added app.py file

app.py

@@ -0,0 +1,109 @@
+import gradio as gr
+from PIL import Image
+import numpy as np
+import onnxruntime as ort
+import os
+from dotenv import load_dotenv
+import ast
+from openai import OpenAI
+
+# Load environment variables
+load_dotenv()
+
+# === Load and clean class names ===
+class_file_path = os.path.join(os.path.dirname(__file__), 'class_names.txt')
+with open(class_file_path, 'r') as f:
+    raw_line = f.read()
+class_names = ast.literal_eval(raw_line.replace("Classes: ", "").strip())
+
+# === Load ONNX model ===
+model_path = os.path.join(os.path.dirname(__file__), 'model.onnx')
+learn = ort.InferenceSession(model_path)
+
+# === OpenRouter setup ===
+client = OpenAI(
+    base_url="https://openrouter.ai/api/v1",
+    api_key=os.getenv("OPENROUTER_API_KEY"),
+)
+
+def generate_description_and_prevention(label):
+    if label == "not_a_crop":
+        return (
+            "The uploaded image does not seem to show a valid crop or leaf.",
+            "Please upload a clear image of a single crop or a leaf showing disease symptoms."
+        )
+
+    prompt = (
+        f"Explain in simple words what the plant disease or condition '{label}' is, "
+        f"and give 2 to 4 clear, practical prevention tips.\n"
+        "Use this format:\n"
+        "Description:\n"
+        "Explain briefly what this disease is and how it affects the plant.\n"
+        "Prevention:\n"
+        "- Tip 1\n"
+        "- Tip 2\n"
+        "- (Optional) Tip 3\n"
+        "- (Optional) Tip 4"
+    )
+
+    try:
+        response = client.chat.completions.create(
+            model="deepseek/deepseek-r1:free",
+            messages=[
+                {"role": "system", "content": "You are a knowledgeable plant pathologist."},
+                {"role": "user", "content": prompt}
+            ],
+            temperature=0.7,
+            max_tokens=800
+        )
+
+        content = response.choices[0].message.content
+        if "Description:" in content and "Prevention:" in content:
+            parts = content.split("Prevention:")
+            description = parts[0].replace("Description:", "").strip()
+            prevention = parts[1].strip()
+            return description, prevention
+        else:
+            return "Description not structured correctly.", "No prevention steps found."
+    except Exception as e:
+        print(f"[ERROR] OpenRouter API error: {e}")
+        return "OpenRouter error.", "Failed to generate prevention steps."
+
+def preprocess_image(image, size=(224, 224)):
+    image = image.resize(size)
+    img_array = np.array(image).astype(np.float32) / 255.0
+    img_array = img_array.transpose(2, 0, 1)
+    img_array = np.expand_dims(img_array, axis=0)
+    return img_array
+
+def predict(image):
+    image = image.convert("RGB")
+    input_tensor = preprocess_image(image)
+
+    input_name = learn.get_inputs()[0].name
+    outputs = learn.run(None, {input_name: input_tensor})
+    probs = outputs[0][0]
+    pred_idx = int(np.argmax(probs))
+    pred_class = class_names[pred_idx]
+    confidence = float(probs[pred_idx] * 100)
+
+    description, prevention = generate_description_and_prevention(pred_class)
+
+    return pred_class, round(confidence, 2), description, prevention
+
+# === Gradio Interface ===
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=[
+        gr.Label(label="Prediction"),
+        gr.Number(label="Confidence %"),
+        gr.Textbox(label="Description"),
+        gr.Textbox(label="Prevention")
+    ],
+    title="🌱 Crop Disease Detection",
+    description="Upload a crop or leaf image to detect plant diseases and get prevention tips."
+)
+
+if __name__ == "__main__":
+    iface.launch(server_name="0.0.0.0", server_port=7860, debug=True)