Update app.py

app.py

@@ -1,28 +1,46 @@
+import os
+
+os.system('pip install -r requirements.txt')
+
 import streamlit as st
+from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
+from datasets import load_dataset
+import torch
+import soundfile as sf
 from transformers import pipeline
 from PIL import Image
+import io
+
+st.title('Video to text and then text to speech app')
+
+
+image = st.file_uploader("Upload an image", type=["jpg", "png"])
+
+question = st.text_input(
+    label="Enter your question",
+	value = "How many people and what is the color of this image?"
+)
+
+def generate_speech(text):
+    processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
+    model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
+    vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
+    inputs = processor(text=text, return_tensors="pt")
+
+    embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
+    speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
+
+    speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder)
+
+    sf.write("speech.wav", speech.numpy(), samplerate=16000)
 
-# Load the text classification model
-classifier = pipeline("text-generation", model="Steven-GU-Yu-Di/Visual-Question-Answering")
-
-# Create a Streamlit app
-st.title("Image and Text Classification")
-
-# Sidebar for user inputs
-st.sidebar.title("Input")
-uploaded_image = st.sidebar.file_uploader("Upload Image", type=["jpg", "jpeg", "png"])
-text_input = st.sidebar.text_input("Enter Text Description")
-
-# Function to classify image and text
-def classify(image, text):
-    if image is not None and text:
-        image = Image.open(image)
-        st.image(image, caption="Uploaded Image", use_column_width=True)
-        st.write("Text Description:", text)
-        result = classifier(text)
-        st.write("Classification Result:")
-        st.write(result)
-
-# Button to trigger classification
-if st.sidebar.button("Classify"):
-    classify(uploaded_image, text_input)
+if st.button("Generate"):
+    image = Image.open(io.BytesIO(image.getvalue()))
+    vqa_pipeline = pipeline("visual-question-answering", model="dandelin/vilt-b32-finetuned-vqa")
+    vqa_result = vqa_pipeline({"image": image, "question": question})
+    answer = vqa_result[0]['answer']
+    st.write(f"Question: {question} Answer: {answer}")  # 显示回答
+    generate_speech(f"Question: {question}, Answer: {answer}")
+    audio_file = open("speech.wav", 'rb')
+    audio_bytes = audio_file.read()
+    st.audio(audio_bytes, format="audio/wav")