From 8830798bd422f2bd12a1760d5b2c7ab7ca52e763 Mon Sep 17 00:00:00 2001
From: Kshitij <notkshitij@git.kska.io>
Date: Sat, 22 Feb 2025 22:38:02 +0530
Subject: [PATCH] Moved test1, test3 and test4 to archive folder.

---
 models/{ => archive}/test1.py |  0
 models/{ => archive}/test3.py |  2 +-
 models/archive/test4.py       | 50 +++++++++++++++++++++++++++++++++++
 3 files changed, 51 insertions(+), 1 deletion(-)
 rename models/{ => archive}/test1.py (100%)
 rename models/{ => archive}/test3.py (95%)
 create mode 100644 models/archive/test4.py

diff --git a/models/test1.py b/models/archive/test1.py
similarity index 100%
rename from models/test1.py
rename to models/archive/test1.py
diff --git a/models/test3.py b/models/archive/test3.py
similarity index 95%
rename from models/test3.py
rename to models/archive/test3.py
index 36beb81..474552a 100644
--- a/models/test3.py
+++ b/models/archive/test3.py
@@ -8,7 +8,7 @@ model_name = "TonyStarkD99/CLIP-Crop_Disease-Large"
 model = CLIPModel.from_pretrained(model_name)
 
 # Load your image
-image_path = "/home/overnion/Status200/tomato.png"  # Replace with your image path
+image_path = "/home/overnion/Status200/rice.png"  # Replace with your image path
 image = Image.open(image_path)
 
 # Define the class labels (text prompts)
diff --git a/models/archive/test4.py b/models/archive/test4.py
new file mode 100644
index 0000000..d9c79b8
--- /dev/null
+++ b/models/archive/test4.py
@@ -0,0 +1,50 @@
+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/tomato.png"  # Replace with your image path
+image = Image.open(image_path)
+
+# Define the class labels (text prompts)
+class_labels = [
+    "healthy plant",
+    "diseased plant",
+    "wilted plant",
+    "pest-infested plant"
+]
+
+# 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())
+