Update app.py

app.py

@@ -1,20 +1,29 @@
 import streamlit as st
 from transformers import pipeline
+from io import BytesIO
+import torch
 
-def main():
-    sentiment_pipeline = pipeline(model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")
+# Load pipelines
+image_to_text = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")
+text_to_speech = pipeline("text-to-speech", model="facebook/mms-tts-eng")
 
-    st.title("Sentiment Analysis with HuggingFace Spaces")
-    st.write("Enter a sentence to analyze its sentiment:")
+st.title("Image-to-Text and Text-to-Speech App")
 
-    user_input = st.text_input("")
-    if user_input:
-        result = sentiment_pipeline(user_input)
-        sentiment = result[0]["label"]
-        confidence = result[0]["score"]
+# Image uploader
+uploaded_image = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
 
-        st.write(f"Sentiment: {sentiment}")
-        st.write(f"Confidence: {confidence:.2f}")
-
-if __name__ == "__main__":
-    main()
+if uploaded_image:
+    st.image(uploaded_image, caption="Uploaded Image", use_column_width=True)
+    
+    # Convert image to text
+    image_bytes = uploaded_image.read()
+    text_output = image_to_text(image_bytes)[0]['generated_text']
+    st.write("### Extracted Text:")
+    st.write(text_output)
+    
+    # Convert text to speech
+    speech_output = text_to_speech(text_output)
+    audio_bytes = BytesIO(speech_output['audio'])
+    
+    st.write("### Listen to Speech Output:")
+    st.audio(audio_bytes, format="audio/wav")