first commit

app.py

@@ -41,10 +41,13 @@ if model_choice not in st.session_state.downloaded_models:
 if model_choice == "TinyBert Sentiment Analysis":
     text = st.text_area("Enter Text:", "This movie was horrible, the plot was really boring. acting was okay")
     predict = st.button("Predict Sentiment")
+    try:
+        classifier = pipeline("text-classification", model=local_path, device=device)
+    except OSError:
+         st.error("❌ Model not found. Please download the model first.")
+         classifier = None
 
-    classifier = pipeline("text-classification", model=local_path, device=device)
-
-    if predict:
+    if predict and classifier:
         with st.spinner("Predicting..."):
             output = classifier(text)
             st.write(output)
@@ -53,10 +56,13 @@ if model_choice == "TinyBert Sentiment Analysis":
 if model_choice == "TinyBert Disaster Classification":
     text = st.text_area("Enter Text:", "There is a fire in the building")
     predict = st.button("Predict Sentiment")
+    try:
+        classifier = pipeline("text-classification", model=local_path, device=device)
+    except OSError:
+        st.error("❌ Model not found. Please download the model first.")
+        classifier = None
 
-    classifier = pipeline("text-classification", model=local_path, device=device)
-
-    if predict:
+    if predict and classifier:
         with st.spinner("Predicting..."):
             output = classifier(text)
             st.write(output)
@@ -68,7 +74,7 @@ if model_choice == "VIT Pose Classification":
 
     if uploaded_file is not None:
         image = Image.open(uploaded_file)
-        st.image(image, caption="Your Image", use_column_width=True)
+        st.image(image, caption="Your Image", width=300)
 
         image_processor = AutoImageProcessor.from_pretrained(local_path, use_fast=True)
         pipe = pipeline('image-classification', model=local_path, image_processor=image_processor, device=device)