created an app

app.py

@@ -1,37 +1,38 @@
-import bpy
+import streamlit as st
+from transformers import pipeline
 
-# Set up a basic scene with a camera and light
-def setup_scene():
-    # Clear existing objects
-    bpy.ops.object.select_all(action='SELECT')
-    bpy.ops.object.delete()
+# Title of the app
+st.title("Hugging Face Transformers with Streamlit")
 
-    # Add a camera
-    bpy.ops.object.camera_add(location=(0, -5, 3))
-    camera = bpy.context.active_object
-    camera.rotation_euler = (1.109, 0, 0)
-    
-    # Set the camera as active
-    bpy.context.scene.camera = camera
-
-    # Add a light source
-    bpy.ops.object.light_add(type='POINT', location=(0, 0, 5))
+# Sidebar for selecting model
+st.sidebar.header("Select a Model")
 
-    # Add a cube to animate
-    bpy.ops.mesh.primitive_cube_add(location=(0, 0, 0))
-    cube = bpy.context.active_object
-    cube.name = 'Cube'
+model_option = st.sidebar.radio("Choose a task", ["Text Generation", "Text Summarization", "Sentiment Analysis"])
 
-    # Keyframe animation (for example, move the cube)
-    cube.location = (0, 0, 0)
-    cube.keyframe_insert(data_path="location", frame=1)
+# Load the transformer model based on selected task
+if model_option == "Text Generation":
+    st.header("Text Generation")
+    model = pipeline("text-generation", model="gpt2")
+    user_input = st.text_area("Enter your prompt:", "Once upon a time")
     
-    cube.location = (5, 5, 0)
-    cube.keyframe_insert(data_path="location", frame=50)
+    if st.button("Generate Text"):
+        result = model(user_input, max_length=100, num_return_sequences=1)
+        st.write(result[0]["generated_text"])
 
-    # Set the frame range for the animation
-    bpy.context.scene.frame_start = 1
-    bpy.context.scene.frame_end = 50
+elif model_option == "Text Summarization":
+    st.header("Text Summarization")
+    model = pipeline("summarization", model="facebook/bart-large-cnn")
+    user_input = st.text_area("Enter the text to summarize:", "The quick brown fox jumps over the lazy dog.")
+    
+    if st.button("Summarize Text"):
+        result = model(user_input, min_length=25, max_length=100, length_penalty=2.0, num_beams=4, early_stopping=True)
+        st.write(result[0]["summary_text"])
 
-# Execute the setup function
-setup_scene()
+elif model_option == "Sentiment Analysis":
+    st.header("Sentiment Analysis")
+    model = pipeline("sentiment-analysis")
+    user_input = st.text_area("Enter the text to analyze:", "I love programming!")
+    
+    if st.button("Analyze Sentiment"):
+        result = model(user_input)
+        st.write(f"Sentiment: {result[0]['label']}, Confidence: {result[0]['score']:.2f}")