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d433b9f f8ee7c1 3ca247c f8ee7c1 d433b9f 28c4939 d433b9f f8ee7c1 d433b9f f8ee7c1 28c4939 d433b9f f8ee7c1 d433b9f 3ca247c f8ee7c1 d433b9f f8ee7c1 d433b9f 3ca247c d433b9f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | import os
from PIL import Image
from gtts import gTTS
from io import BytesIO
import io
from openai import OpenAI
#from dotenv import load_dotenv
import streamlit as st
from transformers import pipeline
# For explaining what is going on in the image
img_nar = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large")
#load_dotenv()
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
st.header("Image Narrator")
# Temporary
uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
if 'history' not in st.session_state:
st.session_state['history'] = []
personality = st.text_input("Enter a personality")
image_narration = "No narration given"
# Check if an image has been uploaded
if uploaded_image is not None:
# Convert the uploaded file to a PIL image
bytes_data = uploaded_image.getvalue()
pil_image = Image.open(io.BytesIO(bytes_data))
# Now, use the PIL image with the pipeline
image_narration = img_nar(pil_image)
# Display the uploaded image using the original bytes data
st.image(pil_image, caption='Uploaded Image.', use_column_width=True)
image_narration = image_narration[0]["generated_text"]
#st.write(image_narration)
def update_and_get_narration(personality, user_input):
if personality and user_input:
st.session_state['history'].append({"role": "user", "content": user_input})
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": f"You reiterate what is said to you but narrate it like a {personality}."}
] + st.session_state['history']
)
gpt_response = response.choices[0].message.content
st.session_state['history'].append({"role": "assistant", "content": gpt_response})
return gpt_response
else:
return "Please enter both a personality and some image classification text."
if st.button('Narrate'):
narration = update_and_get_narration(personality, image_narration)
st.write(narration)
tts = gTTS(text=narration, lang='en')
audio_buffer = BytesIO()
tts.write_to_fp(audio_buffer)
audio_buffer.seek(0)
st.audio(audio_buffer, format='audio/mp3', start_time=0)
else:
st.write(st.session_state['history'][-1]['content'] if st.session_state['history'] else "Narration will appear here.")
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