Update app.py

app.py

@@ -1,36 +1,23 @@
 import os
 import gradio as gr
 from transformers import pipeline
-
-# Initialize the model
-try:
-    model_name = "tiiuae/falcon-40b-instruct"  # Ensure this is the correct model name
-    nlp_model = pipeline('text-generation', model=model_name, trust_remote_code=True)  # Added trust_remote_code=True
-    print("Model loaded successfully.")
-except Exception as e:
-    nlp_model = None
-    print(f"Failed to load model: {str(e)}")
+import gradio as gr
+from transformers import pipeline
 
 def generate_text(prompt):
-    """Generate text based on the input prompt using the loaded NLP model."""
-    if nlp_model:
-        try:
-            response = nlp_model(prompt, max_length=50, num_return_sequences=1)
-            return response[0]['generated_text']
-        except Exception as e:
-            return f"Error during model inference: {str(e)}"
-    else:
-        return "Model could not be loaded or initialized properly."
+    generator = pipeline('text-generation', model='tiiuae/falcon-40b-instruct', trust_remote_code=True)
+    return generator(prompt, max_length=100)[0]['generated_text']
 
-# Setup Gradio interface
-iface = gr.Interface(
-    fn=generate_text,
-    inputs=gr.Textbox(lines=2, placeholder="Enter your text here..."),
-    outputs="text",
-    title="Text Generation with Falcon Model",
-    description="Enter some text and see how the Falcon model continues it! This model automatically generates text based on the input provided. Try it out!"
-)
+def main():
+    interface = gr.Interface(
+        fn=generate_text,
+        inputs=gr.inputs.Textbox(lines=2, placeholder="Type your prompt here..."),
+        outputs="text",
+        title="Text Generation Model",
+        description="This model generates text based on the input prompt. Powered by Hugging Face transformers."
+    )
+    
+    interface.launch()
 
-# Essential for Hugging Face Spaces
 if __name__ == "__main__":
-    iface.launch(share=True)
+    main()