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
from fastapi import FastAPI
from gradio.routes import App

# Define the number of classes
num_classes = 2

# In-memory storage for results
results_cache = {}

# 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)}"}

# Main prediction function with caching
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."}
        
        # Generate and store the event ID
        event_id = id(result)  # Use Python's id() function to generate a unique identifier
        results_cache[event_id] = result
        
        # Log the result
        print(f"Event ID: {event_id}, Result: {result}")
        return {"event_id": event_id, "result": result}

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

# Function to retrieve result by event_id
def get_result(event_id):
    try:
        # Convert event_id from string to int
        event_id = int(event_id)
        result = results_cache.get(event_id)
        if result:
            return result
        else:
            return {"error": "No result found for the provided event ID."}
    except Exception as e:
        return {"error": f"Invalid event ID: {str(e)}"}

# Create a FastAPI app for handling the GET request
app = FastAPI()

@app.get("/result/{event_id}")
def get_result_api(event_id: int):
    return get_result(event_id)

# Gradio interface setup
iface = gr.Blocks()

with iface:
    gr.Markdown("# Maize Anomaly Detection")
    with gr.Row():
        image_input = gr.Image(type="pil", label="Upload an Image")
        url_input = gr.Textbox(label="Or Enter an Image URL", placeholder="Provide a valid image URL")
    output = gr.JSON(label="Prediction Result")
    submit_button = gr.Button("Submit")
    
    submit_button.click(
        fn=predict,
        inputs=[image_input, url_input],
        outputs=output
    )
    
    # Event ID retrieval section
    with gr.Row():
        event_id_input = gr.Textbox(label="Event ID", placeholder="Enter Event ID")
        event_output = gr.JSON(label="Retrieved Result")
        retrieve_button = gr.Button("Get Result")
        
        retrieve_button.click(
            fn=get_result,
            inputs=[event_id_input],
            outputs=event_output
        )

# Launch the Gradio interface
iface.launch(share=True, show_error=True, server_name="0.0.0.0", server_port=7860)