update app.py

app.py

@@ -0,0 +1,81 @@
+import streamlit as st
+import transformers
+from transformers import pipeline
+from PIL import Image
+import requests
+from transformers import AutoProcessor, AutoModelForZeroShotImageClassification
+
+# Define pipelines
+pipe = pipeline("summarization", model="google/pegasus-xsum")
+agepipe = pipeline("image-classification", model="dima806/facial_age_image_detection")
+imgpipe = pipeline("zero-shot-image-classification", model="openai/clip-vit-base-patch32")
+emopipe = pipeline("text-classification", model="michellejieli/emotion_text_classifier")
+transpipe = pipeline("translation_en_to_fr")
+
+st.title("NLP APP")
+option = st.sidebar.selectbox(
+    "Choose a task",
+    ("Summarization", "Age Detection", "Emotion Detection", "Image Classification", "Translation")
+)
+
+if option == "Summarization":
+    st.title("Text Summarization")
+    text = st.text_area("Enter text to summarize")
+    if st.button("Summarize"):
+        if text:
+            st.write("Summary:", pipe(text)[0]["summary_text"])
+        else:
+            st.write("Please enter text to summarize.")
+elif option == "Age Detection":
+    st.title("Welcome to age detection")
+    uploaded_files = st.file_uploader("Choose a image file", type="jpg")
+    if uploaded_files is not None:
+        image = Image.open(uploaded_files)
+        st.write("Detected age is ", agepipe(image)[0]["label"])
+elif option == "Image Classification":
+    st.title("Welcome to object detection")
+    uploaded_file = st.file_uploader("Choose an image file", type=["jpg", "jpeg", "png"])
+    text = st.text_area("Enter possible class names (comma-separated)")
+    if st.button("Submit"):
+        if uploaded_file is not None and text:
+            candidate_labels = [t.strip() for t in text.split(',')]
+            image = Image.open(uploaded_file)
+            st.image(image, caption="Uploaded Image", use_column_width=True)
+            classification_result = imgpipe(image, candidate_labels=candidate_labels)
+            for result in classification_result:
+                st.write(f"Label: {result['label']}, Score: {result['score']}")
+        else:
+            st.write("Please upload an image file and enter class names.")
+elif option == "Emotion Detection":
+    st.title("Detect your emotion")
+    text = st.text_area("Enter your text")
+    if st.button("Submit"):
+        if text:
+            emotion = emopipe(text)[0]["label"]
+            if emotion == "sadness":
+                st.write("Emotion : ", emotion, "😢")
+            elif emotion == "joy":
+                st.write("Emotion : ", emotion, "😃")
+            elif emotion == "fear":
+                st.write("Emotion : ", emotion, "😨")
+            elif emotion == "anger":
+                st.write("Emotion : ", emotion, "😡")
+            elif emotion == "neutral":
+                st.write("Emotion : ", emotion, "😐")
+            elif emotion == "disgust":
+                st.write("Emotion : ", emotion, "🤢")
+            elif emotion == "surprise":
+                st.write("Emotion : ", emotion, "😲")
+        else:
+            st.write("Please enter text.")
+elif option == "Translation":
+    st.title("Text Translation")
+    text = st.text_area("Enter text to translate from English to French")
+    if st.button("Translate"):
+        if text:
+            translation = transpipe(text)[0]["translation_text"]
+            st.write("Translation:", translation)
+        else:
+            st.write("Please enter text to translate.")
+else:
+    st.title("None")