Update app.py

app.py

@@ -1,65 +1,65 @@
 import streamlit as st
 from transformers import pipeline
-from gtts import gTTS
-import os
-from PIL import Image
+from googletrans import Translator
+import time
 
 # Load models
 def load_models():
-    image_to_text = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
-    storyteller = pipeline(
-        "text-generation",
-        model="pranavpsv/gpt2-genre-story-generator",
-        temperature=0.75,
-        max_length=100
-    )
-    return image_to_text, storyteller
+    sentiment_analyzer = pipeline("text-classification", model="miltonc/distilbert-base-uncased_ft_5")
+    summarizer = pipeline("summarization", model="FelixChao/T5-Chinese-Summarization")
+    return sentiment_analyzer, summarizer
+
+
+def sentiment_analysis(text, sentiment_analyzer):
+    try:
+        result = sentiment_analyzer(text)[0]["generated_text"] #Adjusted max and min lengths.
+        return result
+    except Exception as e:
+        print(f"sentiment_analysis error for '{text}': {e}. Returning 'sentiment_analysis Failed'")
+        return "sentiment_analysis Failed"
 
-# Process image to text
-def generate_caption(image, image_to_text):
-    result = image_to_text(image)
-    return result[0]["generated_text"] if result else "No caption generated."
 
 # Generate a narrative story using the GPT-2 genre-based story generator
-def generate_story(text, storyteller):
-    prompt = f"<BOS> <superhero> {text}"
-    story = storyteller(prompt, max_length=100, num_return_sequences=1)
-    # Clean the generated text by removing the <BOS> <superhero> prefix
-    generated_story = story[0]["generated_text"].replace("<BOS> <superhero>", "").strip()
-    return generated_story if generated_story else "No story generated."
+def summarize_news(text, summarizer):
+    try:
+        summary = summarizer(text, max_length=30, min_length=10)[0]['summary_text']
+        return summary
+    except Exception as e:
+        print(f"Summarization error for '{text}': {e}. Returning 'Summarization Failed'")
+        return "Summarization Failed"
+
 
-# Convert text to speech
-def text_to_speech(text, filename="output.mp3"):
-    tts = gTTS(text)
-    tts.save(filename)
-    return filename
+def translate_text(text_to_translate, target_language='en', source_language='zh-TW', delay=1):
+    translator = Translator()
+    try:
+        translation = translator.translate(text_to_translate, dest=target_language, src=source_language)
+        time.sleep(delay)  # Add a delay to avoid rate limiting.
+        return translation.text
+    except Exception as e:
+        print(f"Translation error for '{text_to_translate}': {e}. Returning 'Translation Failed'")
+        time.sleep(delay)
+        return "Translation Failed"
 
 # Main Streamlit app
 def main():
-    st.title("AI-Powered Image Captioning and Storytelling")
+    st.title("AI-Powered Sentiment Analysis and Summarization")
 
-    image_to_text, storyteller = load_models()
+    sentiment_analyzer, summarizer = load_models()
 
-    uploaded_file = st.file_uploader("Upload an image...", type=["jpg", "png", "jpeg"])
+    text = st.text_area("Enter the Chinese text here.....", height=200) # Changed from file_uploader to text_area
 
-    if uploaded_file is not None:
-        # Convert uploaded file to a PIL image
-        image = Image.open(uploaded_file)
-        st.image(image, caption="Uploaded Image", use_container_width=True)
+    if text: # check if text is not empty
+        # google translate package
+        with st.spinner("Analyzing sentiment..."):
+            text_en = translate_text(text, target_language='en', source_language='zh-TW', delay=1)
+            sentiment_output = sentiment_analysis(text_en, sentiment_analyzer)
+            st.write("### Sentiment:")
+            st.write(sentiment_output)
 
-        with st.spinner("Generating caption..."):
-            caption = generate_caption(image, image_to_text)
-            st.write("### Image Caption:")
-            st.write(caption)
-
-        with st.spinner("Generating story..."):
-            story = generate_story(caption, storyteller)
-            st.write("### Generated Story:")
+        with st.spinner("Summarizing News..."):
+            story = summarize_news(text, summarizer)
+            st.write("### Summarized News:")
             st.write(story)
 
-        with st.spinner("Generating speech..."):
-            audio_file = text_to_speech(story)
-            st.audio(audio_file, format="audio/mp3")
-
 if __name__ == "__main__":
     main()