import gradio as gr
import json
import torch
from torch import nn
from torchvision import models, transforms
from huggingface_hub import hf_hub_download
from PIL import Image
import requests
from io import BytesIO

# Define the number of classes
num_classes = 2

# Download model from Hugging Face
def download_model():
    model_path = hf_hub_download(repo_id="jays009/Restnet50", filename="pytorch_model.bin")
    return model_path

# Load the model from Hugging Face
def load_model(model_path):
    model = models.resnet50(pretrained=False)
    model.fc = nn.Linear(model.fc.in_features, num_classes)
    model.load_state_dict(torch.load(model_path, map_location=torch.device("cpu")))
    model.eval()
    return model

# Download the model and load it
model_path = download_model()
model = load_model(model_path)

# Define the transformation for the input image
transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])

# Function to predict from image content
def predict_from_image(image):
    try:
        # Log the image processing
        print(f"Processing image: {image}")
        
        # Ensure the image is a PIL Image
        if not isinstance(image, Image.Image):
            raise ValueError("Invalid image format received. Please provide a valid image.")

        # Apply transformations
        image_tensor = transform(image).unsqueeze(0)

        # Predict
        with torch.no_grad():
            outputs = model(image_tensor)
            predicted_class = torch.argmax(outputs, dim=1).item()

        # Interpret the result
        if predicted_class == 0:
            return {"result": "The photo is of fall army worm with problem ID 126."}
        elif predicted_class == 1:
            return {"result": "The photo is of a healthy maize image."}
        else:
            return {"error": "Unexpected class prediction."}

    except Exception as e:
        print(f"Error during image processing: {e}")
        return {"error": str(e)}

# Function to predict from URL
def predict_from_url(url):
    try:
        # Fetch the image from the URL
        response = requests.get(url)
        response.raise_for_status()  # Ensure the request was successful
        image = Image.open(BytesIO(response.content))
        print(f"Fetched image from URL: {url}")
        return predict_from_image(image)
    except Exception as e:
        print(f"Error during URL processing: {e}")
        return {"error": f"Failed to process the URL: {str(e)}"}

# Gradio interface with logging
def predict(image, url):
    try:
        if image:
            result = predict_from_image(image)
        elif url:
            result = predict_from_url(url)
        else:
            result = {"error": "No input provided. Please upload an image or provide a URL."}
        
        # Log the result
        event_id = id(result)  # Generate a simple event ID for logging purposes
        print(f"Event ID: {event_id}, Result: {result}")
        return result

    except Exception as e:
        print(f"Error in prediction function: {e}")
        return {"error": str(e)}

# Gradio interface setup
iface = gr.Interface(
    fn=predict,
    inputs=[
        gr.Image(type="pil", label="Upload an Image"),
        gr.Textbox(label="Or Enter an Image URL", placeholder="Provide a valid image URL"),
    ],
    outputs=gr.JSON(label="Prediction Result"),
    live=True,
    title="Maize Anomaly Detection",
    description="Upload an image or provide a URL to detect anomalies in maize crops.",
)

# Launch the interface
iface.launch(share=True, show_error=True)