add more examples

.gitignore

@@ -0,0 +1 @@
+venv/

app.py

@@ -1,7 +1,28 @@
 import gradio as gr
+from transformers import pipeline
 
-def analyze_file(file):
-    return f" Uploaded file: {file.name}"
+summarizer = pipeline("summarization")
 
-iface = gr.Interface(analyze_file, inputs="upload", outputs="text", title="File Upload")
-iface.launch()
+
+def summarize(doc: str) -> str:
+    results = summarizer(doc, max_length=150, min_length=30, do_sample=False)
+    summary_text = results[0]["summary_text"]
+    return summary_text
+
+
+app = gr.Interface(
+    summarize,
+    [gr.Textbox(label="Text to summarize")],
+    gr.Textbox(label="Summary"),
+    examples=[
+        [
+            "Researchers have discovered a new species of dinosaur in Argentina. The dinosaur, named Bajadasaurus pronuspinax, lived approximately 140 million years ago during the Cretaceous period. It was a herbivore with a long neck and spiky back, similar to the more well-known Stegosaurus. The discovery sheds light on the diversity of dinosaurs in South America during the Cretaceous period."
+        ],
+        [
+            "Recent advances in deep learning have led to significant improvements in natural language processing tasks. In this paper, we propose a novel architecture for text summarization using transformer-based models. Our approach leverages self-attention mechanisms to capture long-range dependencies and generate coherent summaries. Experimental results on benchmark datasets demonstrate the effectiveness of our method compared to existing approaches."
+        ],
+    ],
+)
+
+if __name__ == "__main__":
+    app.launch()

requirements.txt

@@ -1 +1,6 @@
-gradio_pdf
+gradio_pdf
+transformers
+torch 
+torchvision 
+torchaudio
+datasets