Update app.py

app.py

@@ -1,30 +1,42 @@
 import streamlit as st
 from transformers import pipeline
 
-# Load the text classification model pipeline
-classifier = pipeline("text-classification",model='wxrrrrrrr/finetunde_sentiment_analysis', return_all_scores=True)
+# Summarization
+def summarization(image_path):
+    with open(image_path, "rb") as image_file:
+        bytes_data = image_file.read()
+    image_to_text_model = pipeline("text-generation", model="ainize/bart-base-cnn")
+    summary = image_to_text_model(bytes_data, max_length=100, do_sample=False)[0]["generated_text"]
+    return summary
 
-# Streamlit application title
-st.title("Text Sentiment")
-st.write("Analysis for 2 emotions: love, anger")
+# Sentiment Classification
+def sentiment_classification(summary):
+    sentiment_model = pipeline("text-classification", model="wxrrrrrrr/finetuned_sentiment_analysis")
+    result = sentiment_model(summary, max_length=100, do_sample=False)[0]['label']
+    return result
 
-# Text input for user to enter the text to classify
-text = st.text_area("Enter the text to analysis", "")
+def main():
+    st.set_page_config(page_title="Your Image to Text Analysis", page_icon="🦜")
+    st.header("Tell me your comments!")
+    uploaded_file = st.file_uploader("Select an Image...")
 
-# Perform text classification when the user clicks the "Classify" button
-if st.button("Analysis"):
-    # Perform text classification on the input text
-    results = classifier(text)[0]
+    if uploaded_file is not None:
+        with open(uploaded_file.name, "wb") as file:
+            file.write(uploaded_file.getbuffer())
+        st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
 
-    # Display the classification result
-    max_score = float('-inf')
-    max_label = ''
+        # Stage 1: Summarization
+        st.text('Processing image to text...')
+        summary = summarization(uploaded_file.name)
+        st.write(summary)
 
-    for result in results:
-        if result['score'] > max_score:
-            max_score = result['score']
-            max_label = result['label']
+        # Stage 2: Sentiment Classification
+        st.text('Analyzing sentiment...')
+        sentiment = sentiment_classification(summary)
+        st.write(sentiment)
 
-    st.write("Text:", text)
-    st.write("Label:", max_label)
-    st.write("Score:", max_score)
+        # Display the classification result
+        st.write("Sentiment:", sentiment)
+
+if __name__ == '__main__':
+    main()