Update app.py

app.py

@@ -1,27 +1,24 @@
-import os
 import gradio as gr
+import requests
+from transformers import pipeline
 
-# Set the API token as an environment variable without the angle brackets
-os.environ['REPLICATE_API_TOKEN'] = 'r8_AxVDJKL3PAFFamBzTOpV0PpMPgxX0tz0IDf12'
+# Create a Gradio interface
+def caption_image(input_image):
+   
+    caption_model_url = "Salesforce/blip-image-captioning-large" 
+    files = {"file": open(input_image.name, "rb")}
+    response = requests.post(caption_model_url, files=files)
+    caption = response.json()["caption"]
+    
+    # Use the Transformers pipeline for sentiment analysis
+    sentiment_model = pipeline("sentiment-analysis")
+    sentiment_score = sentiment_model(caption)[0]["label"]
+    
+    return f"Caption: {caption}\nSentiment: {sentiment_score}"
 
-# Import replicate
-import replicate
+# Define the Gradio input interface
+inputs = gr.inputs.Image()
+output = gr.outputs.Textbox()
 
-# Define the function that uses replicate
-def run_replicate(prompt):
-    # Use replicate to run the model
-    output = replicate.run(
-        "stability-ai/sdxl:2b017d9b67edd2ee1401238df49d75da53c523f36e363881e057f5dc3ed3c5b2",
-        input={"prompt": prompt}
-    )
-    return output
-
-# Create the Gradio interface
-iface = gr.Interface(
-    fn=run_replicate,
-    inputs=gr.inputs.Textbox(placeholder="Enter your prompt..."),
-    outputs="text"
-)
-
-# Launch the Gradio interface
-iface.launch()
+# Launch the Gradio app
+gr.Interface(fn=caption_image, inputs=inputs, outputs=output, live=True).launch()