Update app.py

app.py

@@ -12,24 +12,8 @@ device = 0 if torch.cuda.is_available() else -1
 torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
 
 # Load Pipelines with FP16 (if GPU available)
-question_answering = pipeline("question-answering", model="deepset/roberta-base-squad2", device=device)
-code_generation = pipeline("text-generation", model="Salesforce/codegen-350M-mono", device=device)
 summarization = pipeline("summarization", model="facebook/bart-large-cnn", device=device)
-#translation = pipeline("translation_en_to_fr", model="Helsinki-NLP/opus-mt-en-fr", device=device)
-#translation = pipeline("translation", model="facebook/m2m100_418M", device=device)
-text_generation = pipeline("text-generation", model="gpt2", device=device)
-text_classification = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english", device=device)
 
-# Define Functions for Each Task
-@spaces.GPU
-def answer_question(context, question):
-    result = question_answering(question=question, context=context)
-    return result["answer"]
-
-@spaces.GPU
-def generate_code(prompt):
-    output = code_generation(prompt, max_length=50)
-    return output[0]['generated_text']
 
 @spaces.GPU
 def summarize_text(text):
@@ -38,51 +22,19 @@ def summarize_text(text):
 
 
 
-@spaces.GPU
-def generate_text(prompt):
-    output = text_generation(prompt, max_length=100)
-    return output[0]['generated_text']
-
-@spaces.GPU
-def classify_text(text):
-    output = text_classification(text)
-    return f"Label: {output[0]['label']} | Score: {output[0]['score']:.4f}"
 
 # Gradio Interface
 with gr.Blocks() as demo:
     gr.Markdown("# 🤖 Transformers Pipeline with FP16 Inference")
     
-    with gr.Tab("1️⃣ Question Answering"):
-        with gr.Row():
-            context = gr.Textbox(label="Context", lines=4, placeholder="Paste your paragraph here...")
-            question = gr.Textbox(label="Question", placeholder="Ask a question...")
-        answer_btn = gr.Button("Get Answer")
-        answer_output = gr.Textbox(label="Answer")
-        answer_btn.click(answer_question, inputs=[context, question], outputs=answer_output)
 
-    with gr.Tab("2️⃣ Code Generation"):
-        code_input = gr.Textbox(label="Code Prompt", placeholder="Write code snippet...")
-        code_btn = gr.Button("Generate Code")
-        code_output = gr.Textbox(label="Generated Code")
-        code_btn.click(generate_code, inputs=code_input, outputs=code_output)
-
-    with gr.Tab("3️⃣ Summarization"):
-        summary_input = gr.Textbox(label="Text to Summarize", lines=5, placeholder="Paste long text here...")
+    with gr.Tab("Text Summarization"):
+        summary_input = gr.Textbox(label="Text to Summarize ", lines=5, placeholder="Paste long text here...")
         summary_btn = gr.Button("Summarize")
         summary_output = gr.Textbox(label="Summary")
         summary_btn.click(summarize_text, inputs=summary_input, outputs=summary_output)
 
-    with gr.Tab("4️⃣ Text Generation"):
-        text_input = gr.Textbox(label="Text Prompt", placeholder="Start your text...")
-        text_btn = gr.Button("Generate Text")
-        text_output = gr.Textbox(label="Generated Text")
-        text_btn.click(generate_text, inputs=text_input, outputs=text_output)
-
-    with gr.Tab("5️⃣ Text Classification"):
-        classify_input = gr.Textbox(label="Enter Text", placeholder="Enter a sentence...")
-        classify_btn = gr.Button("Classify Sentiment")
-        classify_output = gr.Textbox(label="Classification Result")
-        classify_btn.click(classify_text, inputs=classify_input, outputs=classify_output)
+   
 
 # Launch App
 demo.launch()