from turtle import title
import gradio as gr
from transformers import pipeline
import numpy as np
from PIL import Image  
from dotenv import load_dotenv
import google.generativeai as genai
import os

# from openai import OpenAI
# client = OpenAI(api_key="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")
# import base64

# # Open the image file and encode it as a base64 string
# def encode_image(image_path):
#     with open(image_path, "rb") as image_file:
#         return base64.b64encode(image_file.read()).decode("utf-8")

#load_dotenv()
#GOOGLE_API_KEY = os.getenv("AIzaSyByqW3ByYPxC4xLS_NhgwAOAMMEgB7DvoY")
genai.configure(api_key="AIzaSyByqW3ByYPxC4xLS_NhgwAOAMMEgB7DvoY")
model_vision = genai.GenerativeModel('gemini-pro-vision')

def gemini_response_vision(input_texts, image):
    try:
        if input_texts != "":
            response2 = model_vision.generate_content([input_texts, image])
        else:
            response2 = model_vision.generate_content(image)

        return response2.text

    except Exception as e:
        raise e

pipes = {
    "ViT/B-16": pipeline("zero-shot-image-classification", model="openai/clip-vit-base-patch16"),
    "ViT/L-14": pipeline("zero-shot-image-classification", model="openai/clip-vit-large-patch14"),
}
inputs = [
    gr.Image(type='pil', 
                    label="Image"),
    gr.Textbox(lines=1, 
                      label="Candidate Labels", placeholder="Add a class label, one by one"),
    gr.Radio(choices=[
                                "ViT/B-16",
                                "ViT/L-14", 
                            ], type="value", label="Model"), 
    gr.Textbox(lines=1, 
                      label="Prompt Template Prompt", 
                      placeholder="Optional prompt template as prefix",
                      value="a photo of a {}"),

    gr.Textbox(lines=1, 
                      label="Prompt Template Prompt", 
                      placeholder="Optional prompt template as suffix",
                      value="in {} {} {} from {} with {}."),
    
    gr.Textbox(lines=1, 
                      label="Prior Domains", placeholder="Add a domain label, one by one"),
]
images="festival.jpg"

def shot(image, labels_text, model_name, hypothesis_template_prefix, hypothesis_template_suffix, domains_text):
    labels = [label.strip(" ") for label in labels_text.strip(" ").split(",")]

    if not domains_text == '':
        domains = [domain.strip(" ") for domain in domains_text.strip(" ").split(",")]
    else:
        #img = Image.open(image)
        input_text = "Please describe the image from six dimensions, including weather (clear, sandstorm, foggy, rainy, snowy), angle (front, left, top), time (daytime, night), occlusion (unoccluded, lightly-occluded, partially-occluded, moderately-occluded, heavily-occluded), season (spring-summer, autumn, winter). Each dimension should be described in no more than 4 words and should match the image content. Please try to output from the options in the previous brackets. If there is no suitable result, output N/A."# Please also output a probability of your inference."# If there is no information in a certain dimension, you can directly output no information.
        domains = gemini_response_vision(input_texts=input_text, image=image)
        #IMAGE_PATH = './reasoning_xy.jpg'
        # base64_image = encode_image('car.png')
        # prompt = "Please describe the image from six dimensions, including weather (clear, sandstorm, foggy, rainy, snowy), angle (front, left, top), time (daytime, night), occlusion (unoccluded, lightly-occluded, partially-occluded, moderately-occluded, heavily-occluded), season (spring-summer, autumn, winter). Each dimension should be described in no more than 4 words and should match the image content. Please try to output from the options in the previous brackets. If there is no suitable result, output N/A."# Please also output a probability of your inference."# If there is no information in a certain dimension, you can directly output no information."
        
        # response = client.chat.completions.create(
        #     model="gpt-4o",
        #     messages=[
        #         # {"role": "system", "content": "You are a helpful assistant that responds in Markdown. Help me with my math homework!"},
        #         {"role": "user", "content": [
        #             {"type": "text", "text": prompt},
        #             {"type": "image_url", "image_url": {
        #                 "url": f"data:image/png;base64,{base64_image}"}
        #             }
        #         ]}
        #     ],
        #     temperature=0.0,
        # )
        # domains = response.choices[0].message.content
        print(domains)
        
    hypothesis_template = hypothesis_template_prefix + ' ' + hypothesis_template_suffix.format(*domains)
    print(hypothesis_template)
    
    res = pipes[model_name](images=image, 
           candidate_labels=labels,
           hypothesis_template=hypothesis_template)
    return {dic["label"]: dic["score"] for dic in res}

#clear, winter, day, front, moderate occlusion

iface = gr.Interface(shot, 
            inputs, 
            "label", 
            examples=[
                    #["festival.jpg", "lantern, firecracker, couplet", "ViT/B-16", "a photo of a {}", "in {} {} {} from {} with {}.", "clear, autumn, day, side, light occlusion"],
                     ["car.png", "car, bike, truck", "ViT/B-16", "a photo of a {}", "in {} {} {} from {} with {}.", ""]], 
            description="""<p>Chinese CLIP is a contrastive-learning-based vision-language foundation model pretrained on large-scale Chinese data. For more information, please refer to the paper and official github. Also, Chinese CLIP has already been merged into Huggingface Transformers! <br><br>
            Paper: <a href='https://arxiv.org/pdf/2403.02714'>https://arxiv.org/pdf/2403.02714</a> <br>
            To begin with the demo, provide a picture (either upload manually, or select from the given examples) and add class labels one by one. Optionally, you can also add template as a prefix to the class labels. <br>""",
            title="Cross-Domain Recognition")

iface.launch()