Create app.py

app.py

@@ -1,23 +1,62 @@
-import streamlit as st from transformers import pipeline from gtts import gTTS import os
+import streamlit as st
+from transformers import pipeline
 
-def translate_text(text, src_lang, tgt_lang): translator = pipeline("translation_{}-to-{}".format(src_lang, tgt_lang)) result = translator(text, max_length=100)[0]['translation_text'] return result
+# Set up the app title
+st.title("Language Learning App with Hugging Face")
 
-def text_to_speech(text, lang): tts = gTTS(text=text, lang=lang) tts.save("output.mp3") return "output.mp3"
+# Add a sidebar for user input
+st.sidebar.header("Settings")
+task = st.sidebar.selectbox(
+    "Choose a task",
+    ["Translation", "Text Generation", "Sentiment Analysis"]
+)
 
-def main(): st.title("Language Learning App") st.sidebar.header("Settings") src_lang = st.sidebar.selectbox("Select Source Language", ["en", "fr", "es", "de"]) tgt_lang = st.sidebar.selectbox("Select Target Language", ["en", "fr", "es", "de"])
+# Load the Hugging Face pipeline based on the selected task
+if task == "Translation":
+    model_name = st.sidebar.selectbox(
+        "Choose a translation model",
+        ["Helsinki-NLP/opus-mt-en-fr", "Helsinki-NLP/opus-mt-fr-en"]
+    )
+    st.sidebar.write(f"Selected model: {model_name}")
+    translator = pipeline("translation", model=model_name)
 
-st.header("Translation")
-user_input = st.text_area("Enter text to translate:")
-if st.button("Translate"):
-    if user_input:
-        translation = translate_text(user_input, src_lang, tgt_lang)
-        st.success("Translated Text: " + translation)
-        
-        if st.button("Listen to Translation"):
-            audio_file = text_to_speech(translation, tgt_lang)
-            st.audio(audio_file)
+elif task == "Text Generation":
+    model_name = st.sidebar.selectbox(
+        "Choose a text generation model",
+        ["gpt2", "EleutherAI/gpt-neo-125M"]
+    )
+    st.sidebar.write(f"Selected model: {model_name}")
+    generator = pipeline("text-generation", model=model_name)
 
-st.header("Vocabulary Practice")
-st.write("(Coming Soon!)")
+elif task == "Sentiment Analysis":
+    model_name = st.sidebar.selectbox(
+        "Choose a sentiment analysis model",
+        ["distilbert-base-uncased-finetuned-sst-2-english"]
+    )
+    st.sidebar.write(f"Selected model: {model_name}")
+    analyzer = pipeline("sentiment-analysis", model=model_name)
 
-if name == "main": main()
+# Main app functionality
+st.header(f"{task} Task")
+
+if task == "Translation":
+    text = st.text_area("Enter text to translate", "Hello, how are you?")
+    if st.button("Translate"):
+        translation = translator(text)
+        st.write("Translation:")
+        st.write(translation[0]['translation_text'])
+
+elif task == "Text Generation":
+    prompt = st.text_area("Enter a prompt", "Once upon a time")
+    max_length = st.slider("Max length", 10, 100, 50)
+    if st.button("Generate Text"):
+        generated_text = generator(prompt, max_length=max_length)
+        st.write("Generated Text:")
+        st.write(generated_text[0]['generated_text'])
+
+elif task == "Sentiment Analysis":
+    text = st.text_area("Enter text for sentiment analysis", "I love learning new languages!")
+    if st.button("Analyze Sentiment"):
+        sentiment = analyzer(text)
+        st.write("Sentiment Analysis Result:")
+        st.write(sentiment[0])