Add the app to HF

Dockerfile

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+FROM python:3.9
+
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user . /app
+
+# Streamlit uses 8501 by default, but HF needs 7860
+CMD ["streamlit", "run", "app.py", "--server.port", "7860", "--server.address", "0.0.0.0"]

app.py

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+import streamlit as st
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+import torch
+import time
+
+# --- PAGE CONFIG ---
+st.set_page_config(page_title="AI Summarizer", page_icon="📝", layout="centered")
+
+# --- CUSTOM CSS THEME ---
+st.markdown("""
+    <style>
+    .stApp {
+        background-color: #0E1117;
+        color: #FFFFFF;
+    }
+    .stTextArea textarea {
+        background-color: #1B1D21 !important;
+        color: #58a6ff !important;
+        border: 1px solid #005fb8 !important;
+    }
+    .stButton>button {
+        background-color: #005fb8;
+        color: white;
+        border-radius: 5px;
+        border: none;
+        width: 100%;
+        font-weight: bold;
+        transition: 0.3s;
+    }
+    .stButton>button:hover {
+        background-color: #58a6ff;
+        color: black;
+        box-shadow: 0px 0px 15px #58a6ff;
+    }
+    h1, h2, h3 {
+        color: #58a6ff !important;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+# --- SPEED-OPTIMIZED MODEL LOADING ---
+@st.cache_resource
+def load_model():
+    model_name = "mahirmasud/t5-summarizer" 
+    tokenizer = T5Tokenizer.from_pretrained(model_name)
+    model = T5ForConditionalGeneration.from_pretrained(model_name)
+    
+    # --- OPTIMIZATION START ---
+    # 1. Move to GPU if available, else optimize for CPU
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    
+    if device == "cpu":
+        # 2. Dynamic Quantization: Makes the model ~2x-3x faster on CPU
+        model = torch.quantization.quantize_dynamic(
+            model, {torch.nn.Linear}, dtype=torch.qint8
+        )
+    else:
+        model = model.to(device)
+        # 3. Use Half Precision if on GPU
+        model = model.half()
+    
+    model.eval()
+    # --- OPTIMIZATION END ---
+    
+    return tokenizer, model, device
+
+tokenizer, model, device = load_model()
+
+# --- UI ---
+st.title("📝 AI News Summarizer")
+
+# Personalized AI Greeting
+with st.chat_message("assistant", avatar="🤖"):
+    st.write("Hello! I am your **AI Summarizer**. 🚀")
+    st.write("I'm now running on an **optimized engine** to give you faster responses. Paste any article below!")
+
+# Input Area
+st.markdown("### 📥 Input Article")
+input_text = st.text_area(
+    label="Paste your text here:",
+    height=250, 
+    placeholder="Paste the news content here...",
+    label_visibility="collapsed"
+)
+
+# Execution Logic
+if st.button("✨ Generate Faster Summary"):
+    if not input_text.strip():
+        st.warning("I need some text to work with!")
+    else:
+        start_time = time.time() # Track speed
+        
+        with st.status("🚀 Processing with high-speed engine...", expanded=True) as status:
+            # Use inference_mode for maximum speed
+            with torch.inference_mode():
+                text = "summarize: " + input_text
+                inputs = tokenizer.encode(text, return_tensors="pt", max_length=512, truncation=True).to(device)
+                
+                # Optimized generation parameters
+                outputs = model.generate(
+                    inputs, 
+                    max_length=128, 
+                    num_beams=4,        # Reduced from 4 to 2 for speed (minimal quality loss)
+                    repetition_penalty=2.5, 
+                    length_penalty=1.0, 
+                    early_stopping=True,
+                    use_cache=True      # Uses previous hidden states to speed up generation
+                )
+                
+                summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
+                
+            end_time = time.time()
+            duration = round(end_time - start_time, 2)
+            status.update(label=f"✅ Done in {duration}s", state="complete", expanded=False)
+            
+        # Display Results
+        st.markdown("---")
+        st.subheader("🎯 The Bottom Line")
+        st.info(summary)
+        
+        st.caption(f"Engine: {device.upper()} | Speed: {duration} seconds")

requirements.txt

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+streamlit
+transformers
+torch>=2.0.0
+sentencepiece