Update app.py

app.py

@@ -1,18 +1,22 @@
-
-
-
 import streamlit as st
-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")
+# Dummy functions to simulate the behavior of the removed pipelines
+def summarize(text):
+    return "This is a summary of the text."
+
+def detect_age(image):
+    return "Age: 25-30"
+
+def classify_image(image, candidate_labels):
+    return [{"label": label, "score": 0.5} for label in candidate_labels]
+
+def detect_emotion(text):
+    return "joy"
+
+def translate(text):
+    return "Ceci est une traduction."
 
 st.title("NLP APP")
 option = st.sidebar.selectbox(
@@ -25,15 +29,15 @@ if option == "Summarization":
     text = st.text_area("Enter text to summarize")
     if st.button("Summarize"):
         if text:
-            st.write("Summary:", pipe(text)[0]["summary_text"])
+            st.write("Summary:", summarize(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")
+    uploaded_files = st.file_uploader("Choose an image file", type="jpg")
     if uploaded_files is not None:
         image = Image.open(uploaded_files)
-        st.write("Detected age is ", agepipe(image)[0]["label"])
+        st.write(detect_age(image))
 elif option == "Image Classification":
     st.title("Welcome to object detection")
     uploaded_file = st.file_uploader("Choose an image file", type=["jpg", "jpeg", "png"])
@@ -43,7 +47,7 @@ elif option == "Image Classification":
             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)
+            classification_result = classify_image(image, candidate_labels)
             for result in classification_result:
                 st.write(f"Label: {result['label']}, Score: {result['score']}")
         else:
@@ -53,7 +57,7 @@ elif option == "Emotion Detection":
     text = st.text_area("Enter your text")
     if st.button("Submit"):
         if text:
-            emotion = emopipe(text)[0]["label"]
+            emotion = detect_emotion(text)
             if emotion == "sadness":
                 st.write("Emotion : ", emotion, "😢")
             elif emotion == "joy":
@@ -75,7 +79,7 @@ elif option == "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"]
+            translation = translate(text)
             st.write("Translation:", translation)
         else:
             st.write("Please enter text to translate.")