+    "chlorosis",
+    "wilt",
+    "damping off",
+    "viral infection",
+    "pest damage"
+]
+
+# Resize and normalize the image
+image = image.convert("RGB")  # Ensure the image is in RGB format
+image = image.resize((224, 224))  # Resize to the expected input size
+
+# Convert the image to a tensor
+image_tensor = torch.tensor(np.array(image)).permute(2, 0, 1).unsqueeze(0)  # Convert to (1, C, H, W)
+image_tensor = image_tensor.float() / 255.0  # Normalize to [0, 1]
+
+# Load the tokenizer
+tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch16")  # Use a compatible tokenizer
+
+# Tokenize the text prompts
+text_inputs = tokenizer(class_labels, padding=True, return_tensors="pt")
+
+# Make predictions
+with torch.no_grad():
+    outputs = model(pixel_values=image_tensor, input_ids=text_inputs['input_ids'])
+
+logits_per_image = outputs.logits_per_image  # This gives the similarity scores
+probs = logits_per_image.softmax(dim=1)  # Convert to probabilities
+
+# Get the predicted class
+predicted_class_idx = probs.argmax().item()
+predicted_class = class_labels[predicted_class_idx]
+
+# Print the predicted class and probabilities
+print("Predicted class:", predicted_class)
+# print("Probabilities:", probs.detach().numpy())
+