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app.py

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+import streamlit as st
+from PIL import Image
+import pandas as pd
+from transformers import pipeline
+
+# Create a sentiment analysis pipeline
+sentiment_analysis = pipeline("sentiment-analysis", model="chayanee/Detected_img")
+
+# Set the title for your Streamlit app
+st.title("NLP and Image Analysis")
+
+# Text Input Widget
+text_input = st.text_area("Enter some text for sentiment analysis:")
+
+# Image Upload Widget
+uploaded_image = st.file_uploader("Upload an image for analysis", type=["jpg", "jpeg", "png"])
+
+# Perform sentiment analysis when the user clicks a button
+if st.button("Analyze"):
+    # Perform sentiment analysis on the text
+    if text_input:
+        sentiment_result = sentiment_analysis(text_input)
+        st.write("Sentiment Analysis Result:")
+        st.write(sentiment_result)
+
+    # Analyze the uploaded image if available
+    if uploaded_image:
+        # Display the uploaded image
+        image = Image.open(uploaded_image)
+        st.image(image, caption="Uploaded Image", use_column_width=True)
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+torch
+transformers
+altair<5