Upload 2 files

app.py

@@ -0,0 +1,151 @@
+# -*- coding: utf-8 -*-
+"""Noddy.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1qBNYLNHT87B8kwlwXQ4mMM-0OLDGxtum
+"""
+
+import pandas as pd
+import numpy as np
+from datetime import date, datetime
+from PIL import Image
+import requests
+import transformers as trf
+import torch
+import math as Math
+import tensorflow as tf
+
+processor = trf.AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")
+model = trf.AutoModelForZeroShotImageClassification.from_pretrained("openai/clip-vit-base-patch32")
+
+#get bert model
+tokenizer = trf.AutoTokenizer.from_pretrained("fabriceyhc/bert-base-uncased-imdb")
+model1 = trf.AutoModelForSequenceClassification.from_pretrained("fabriceyhc/bert-base-uncased-imdb")
+
+def get_mood(image):
+    #image = image.convert('RGB')
+    #image = image.resize((224, 224))
+    image = np.array(image)
+    image = np.expand_dims(image, axis=0)
+    inputs = processor(text=["happy", "sad", "angry", "surprised", "disgusted", "calm", "neutral"], images=image, return_tensors="pt", padding=True)
+    outputs = model(**inputs)
+    logits_per_image = outputs.logits_per_image # this is the image-text similarity score
+    probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
+    return probs
+
+def get_productivity(image):
+    #image = image.convert('RGB')
+    #image = image.resize((224, 224))
+    image = np.array(image)
+    image = np.expand_dims(image, axis=0)
+    inputs = processor(text=["low productivity", "medium productivity", "high productivity"], images=image, return_tensors="pt", padding=True)
+    outputs = model(**inputs)
+    logits_per_image = outputs.logits_per_image # this is the image-text similarity score
+    probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
+    return probs
+
+#from the prob array, get the index of the highest prob and return the label
+def get_label_mood(probs):
+    labels = ["happy", "sad", "angry", "surprised", "disgusted", "calm", "neutral"]
+    index = torch.argmax(probs)
+    return labels[index]
+
+#from the prob array, get the index of the highest prob and return the label
+def get_label_productivity(probs):
+    labels = ["low", "medium", "high"]
+    index = torch.argmax(probs)
+    return labels[index]
+
+#using bert model, get the sentiment of the text
+def get_sentiment(mood, productivity):
+    text = ["I am feeling " + str(mood) + " and my productivity is " + str(productivity)]
+    inputs = tokenizer(text, return_tensors="pt")
+    outputs = model1(**inputs)
+    probs = outputs.logits.softmax(dim=1)
+    labels = ["negative", "positive"]
+    index = torch.argmax(probs)
+    return labels[index]
+
+# Get user input - use image file from local directory, not streamlit
+user_upload = Image.open("sad_sid.jpg")
+user_upload.resize((224, 224))
+
+# #use the uploaded image to get mood and productivity
+# mood = get_mood(user_upload)
+# mood
+
+# moodname = get_label_mood(mood)
+# moodname
+
+# productivity = get_productivity(user_upload)
+# productivity
+
+# pr = get_label_productivity(productivity)
+# pr
+
+# #use mood and productivity to get sentiment score using bert base model of huggingface
+# sentiment_score = get_sentiment(moodname, pr)
+# sentiment_score
+
+# give playlist based on mood and productivity
+
+def get_playlist(mood, productivity):
+    if(mood == "happy" and (productivity == "high"or productivity == "medium" or productivity =="low")):
+        return "https://open.spotify.com/playlist/37i9dQZF1DXdPec7aLTmlC"
+    elif(mood == "sad" and (productivity == "high" or productivity == "medium")):
+        return "https://open.spotify.com/album/7xSEO4RLeiTcVuwUFLb3UG"
+    elif(mood == "sad" and (productivity == "low")):
+        return "https://open.spotify.com/album/0qRhFH2PLpHPPYUGxwFwHO"
+    elif(mood == "calm" and (productivity == "high"or productivity == "medium")):
+        return "https://open.spotify.com/playlist/37i9dQZF1DWZeKCadgRdKQ"
+    elif(mood == "calm" and (productivity == "low")):
+        return "https://open.spotify.com/playlist/37i9dQZF1DWWQRwui0ExPn"
+    elif(mood == "angry" and (productivity == "high"or productivity == "medium"or productivity =="low")):
+        return "https://open.spotify.com/playlist/37i9dQZF1DX4sWSpwq3LiO"
+    elif (mood == "disgusted" and (productivity == "high" or productivity == "medium" or productivity == "low")):
+        return "https://open.spotify.com/playlist/37i9dQZF1DXa2SPUyWl8Y5"
+    elif (mood == "surprised" and (productivity == "high" or productivity == "medium" or productivity == "low")):
+        return "https://open.spotify.com/playlist/37i9dQZF1DWXti3N4Wp5xy"
+    else:
+        return "https://open.spotify.com/playlist/37i9dQZF1DX4SBhb3fqCJd"
+
+#make an output function that takes in the image and returns the mood and productivity, and then the playlist
+def out(image):
+    mood = get_label_mood(get_mood(image))
+    productivity = get_label_productivity(get_productivity(image))
+    playlist = get_playlist(mood, productivity)
+    sentiment = get_sentiment(mood, productivity)
+    #output fun message based on mood
+    if(mood == "happy"):
+        m = "Yayy! Your current mood is " + str(mood) + " and your productivity is " + str(productivity) + ". Your overall sentiment is " +str(sentiment) +". \n You are doing great! Keep it up!  \n Here is a playlist for you: " + "<a href="+str(playlist)+">"+str(playlist)+"</a>"
+    elif(mood == "sad"):
+        m = "Aww! Your current mood is " + str(mood) + " and your productivity is " + str(productivity) + ". Your overall sentiment is " +str(sentiment) +". \n Don't worry, everything will be alright! \n Here is a playlist to cheer you up: " + "<a href="+str(playlist)+">"+str(playlist)+"</a>"
+    elif(mood == "angry"):
+        m = "Calm down Angry Bird! Your current mood is " + str(mood) + " and your productivity is " + str(productivity) + ". Your overall sentiment is " +str(sentiment) +". \n Take a deep breath and relax! \n Listn to some music: " + "<a href="+str(playlist)+">"+str(playlist)+"</a>"
+    elif(mood == "surprised"):
+        m = "Woah! Your current mood is " + str(mood) + " and your productivity is " + str(productivity) + ". Your overall sentiment is " +str(sentiment) +". \n All of us love surprieses. Here is another one for you " + "<a href="+str(playlist)+">"+str(playlist)+"</a>"
+    elif(mood == "disgusted"):
+        m = "Eww! Your current mood is " + str(mood) + " and your productivity is " + str(productivity) + ". Your overall sentiment is " +str(sentiment) +". \n Don't worry, everything will be alright! \n Here is a playlist to cheer you up: " +"<a href="+str(playlist)+">"+str(playlist)+"</a>"
+    elif(mood == "calm"):
+        m = "Amazing! Your current mood is " + str(mood) + " and your productivity is " + str(productivity) + ". Your overall sentiment is " +str(sentiment) +". \n You are doing great! Keep it up!  \n Here is a playlist for you: " + "<a href="+str(playlist)+">"+str(playlist)+"</a>"
+    else:
+        m = "Your current mood is " + str(mood) + " and your productivity is " + str(productivity) + ". Your overall sentiment is " +str(sentiment) +". \n Here is a playlist for you: " + "<a href="+str(playlist)+">"+str(playlist)+"</a>"
+    return m
+
+
+# c = out(user_upload)
+# c
+
+#import gradio
+import gradio as gr
+
+#image input
+image = gr.inputs.Image(shape=(224, 224))
+
+#output
+output = gr.outputs.HTML("<h2 style='font-family:Helvetica; color:Black; font-size: 20px; text-align: center'>Mood: {mood}</h2><h2 style='font-family:Helvetica; color:Black; font-size: 20px; text-align: center'>Productivity: {productivity}</h2><h2 style='font-family:Helvetica; color:Black; font-size: 20px; text-align: center'>Sentiment: {sentiment}</h2><h2 style='font-family:Helvetica; color:Black; font-size: 20px; text-align: center'>Playlist: <a href='{playlist}'>{playlist}</a></h2>")
+
+#interface
+gr.Interface(fn=out, inputs=image, outputs=output, title="Music Recommender - Noddy", description="Upload an image of yourself and we will recommend a playlist based on your mood and productivity level. \n I hope you have fun and I get grades Professor :) \n -Bhumika", allow_flagging=False, allow_screenshot=False, allow_embedding=False, theme="huggingface").launch(share=True)

requirements.txt

@@ -0,0 +1,6 @@
+transformers
+streamlit
+pandas
+numpy
+torch
+tensorflow