Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline, Text2SpeechPipeline, VisualQAProcessor
+from PIL import Image
+
+# Load the text classification model
+classifier = pipeline("text-classification")
+# Load the Visual Question Answering (VQA) model
+vqa_model = VisualQAProcessor.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")
+# Load the Text-to-Speech model
+tts_model = Text2SpeechPipeline("facebook/wav2vec2-base-960h")
+
+# Create a Streamlit app
+st.title("Image, Text, and Speech 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")
+question_input = st.sidebar.text_input("Enter Question for Image")
+
+# Function to classify image and text
+def classify(image, text, question):
+    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)
+        st.write("Question for Image:", question)
+
+        # Text classification
+        text_result = classifier(text)
+        st.write("Text Classification Result:")
+        st.write(text_result)
+
+        # Visual Question Answering
+        vqa_input = {
+            "question": question,
+            "context": text_result[0]['label'],
+        }
+        vqa_output = vqa_model(vqa_input)
+        st.write("Visual Question Answering Result:")
+        st.write(vqa_output)
+
+        # Text-to-Speech
+        tts_input = vqa_output['answer']
+        tts_output = tts_model(tts_input)
+        st.audio(tts_output[0]['audio'], format='audio/wav')
+
+# Button to trigger classification
+if st.sidebar.button("Classify"):
+    classify(uploaded_image, text_input, question_input)