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import streamlit as st
from PIL import Image
from transformers import pipeline
from langchain_openai import ChatOpenAI
import wave
import numpy as np
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
openai_key = st.sidebar.text_input("Enter your OpenAI Key", type="password")
# Use a pipeline as a high-level helper
pipe = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
pipe2 = pipeline("text-to-speech", model="facebook/mms-tts-eng")
output_parser = StrOutputParser()
def get_scenario_from_img_text(text):
llm = ChatOpenAI(openai_api_key=openai_key)
prompt = ChatPromptTemplate.from_messages([
("system", "You need to write few lines on the scenario provided by user."),
("user", "{text}")
])
chain = prompt | llm
response = chain.invoke({"text": text})
return response.content
def main():
st.title("Image-to-Text-to-Speech App")
# Upload image file
uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
# Open and display the uploaded image
img = Image.open(uploaded_file)
st.image(img, caption="Uploaded Image", use_column_width=True)
# Generate text from the image
result = pipe(img)
text = result[0]['generated_text']
print(text)
text = get_scenario_from_img_text(text)
print(text)
speech = speech = pipe2(text)
# Display the generated text
st.subheader("Generated Text:")
st.write(text)
wav_data = np.array(speech['audio'] * 32767, dtype=np.int16)
with wave.open('output.wav', 'wb') as wav_file:
wav_file.setnchannels(1)
wav_file.setsampwidth(wav_data.dtype.itemsize)
wav_file.setframerate(speech['sampling_rate'])
wav_file.writeframes(wav_data.tobytes())
# Play the WAV file using the st.audio() function
st.audio('output.wav')
if __name__ == "__main__":
main() |