Update app.py

app.py

@@ -1,27 +1,96 @@
 import gradio as gr
 from transformers import pipeline
-
 import spaces
 import torch
 
 zero = torch.Tensor([0]).cuda()
 print(zero.device)
 
-# Code Generation Pipeline
-code_pipeline = pipeline("text-generation", model="Salesforce/codegen-350M-mono", device=0)
+
+# Check if GPU is available for FP16 inference
+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)
+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_pipeline(prompt, max_length=50)
+    output = code_generation(prompt, max_length=50)
+    return output[0]['generated_text']
+
+@spaces.GPU
+def summarize_text(text):
+    output = summarization(text, max_length=100, min_length=30, do_sample=False)
+    return output[0]['summary_text']
+
+@spaces.GPU
+def translate_text(text):
+    output = translation(text)
+    return output[0]['translation_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
-demo = gr.Interface(
-    fn=generate_code,
-    inputs=gr.Textbox(label="Enter Code Prompt"),
-    outputs=gr.Textbox(label="Generated Code"),
-    title="Code Generation with Transformers",
-    description="Enter a Python function header or code snippet, and the model will generate the rest."
-)
-
-demo.launch()
+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...")
+        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 Translation (EN → FR)"):
+        translate_input = gr.Textbox(label="English Text", placeholder="Enter text in English...")
+        translate_btn = gr.Button("Translate")
+        translate_output = gr.Textbox(label="French Translation")
+        translate_btn.click(translate_text, inputs=translate_input, outputs=translate_output)
+
+    with gr.Tab("5️⃣ 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("6️⃣ 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()