Update app.py

app.py

@@ -1,62 +1,93 @@
 import gradio as gr
 from transformers import pipeline
+from rouge_score import rouge_scorer
 
-# Lightweight models that work within 16GB RAM
-
-models = {
+# Load lightweight models
 models = {
     "📘 T5-Small": pipeline("text2text-generation", model="t5-small"),
     "📗 DistilGPT2": pipeline("text-generation", model="distilgpt2"),
     "📕 DistilBART": pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
 }
 
-# Content generation function
-def generate_content(topic):
-    outputs = {}
-    for name, model in models.items():
-        try:
-            if "text-generation" in str(model):
-                result = model(topic, max_length=100, do_sample=False)[0]['generated_text']
-            else:
-                result = model(topic, max_length=100, do_sample=False)[0]['generated_text']
-            outputs[name] = result
-        except Exception as e:
-            outputs[name] = f"Error: {str(e)}"
-    return [outputs[name] for name in models.keys()]
-
-# Gradio UI
-gr.Theme.set("default")
+# Summarization + Evaluation Function
+def summarize_and_evaluate(original_text, reference_summary, model_choice):
+    if not original_text.strip():
+        return "❗ Please enter some text.", "", ""
+
+    summarizer = models[model_choice]
+
+    # Decide prompt & config
+    if model_choice == "📘 T5-Small":
+        prompt = "summarize: " + original_text
+        output = summarizer(prompt, max_length=100, min_length=30, do_sample=False)
+        summary = output[0]["generated_text"]
+    elif model_choice == "📗 DistilGPT2":
+        output = summarizer(original_text, max_length=100, num_return_sequences=1)
+        summary = output[0]["generated_text"]
+    else:  # DistilBART
+        output = summarizer(original_text, max_length=100, min_length=30, do_sample=False)
+        summary = output[0]["summary_text"]
+
+    # ROUGE evaluation
+    if reference_summary.strip():
+        scorer = rouge_scorer.RougeScorer(['rouge1', 'rouge2', 'rougeL'], use_stemmer=True)
+        scores = scorer.score(reference_summary, summary)
+        rouge_str = f"""
+🔍 **ROUGE Scores vs Reference**:
+- ROUGE-1: {scores['rouge1'].fmeasure:.2f}
+- ROUGE-2: {scores['rouge2'].fmeasure:.2f}
+- ROUGE-L: {scores['rougeL'].fmeasure:.2f}
+"""
+    else:
+        rouge_str = "ℹ️ No reference summary provided, so accuracy not calculated."
+
+    return summary, rouge_str, ""
+
+# Gradio App UI
 with gr.Blocks(css="""
 body {
-    background-color: #fffde7;
+    background-color: #fffdd0;
 }
-#title {
+#main-title {
     text-align: center;
-    font-size: 34px;
+    font-size: 32px;
     font-weight: bold;
     color: #8b4513;
-    margin-bottom: 20px;
+    margin-top: 20px;
 }
 .output-box {
-    border: 2px solid #d4af37;
-    padding: 10px;
+    border: 2px dashed #caa74d;
+    padding: 12px;
     border-radius: 10px;
-    background-color: #fff8dc;
+    background-color: #fffbe0;
+}
+#footer {
+    text-align: center;
+    font-size: 13px;
+    color: #777;
+    margin-top: 30px;
 }
 """) as demo:
 
-    gr.Markdown("<div id='title'>📝 LLM for Content Generation</div>")
+    gr.Markdown("<div id='main-title'>📝 LLM for Content Generation</div>")
 
     with gr.Row():
-        with gr.Column():
-            topic = gr.Textbox(label="Enter your topic", placeholder="e.g. Climate Change Impact on Economy")
-            generate_button = gr.Button("Generate Content ✨")
+        with gr.Column(scale=1):
+            input_text = gr.Textbox(label="Original Text", lines=10, placeholder="Paste your content...")
+            reference_summary = gr.Textbox(label="Reference Summary (Optional)", lines=4, placeholder="For ROUGE score (optional)")
+            model_choice = gr.Radio(choices=list(models.keys()), value="📘 T5-Small", label="Select Model")
+            summarize_button = gr.Button("🚀 Generate Content & Evaluate")
 
-        with gr.Column():
-            outputs = [gr.Textbox(label=model, lines=6, elem_classes=["output-box"]) for model in models.keys()]
+        with gr.Column(scale=1):
+            output_summary = gr.Textbox(label="Generated Output", lines=10, elem_classes=["output-box"])
+            rouge_output = gr.Markdown()
 
-    generate_button.click(fn=generate_content, inputs=[topic], outputs=outputs)
+    summarize_button.click(
+        fn=summarize_and_evaluate,
+        inputs=[input_text, reference_summary, model_choice],
+        outputs=[output_summary, rouge_output, gr.Textbox(visible=False)]
+    )
 
-    gr.Markdown("<center>💡 Compare outputs from 5 different lightweight LLMs. Fast, efficient, and creative!</center>")
+    gr.Markdown("<div id='footer'>🚧 Powered by Transformers & Gradio | ROUGE auto-evaluation supported</div>")
 
 demo.launch()