app.py

from transformers import TextClassificationPipeline
from transformers import AutoTokenizer
from transformers import pipeline
import evaluate
import gradio as gr
import torch
import random
from transformers.file_utils import is_tf_available, is_torch_available, is_torch_tpu_available
from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments
from datasets import load_metric
from sklearn.model_selection import train_test_split
import pandas as pd
import numpy as np
import streamlit as st
from textblob import TextBlob
from streamlit_extras.switch_page_button import switch_page
from transformers import YolosImageProcessor, YolosForObjectDetection
from PIL import Image
import torch
import requests
import numpy as np
import torchvision
from torchvision.io import read_image
from torchvision.utils import draw_bounding_boxes
from transformers import DetrImageProcessor, DetrForObjectDetection
from transformers import DetrImageProcessor, DetrForObjectDetection
from transformers import pipeline
import torch
from transformers import PegasusForConditionalGeneration, PegasusTokenizer


st.set_page_config(layout="wide")
def get_models(prompt):
  #prompt = input("Enter your AI task idea:")
  response = pipe(prompt)
  print("AI Model Idea: ", prompt,"\n")

  x = pd.json_normalize(response[0])
  # x.nlargest(3,['score'])["label"].values
  knowledge_base_tasks = ['depth-estimation', 'image-classification', 'image-segmentation',
        'image-to-image', 'object-detection', 'video-classification',
        'unconditional-image-generation', 'zero-shot-image-classification',
        'conversational', 'fill-mask', 'question-answering',
        'sentence-similarity', 'summarization', 'table-question-answering',
        'text-classification', 'text-generation', 'token-classification',
        'translation', 'zero-shot-classification']

  temp = []
  for label_code in x.nlargest(3,['score'])["label"].values:
    temp.append(label_code[6:])
  # temp

  cat_to_model = {}
  top_cats = []

  for i in range(len(temp)):
    print("Possible Category ",i+1," : ",knowledge_base_tasks[int(temp[i])])
    print("Top three models for this category are:",models_list[models_list["pipeline_tag"] == knowledge_base_tasks[int(temp[i])]].nlargest(3,"downloads")["modelId"].values)
    cat_to_model[knowledge_base_tasks[int(temp[i])]] = models_list[models_list["pipeline_tag"] == knowledge_base_tasks[int(temp[i])]].nlargest(3,"downloads")["modelId"].values
    top_cats.append(knowledge_base_tasks[int(temp[i])])
    # models_list[models_list["pipeline_tag"] == "image-classification"].nlargest(3,"downloads")["modelId"].values
    print()
    print("Returning category-models dictionary..")
  return top_cats,cat_to_model



def get_top_3(top_cat):
    
    top_3_df = pd.read_csv("./Top_3_models.csv")
    top_3 = []
    for i in range(top_3_df.shape[0]):
        if top_3_df["Category"].iloc[i].lower() == top_cat:
                top_3.append(top_3_df["Model_1"].iloc[i])
                top_3.append(top_3_df["Model_2"].iloc[i])
                top_3.append(top_3_df["Model_3"].iloc[i])
                break
    return top_3





def get_top_3_a(prompt,pipe):
    response = pipe(prompt)
    x = pd.json_normalize(response[0])
    temp = []
    for label_code in x.nlargest(3,['score'])["label"].values:
        temp.append(label_code[6:])
    knowledge_base_tasks = ['depth-estimation', 'image-classification', 'image-segmentation',
        'image-to-image', 'object-detection', 'video-classification',
        'unconditional-image-generation', 'zero-shot-image-classification',
        'conversational', 'fill-mask', 'question-answering',
        'sentence-similarity', 'summarization', 'table-question-answering',
        'text-classification', 'text-generation', 'token-classification',
        'translation', 'zero-shot-classification']
    
    top_cat = knowledge_base_tasks[int(temp[0])]
    
    
    top_3_df = pd.read_csv("./Top_3_models.csv")
    top_3 = []
    for i in range(top_3_df.shape[0]):
        if top_3_df["Category"].iloc[i] == top_cat:
                top_3.append(top_3_df["Model_1"].iloc[i])
                top_3.append(top_3_df["Model_2"].iloc[i])
                top_3.append(top_3_df["Model_3"].iloc[i])
                break
    return top_cat,top_3



            
def get_response(input_text,model_name):
    torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
    tokenizer = PegasusTokenizer.from_pretrained(model_name)
    model = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_device)
    batch = tokenizer([input_text],truncation=True,padding='longest',max_length=1024, return_tensors="pt").to(torch_device)
    gen_out = model.generate(**batch,max_length=128,num_beams=5, num_return_sequences=1, temperature=1.5)
    output_text = tokenizer.batch_decode(gen_out, skip_special_tokens=True)
    return output_text

            
def summarizer (models, data):
    model_Eval = {}
    for i in range (len(models)):
        # print(models[i])
        if models[i] == 'tuner007/pegasus_summarizer':
            model_name = 'tuner007/pegasus_summarizer'

            result = get_response(data,model_name)
            rouge = evaluate.load('rouge')
            # print("345",rouge.compute(predictions=[result],references=[data]))
            print(type(result), type([data]))
            quality = rouge.compute(predictions=[result[0]],references=[data])
            model_Eval[models[i]] = {"Score":quality,"Result": result}
        else:
            summarizer_model = pipeline("summarization", model = models[i])
            print(models[i], summarizer_model(data))
            try:
                result = summarizer_model(data)[0]["summary_text"]
                rouge = evaluate.load('rouge')
                # print("345",rouge.compute(predictions=[result],references=[data]))
                quality = rouge.compute(predictions=[result],references=[data])
                model_Eval[models[i]] = {"Score":quality,"Result": result}
            except:
                print("Model {} has issues.".format(models[i]))

    return model_Eval




def best_model (analysis, data):
    best_model_score = 0
    best_model_name = ""
    best_model_result = ""
    temp2 = 0
    for model in analysis.keys():
        temp1 = analysis[model]["Score"]["rougeLsum"]
        if temp1 > temp2:
            temp2 = analysis[model]["Score"]["rougeLsum"]
            best_model_score = analysis[model]["Score"]
            best_model_name = model
            best_model_result = analysis[model]["Result"]

    return best_model_name, best_model_score,data[:50],best_model_result.replace("\n","")



def text_summarization():
    top_models = get_top_3("summarization")
#     st.write("Upload your file: ")
#     uploaded_files = ""
#     uploaded_files = st.file_uploader("Choose your file", accept_multiple_files=True)




    option = st.selectbox(
    'What text would you like AI to summarize for you?',
    ("Choose text files below:",'How to Win friends - Text', 'mocktext', '--')) #add 2 other options of files here

    if option == 'How to Win friends - Text' or option == 'mocktext' or option == '--':### update book text files here
        st.write('You selected:', option)

    if option == 'How to Win friends - Text': # add text
        name = "How_to_win_friends.txt"
        st.write("Selected file for analyis is: How_to_win_friends.txt")

    if option == 'mocktext':
        name = "mocktext.txt"
        st.write("Selected file for analyis is: mocktext.txt")

    if option == '--':
        name = "--"
        st.write("--")
        
        
        
    if st.button("Select"):
        global file_data
#         st.write("filename:", uploaded_files)
#         for uploaded_file in uploaded_files:
# #             print("here")
#             file_data = open(uploaded_file.name,encoding="utf8").read()
#             st.write("filename:", uploaded_file.name)
#     #         st.write(file_data[:500])
# #         print("before summarizer")
#         print(file_data[:50])
        file_data = open(name,encoding="utf8").read()
        
        analysis = summarizer(models = top_models, data = file_data[:500])

        x,c,v,b = best_model(analysis,file_data[:500])
#         st.write("Best model for Task: ",z)

        st.markdown(f'<p style="color: #012d51;font-size:24px;border-radius:%;">{"Best Model with Summarization Results"}</p>', unsafe_allow_html=True) 
        st.write("\nBest model name: ",x)
#         st.write("\nBest model Score: ",c)
        
        st.write("Best Model Rouge Scores: ")
        st.write("Rouge 1 Score: ",c["rouge1"])
        st.write("Rouge 2 Score: ",c["rouge2"])
        st.write("Rouge L Score: ",c["rougeL"])
        st.write("Rouge LSum Score: ",c["rougeLsum"])
    
        st.write("\nOriginal Data first 50 characters: ", v)
        st.write("\nBest Model Result: ",b)


#         print("between summarizer analysis")
        st.markdown(f'<p style="color: #012d51;font-size:18px;border-radius:%;">{"Summarization Results for Model 1"}</p>', unsafe_allow_html=True)
#         st.write("Summarization Results for Model 1")
        st.write("Model name: facebook/bart-large-cnn")
        st.write("Rouge Scores: ")
        st.write("Rouge 1 Score: ",analysis["facebook/bart-large-cnn"]["Score"]["rouge1"])
        st.write("Rouge 2 Score: ",analysis["facebook/bart-large-cnn"]["Score"]["rouge2"])
        st.write("Rouge L Score: ",analysis["facebook/bart-large-cnn"]["Score"]["rougeL"])
        st.write(f"Rouge LSum Score: ",analysis["facebook/bart-large-cnn"]["Score"]["rougeLsum"])
        st.write("Result: ", analysis["facebook/bart-large-cnn"]["Result"])
        
        st.markdown(f'<p style="color: #012d51;font-size:18px;border-radius:%;">{"Summarization Results for Model 2"}</p>', unsafe_allow_html=True)        
#         st.write("Summarization Results for Model 2")
        st.write("Model name: tuner007/pegasus_summarizer")
        st.write("Rouge Scores: ")
        st.write("Rouge 1 Score: ",analysis["tuner007/pegasus_summarizer"]["Score"]["rouge1"])
        st.write("Rouge 2 Score: ",analysis["tuner007/pegasus_summarizer"]["Score"]["rouge2"])
        st.write("Rouge L Score: ",analysis["tuner007/pegasus_summarizer"]["Score"]["rougeL"])
        st.write("Rouge LSum Score: ",analysis["tuner007/pegasus_summarizer"]["Score"]["rougeLsum"])
        st.write("Result: ", analysis["tuner007/pegasus_summarizer"]["Result"][0])
        
        
        
        st.markdown(f'<p style="color: #012d51;font-size:18px;border-radius:%;">{"Summarization Results for Model 3"}</p>', unsafe_allow_html=True)        
#         st.write("Summarization Results for Model 3")
        st.write("Model name: sshleifer/distilbart-cnn-12-6")
        st.write("Rouge Scores: ")
        st.write("Rouge 1 Score: ",analysis["sshleifer/distilbart-cnn-12-6"]["Score"]["rouge1"])
        st.write("Rouge 2 Score: ",analysis["sshleifer/distilbart-cnn-12-6"]["Score"]["rouge2"])
        st.write("Rouge L Score: ",analysis["sshleifer/distilbart-cnn-12-6"]["Score"]["rougeL"])
        st.write("Rouge LSum Score: ",analysis["sshleifer/distilbart-cnn-12-6"]["Score"]["rougeLsum"])
        
        st.write("Result: ", analysis["sshleifer/distilbart-cnn-12-6"]["Result"])

        
        
        
#OBJECT DETECTION

def yolo_tiny(name):
    image = read_image(name)

    model = YolosForObjectDetection.from_pretrained('hustvl/yolos-tiny')
    image_processor = YolosImageProcessor.from_pretrained("hustvl/yolos-tiny")

    inputs = image_processor(images=image, return_tensors="pt")
    outputs = model(**inputs)

    # model predicts bounding boxes and corresponding COCO classes
    logits = outputs.logits
    bboxes = outputs.pred_boxes


    # print results
    target_sizes = torch.tensor([image.shape[::-1][:2]])

    results = image_processor.post_process_object_detection(outputs, threshold=0.7, target_sizes=target_sizes)[0]

    label_ = []
    bboxes = []

    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
        box = [round(i, 2) for i in box.tolist()]
        print(
            f"Detected {model.config.id2label[label.item()]} with confidence "
            f"{round(score.item(), 3)} at location {box}"
        )
        
        label_.append(model.config.id2label[label.item()])
        bboxes.append(np.asarray(box,dtype="int"))
    bboxes = torch.tensor(bboxes, dtype=torch.int)

    img=draw_bounding_boxes(image, bboxes,labels = label_, width=3)
    img = torchvision.transforms.ToPILImage()(img)
    return img
# img.show()



def resnet_101(name):
    image = read_image(name)
    processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-101")
    model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-101")

    inputs = processor(images=image, return_tensors="pt")
    outputs = model(**inputs)

    # convert outputs (bounding boxes and class logits) to COCO API
    # let's only keep detections with score > 0.9
    target_sizes = torch.tensor([image.shape[::-1][:2]])
    results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.7)[0]
    label_ = []
    bboxes = []
    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
        box = [round(i, 2) for i in box.tolist()]
        print(
                f"Detected {model.config.id2label[label.item()]} with confidence "
                f"{round(score.item(), 3)} at location {box}")
        label_.append(model.config.id2label[label.item()])
        bboxes.append(np.asarray(box,dtype="int"))
    bboxes = torch.tensor(bboxes, dtype=torch.int)


    bboxes = torch.tensor(bboxes, dtype=torch.int)

    img=draw_bounding_boxes(image, bboxes,labels = label_, width=3)
    img = torchvision.transforms.ToPILImage()(img)
    return img





def resnet_50(name):
    image = read_image(name)
    processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
    model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")

    inputs = processor(images=image, return_tensors="pt")
    outputs = model(**inputs)

    # convert outputs (bounding boxes and class logits) to COCO API
    # let's only keep detections with score > 0.9
    target_sizes = torch.tensor([image.shape[::-1][:2]])
    results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.7)[0]
    label_ = []
    bboxes = []
    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
        box = [round(i, 2) for i in box.tolist()]
        print(
                f"Detected {model.config.id2label[label.item()]} with confidence "
                f"{round(score.item(), 3)} at location {box}"
        )
        label_.append(model.config.id2label[label.item()])
        bboxes.append(np.asarray(box,dtype="int"))
    bboxes = torch.tensor(bboxes, dtype=torch.int)

    bboxes = torch.tensor(bboxes, dtype=torch.int)

    img=draw_bounding_boxes(image, bboxes,labels = label_, width=3)
    img = torchvision.transforms.ToPILImage()(img)
    return img



def object_detection():
#     st.write("Upload your image: ")
#     uploaded_files = ""
#     uploaded_files = st.file_uploader("Choose a image file", accept_multiple_files=True)

    option = st.selectbox(
    'What image you want for analysis?',
    ("Choose an image for object detection analysis from the options below:",'Cat and Dog', '2 lazy cats chilling on a couch', 'An astronaut riding wild horse'))

    if option == 'Cat and Dog' or option == '2 lazy cats chilling on a couch' or option == 'An astronaut riding wild horse':
        st.write('You selected:', option)

    if option == 'Cat and Dog':
        name = "cat_dog.jpg"
        st.image("cat_dog.jpg")

    if option == '2 lazy cats chilling on a couch':
        name = "cat_remote.jpg"
        st.image("cat_remote.jpg")

    if option == 'An astronaut riding wild horse':
        name = "astronaut_rides_horse.png"
        st.image("astronaut_rides_horse.png")
        
    if st.button("Select"):
    #     global file_data
#         st.write("filename:", uploaded_files)
#         for uploaded_file in uploaded_files:
    #             print("here")
    #         file_data = open(uploaded_file.name).read()
        st.write("filename:", name)
#         name = uploaded_file.name
        st.image([yolo_tiny(name),resnet_101(name),resnet_50(name)],caption=["hustvl/yolos-tiny","facebook/detr-resnet-101","facebook/detr-resnet-50"])


def task_categorization_model_predictions():
    st.image("./panelup.png")

    # st.title("Text Analysis App")

    data = "" 

    classifier = pipeline("zero-shot-classification",model="facebook/bart-large-mnli")

    global check

    st.markdown(f'<p style="color: #012d51;font-size:18px;border-radius:%;">{"Write down below the description of your AI application in few sentences:"}</p>', unsafe_allow_html=True)

    prompt = st.text_input(" ")

    st.write("")
    st.write("")

    if prompt != "":
    #     sbert_saved_model = torch.load("Sbert_saved_model", map_location=torch.device('cpu')).to("cpu")
    #     model = sbert_saved_model.to("cpu")
    #     tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-mpnet-base-v2")
    #     pipe = TextClassificationPipeline(model= model, tokenizer=tokenizer, return_all_scores=True)
    #     # outputs a list of dicts like [[{'label': 'NEGATIVE', 'score': 0.0001223755971295759},  {'label': 'POSITIVE', 'score': 0.9998776316642761}]]

    #     # prompt = ["What is the the best ai for putting text report into data table?","How can I generate car sales agreement with ai model?","AI model to detect burglar on 48 hours of cctv video footage","I need Ai model help me with rewriting 50 financial statements emails into one summary report ?","I need a model for extracting person from an image"]
    #     # responses = pipe(prompt)


    #     models_list = pd.read_csv("models.csv")
    # #     st.write(get_top_3(prompt))

    #     top_cat, top_models = get_top_3(prompt)
    #     # prompt = input("Enter your AI task idea:")
    # #     top_cats,cat_to_models = get_models(prompt)

    # #     top_models = cat_to_models[top_cats[0]]

    #     top_cat = "  " + top_cat[0].upper() + top_cat[1:]



        st.markdown(f'<p style="color: #012d51;font-size:24px;border-radius:%;">{"Recognized AI Domain: "}</p>', unsafe_allow_html=True)

        domains = ["Computer Vision Task","Natural Language Processing Problem","Audio Operations Problem","Tabular Data Task","Reinforcement Learning Problem","Time Series Forecasting Problem"]



        #st.write(classifier(prompt, domains))
        domain = classifier(prompt, domains)["labels"][0]

        st.markdown(f'<p style="background-color:#12d51; color:#1782ea;font-size:18px;border-radius:%;">{domain}</p>', unsafe_allow_html=True)
    #     st.write("Recommended AI Domain Type: ",top_cat)
        check = 0
        if st.button("This seems accurate"):
            check = 1
        if st.button("Show me other likely category recommendations:"):
            if domain == "Tabular Data Problem":
                if st.button("Computer Vision Task"):
                    domain = "Computer Vision Task"
                    check = 1
                if st.button("Natural Language Processing Problem"):
                    domain = "Natural Language Processing Problem"
                    check = 1
                if st.button("Multimodal AI Model"):
                    domain = "Multimodal AI Model"
                    check = 1
                if st.button("Audio Operations Problem"):
                    domain = "Audio Operations Problem"
                    check = 1
        #         if st.button("Tabular Data Task"):
        #             domain = "Tabular Data Task"
                if st.button("Reinforcement Learning Problem"):
                    domain = "Reinforcement Learning Problem"
                    check = 1
                if st.button("Time Series Forecasting Problem"):
                    domain = "Time Series Forecasting Problem"
                    check = 1


            if domain == "Computer Vision Task":
        #         if st.button("Computer Vision Task"):
        #             domain = "Computer Vision Task"
                if st.button("Natural Language Processing Problem"):
                    domain = "Natural Language Processing Problem"
                    check = 1

                if st.button("Multimodal AI Model"):
                    domain = "Multimodal AI Model"
                    check = 1

                if st.button("Audio Operations Problem"):
                    domain = "Audio Operations Problem"
                    check = 1
                if st.button("Tabular Data Task"):
                    domain = "Tabular Data Task"
                    check = 1
                if st.button("Reinforcement Learning Problem"):
                    domain = "Reinforcement Learning Problem"
                    check = 1
                if st.button("Time Series Forecasting Problem"):
                    domain = "Time Series Forecasting Problem"
                    check = 1


            if domain == "Natural Language Processing Problem":
                if st.button("Computer Vision Task"):
                    domain = "Computer Vision Task"
                    check = 1
        #         if st.button("Natural Language Processing Problem"):
        #             domain = "Natural Language Processing Problem"
                if st.button("Multimodal AI Model"):
                    domain = "multimodal"
                    check = 1
                if st.button("Audio Operations Problem"):
                    domain = "Audio Operations Problem"
                    check = 1
                if st.button("Tabular Data Task"):
                    domain = "Tabular Data Task"
                    check = 1
                if st.button("Reinforcement Learning Problem"):
                    domain = "Reinforcement Learning Problem"
                    check = 1
                if st.button("Time Series Forecasting Problem"):
                    domain = "Time Series Forecasting Problem"
                    check = 1


            if domain == "Multimodal AI Model":
                if st.button("Computer Vision Task"):
                    domain = "Computer Vision Task"
                    check = 1
                if st.button("Natural Language Processing Problem"):
                    domain = "Natural Language Processing Problem"
                    check = 1
        #         if st.button("Multimodal AI Model"):
        #             domain = "Multimodal AI Model"
                if st.button("Audio Operations Problem"):
                    domain = "Audio Operations Problem"
                    check = 1
                if st.button("Tabular Data Task"):
                    domain = "Tabular Data Task"
                    check = 1
                if st.button("Reinforcement Learning Problem"):
                    domain = "Reinforcement Learning Problem"
                    check = 1
                if st.button("Time Series Forecasting Problem"):
                    domain = "Time Series Forecasting Problem"
                    check = 1


            if domain == "audio":
                if st.button("Computer Vision Task"):
                    domain = "Computer Vision Task"
                    check = 1
                if st.button("Natural Language Processing Problem"):
                    domain = "Natural Language Processing Problem"
                    check = 1
                if st.button("Multimodal AI Model"):
                    domain = "Multimodal AI Model"
                    check = 1
        #         if st.button("Audio Operations Problem"):
        #             domain = "Audio Operations Problem"
                if st.button("Tabular Data Task"):
                    domain = "Tabular Data Task"
                    check = 1
                if st.button("Reinforcement Learning Problem"):
                    domain = "Reinforcement Learning Problem"
                    check = 1
                if st.button("Time Series Forecasting Problem"):
                    domain = "Time Series Forecasting Problem"
                    check = 1


            if domain == "reinforcement-learning":
                if st.button("Computer Vision Task"):
                    domain = "Computer Vision Task"
                    check = 1
                if st.button("Natural Language Processing Problem"):
                    domain = "Natural Language Processing Problem"
                    check = 1
                if st.button("Multimodal AI Model"):
                    domain = "multimodal"
                    check = 1
                if st.button("Audio Operations Problem"):
                    domain = "Audio Operations Problem"
                    check = 1
                if st.button("Tabular Data Task"):
                    domain = "Tabular Data Task"
                    check = 1
        #         if st.button("Reinforcement Learning Problem"):
        #             domain = "Reinforcement Learning Problem"
                if st.button("Time Series Forecasting Problem"):
                    domain = "Time Series Forecasting Problem"
                    check = 1

            if domain == "Time Series Forecasting":
                if st.button("Computer Vision Task"):
                    domain = "Computer Vision Task"
                    check = 1
                if st.button("Natural Language Processing Problem"):
                    domain = "Natural Language Processing Problem"
                    check = 1
                if st.button("Multimodal AI Model"):
                    domain = "Multimodal AI Model"
                    check = 1
                if st.button("Audio Operations Problem"):
                    domain = "Audio Operations Problem"
                    check = 1
                if st.button("Tabular Data Task"):
                    domain = "Tabular Data Task"
                    check = 1
                if st.button("Reinforcement Learning Problem"):
                    domain = "Reinforcement Learning Problem"
                    check = 1
        #         if st.button("Time Series Forecasting Problem"):
        #             domain = "Time Series Forecasting Problem"

    #     st.write("Recommended Models for category: ",top_cats[0], " are:",top_models)

    #     st.write("Recommended Task category: ",top_models[0])



        knowledge_base_tasks = {"Computer Vision Task":['depth-estimation', 'image-classification', 'image-segmentation',
            'image-to-image', 'object-detection', 'video-classification',
            'unconditional-image-generation', 'zero-shot-image-classification'],"Natural Language Processing Problem":[
            'conversational', 'fill-mask', 'question-answering',
            'sentence-similarity', 'summarization', 'table-question-answering',
            'text-classification', 'text-generation', 'token-classification',
            'translation', 'zero-shot-classification'],"Audio Operations Problem":["audio-classification","audio-to-audio","automatic-speech-recognition",
            "text-to-speech"],"Tabular Data Task":["tabular-classification","tabular-regression"],"others":["document-question-answering",
            "feature-extraction","image-to-text","text-to-image","text-to-video","visual-question-answering"],
            "Reinforcement Learning Problem":["reinforcement-learning"],"time-series-forecasting":["time-series-forecasting"]}    

    #     st.write(check)
    #     st.write(domain)
        if check == 1:

            category = classifier(prompt, knowledge_base_tasks[domain])["labels"][0]


            st.markdown(f'<p style="color: #012d51;font-size:24px;border-radius:%;">{"Recognized sub category in Domain: "+domain}</p>', unsafe_allow_html=True)

            st.markdown(f'<p style="background-color:#12d51; color:#1782ea;font-size:18px;border-radius:%;">{category}</p>', unsafe_allow_html=True)


            top_models = get_top_3(category)
            #st.write(top_models)
            st.markdown(f'<p style=" margin-left: 0px;color: #012d51;font-size:18px;border-radius:%;">{"The best models selected for this domain:"}</p>', unsafe_allow_html=True)


            st.markdown(f'<p style="margin-left: 0px;background-color:#e1e1e1; color:#012d51;font-size:18px;border-radius:%;">{"1- "+top_models[0]}</p>', unsafe_allow_html=True)

            st.image("./buttons1.png")

    #         if st.button("Show more"): 

            st.markdown(f'<p style="margin-left: 0px;background-color:#e1e1e1; color:#012d51;font-size:18px;border-radius:%;">{"2- "+top_models[1]}</p>', unsafe_allow_html=True)
            st.image("./buttons1.png")


            st.markdown(f'<p style="margin-left: 0px;background-color:#e1e1e1; color:#012d51;font-size:18px;border-radius:%;">{"3- "+top_models[2]}</p>', unsafe_allow_html=True)
            st.image("./buttons1.png")



    
    
    
    
def model_selector_sbert():
    
    st.image("./panelup.jpg")

    # st.title("Text Analysis App")

    data = "" 


    st.markdown(f'<p style="color: #012d51;font-size:18px;border-radius:%;">{"Please, enter your AI task:"}</p>', unsafe_allow_html=True)

    prompt = st.text_input(" ")

    st.write("")
    st.write("")

    if prompt != "":
        sbert_saved_model = torch.load("Sbert_saved_model", map_location=torch.device('cpu')).to("cpu")
        model = sbert_saved_model.to("cpu")
        tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-mpnet-base-v2")
        pipe = TextClassificationPipeline(model= model, tokenizer=tokenizer, return_all_scores=True)
        # outputs a list of dicts like [[{'label': 'NEGATIVE', 'score': 0.0001223755971295759},  {'label': 'POSITIVE', 'score': 0.9998776316642761}]]

        # prompt = ["What is the the best ai for putting text report into data table?","How can I generate car sales agreement with ai model?","AI model to detect burglar on 48 hours of cctv video footage","I need Ai model help me with rewriting 50 financial statements emails into one summary report ?","I need a model for extracting person from an image"]
        # responses = pipe(prompt)


        models_list = pd.read_csv("models.csv")
    #     st.write(get_top_3(prompt))

        top_cat, top_models = get_top_3_a(prompt,pipe)
        # prompt = input("Enter your AI task idea:")
    #     top_cats,cat_to_models = get_models(prompt)

    #     top_models = cat_to_models[top_cats[0]]

        top_cat = "  " + top_cat[0].upper() + top_cat[1:]
        st.markdown(f'<p style="color: #012d51;font-size:24px;border-radius:%;">{"Recognized AI Domain Category: "}</p>', unsafe_allow_html=True)

        st.markdown(f'<p style="background-color:#12d51; color:#1782ea;font-size:18px;border-radius:%;">{top_cat}</p>', unsafe_allow_html=True)
    #     st.write("Recommended AI Domain Type: ",top_cat)
    #     st.write("Recommended Models for category: ",top_cats[0], " are:",top_models)

    #     st.write("Recommended Task category: ",top_models[0])


        st.markdown(f'<p style=" margin-left: 0px;color: #012d51;font-size:18px;border-radius:%;">{"The best models selected for this task:"}</p>', unsafe_allow_html=True)


        st.markdown(f'<p style="margin-left: 0px;background-color:#e1e1e1; color:#012d51;font-size:18px;border-radius:%;">{"1- "+top_models[0]}</p>', unsafe_allow_html=True)

        st.image("./buttons1.png")

        if st.button("Show more"): 

            st.markdown(f'<p style="margin-left: 0px;background-color:#e1e1e1; color:#012d51;font-size:18px;border-radius:%;">{"2- "+top_models[1]}</p>', unsafe_allow_html=True)
            st.image("./buttons1.png")


            st.markdown(f'<p style="margin-left: 0px;background-color:#e1e1e1; color:#012d51;font-size:18px;border-radius:%;">{"3- "+top_models[2]}</p>', unsafe_allow_html=True)
            st.image("./buttons1.png")



    
page_names_to_funcs = {
    "Select the best Model for your AI app":model_selector_sbert,
    "Compare Model Outputs on Object Detection": object_detection,
    "Compare Model Outputs on Text Summarization": text_summarization
}

demo_name = st.sidebar.selectbox("Find the best model for your next AI task or compare model outputs", page_names_to_funcs.keys())
page_names_to_funcs[demo_name]()
    
    
    
#     st.write("Recommended Most Popular Model for category ",top_cat, " is:",top_models[0])
#     if st.button("Show more"):
#         for i in range(1,len(top_models)):
#             st.write("Model#",str(i+1),top_models[i])
        

# data = prompt

# # print("before len data")

# if len(data) != 0:
# #     print("after len data")
#     st.write("Recommended Task category: ",top_cats[0])
#     st.write("Recommended Most Popular Model for category ",top_cats[0], " is:",top_models[0])
#     if st.button("Show more"):
#         for i in range(1,len(top_models)):
#             st.write("Model#",str(i+1),top_models[i])
            
#     st.write("Upload your file: ")
#     uploaded_files = ""
#     uploaded_files = st.file_uploader("Choose a text file", accept_multiple_files=True)
#     if st.button("Select"):
#         global file_data
#         st.write("filename:", uploaded_files)
#         for uploaded_file in uploaded_files:
# #             print("here")
#             file_data = open(uploaded_file.name,encoding="utf8").read()
#             st.write("filename:", uploaded_file.name)
#     #         st.write(file_data[:500])
# #         print("before summarizer")
#         print(file_data[:500])
#         analysis = summarizer(models = top_models, data = file_data[:500])
# #         print("between summarizer analysis")

#         z,x,c,v,b = best_model(analysis,file_data[:500])
#         st.write("Best model for Task: ",z)
#         st.write("\nBest model name: ",x)
#         st.write("\nBest model Score: ",c)
#         st.write("\nOriginal Data first 500 characters: ", v)
#         st.write("\nBest Model Result: ",b)
#     st.success(result)