Fix input and thread with gradio.

app.py

@@ -1,32 +1,16 @@
 import gradio as gr
+from transformers import pipeline
 import streamlit as st
 import socket
+import threading
 
-try:
-    from transformers import pipeline
-except ImportError as e:
-    st.error(f"ImportError: {e}")
-    st.stop()
-except Exception as e:
-    st.error(f"Unexpected error: {e}")
-    st.stop()
-
-# Specify the model name explicitly to avoid warnings
-model_name = "distilbert-base-uncased-finetuned-sst-2-english"
-
-try:
-    classifier = pipeline('sentiment-analysis', model=model_name)
-except Exception as e:
-    st.error(f"Error loading pipeline: {e}")
-    st.stop()
+# Load the pre-trained sentiment-analysis pipeline
+classifier = pipeline('sentiment-analysis')
 
 # Function to classify sentiment
 def classify_text(text):
-    try:
-        result = classifier(text)[0]
-        return f"{result['label']} with score {result['score']}"
-    except Exception as e:
-        return f"Error classifying text: {e}"
+    result = classifier(text)[0]
+    return f"{result['label']} with score {result['score']}"
 
 # Function to find an available port
 def find_free_port():
@@ -34,13 +18,23 @@ def find_free_port():
         s.bind(('', 0))
         return s.getsockname()[1]
 
-# Find an available port
-port = find_free_port()
+# Function to run Gradio in a separate thread
+def run_gradio():
+    iface = gr.Interface(fn=classify_text, inputs="text", outputs="text")
+    iface.launch(server_port=find_free_port())
 
-# Launch the Gradio interface on the dynamically found port
-iface = gr.Interface(fn=classify_text, inputs="text", outputs="text")
-iface.launch(server_port=port)
+# Start Gradio in a separate thread
+threading.Thread(target=run_gradio).start()
 
 # Streamlit code
 st.title('IMDb Sentiment Analysis')
 st.write('This project performs sentiment analysis on IMDb movie reviews using Streamlit.')
+
+st.text_input("Enter text for sentiment analysis", key="input_text")
+if st.button("Classify"):
+    text = st.session_state.input_text
+    if text:
+        result = classify_text(text)
+        st.write(result)
+    else:
+        st.write("Please enter text for classification.")