Create app.py

app.py

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+from transformers import TextClassificationPipeline, AutoTokenizer, AutoModelForCausalLM
+import torch 
+import gradio as gr
+
+tokenizer = AutoTokenizer.from_pretrained("gpt2")
+model = AutoModelForCausalLM.from_pretrained("Vangmayy/Bollywood-Summary-Generator")
+device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+
+def run_inference(input_text):
+    input_text = str(input_text)
+    input_ids = tokenizer.encode(input_text, return_tensors = "pt")
+    input_ids.to(device)
+    new_model = AutoModelForCausalLM.from_pretrained("Vangmayy/Bollywood-Summary-Generator")
+    output = new_model.generate(input_ids, max_length = 5000, num_return_sequences = 1)
+    output = tokenizer.decode(output[0], skip_special_tokens = True)
+    return output
+
+genres = ["Action", "Comedy", "Drama", "Horror", "Romance", "Sci-Fi", "Thriller"]
+
+with gr.Blocks() as intf:
+    gr.Markdown("## Movie Summary Generator")
+    
+    with gr.Row():
+        genre_checkboxes = gr.CheckboxGroup(choices=genres, label="Select Genres")
+        summary_output = gr.Textbox(label="Generated Summary")
+
+    generate_button = gr.Button("Generate Summary")
+    
+    def on_click(selected_genres):
+        return run_inference(selected_genres)
+
+    generate_button.click(on_click, inputs=genre_checkboxes, outputs=summary_output)
+
+intf.launch()