Switched to FLAN-T5-Small and added UX improvements

Dockerfile

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+# Use a suitable base image with Python installed
+FROM python:3.9-slim
+
+# Set the working directory inside the container to /app
+WORKDIR /app
+
+# Copy all files from the current directory on the host to the container's /app directory
+COPY . .
+
+# Install Python dependencies listed in requirements.txt
+RUN pip3 install -r requirements.txt
+
+# --- FIX: Set Streamlit to use /tmp for configuration to avoid PermissionError ---
+ENV STREAMLIT_SERVER_PORT=7860 \
+    STREAMLIT_SERVER_ADDRESS=0.0.0.0 \
+    STREAMLIT_BROWSER_GATHER_USAGE_STATS=false \
+    # This is the crucial fix for the PermissionError:
+    STREAMLIT_GLOBAL_DIR="/tmp/streamlit_global" 
+
+# Define the command to run the Streamlit app
+CMD ["streamlit", "run", "app.py"]

app.py

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+import streamlit as st
+import torch
+import time
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+# --- Configuration ---
+# Using FLAN-T5-Small model (approx. 80MB) for fast startup
+MODEL_NAME = "google/flan-t5-small" 
+
+# -------------------- Model Logic --------------------
+
+# Define the system prompt with tuning incorporated
+sys_prompt = """
+Categorize the sentiment of the customer review as positive, negative, or neutral.
+Leverage your expertise in the aviation industry and deep understanding of industry trends to analyze the nuanced expressions and overall tone.
+It is crucial to accurately identify neutral sentiments, which may indicate a balanced view or neutral stance towards the airline. Neutral expressions could involve factual statements without explicit positive or negative opinions.
+Consider the importance of these neutral sentiments in gauging the public sentiment towards the airline company.
+For instance, a positive sentiment might convey satisfaction with the airline's services, a negative sentiment could express dissatisfaction, while neutral sentiment may reflect an impartial observation or a neutral standpoint
+"""
+
+@st.cache_resource
+def load_llm():
+    """Loads the model and tokenizer with a friendly loading message."""
+    try:
+        with st.spinner(f"Loading tokenizer and model ({MODEL_NAME}). This is the slowest part!"):
+            # Determine the device
+            device = "cuda" if torch.cuda.is_available() else "cpu"
+            st.info(f"Using device: **{device.upper()}**. Starting model download...")
+            
+            start_time = time.time()
+            tokenizer = T5Tokenizer.from_pretrained(MODEL_NAME)
+            # We explicitly place the model on the determined device
+            model = T5ForConditionalGeneration.from_pretrained(MODEL_NAME).to(device)
+            
+            end_time = time.time()
+            st.success(f"Model {MODEL_NAME} loaded successfully in {end_time - start_time:.2f} seconds!")
+            
+            return tokenizer, model, device
+            
+    except Exception as e:
+        st.error(f"FATAL ERROR LOADING MODEL: {e}")
+        st.info("Check the Space logs for dependency or memory errors.")
+        return None, None, None
+
+def llm_response(tokenizer, model, device, prompt):
+    if tokenizer is None or model is None:
+        return "Model not initialized due to previous error."
+    
+    # Process and move input to the correct device
+    input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
+    
+    # Generate response
+    outputs = model.generate(input_ids, max_length=150, do_sample=False)
+    
+    # Decode and clean the output
+    return tokenizer.decode(outputs[0], skip_special_tokens=True).strip()
+
+def predict_review_sentiment(tokenizer, model, device, review):
+    """Function to format the prompt and get the prediction."""
+    full_prompt = """
+            {}
+            Review text: '{}'
+    """.format(sys_prompt, review)
+    
+    return llm_response(tokenizer, model, device, full_prompt)
+
+
+# -------------------- Streamlit UI --------------------
+
+# --- 1. Load Resources ---
+tokenizer, model, device = load_llm()
+
+# --- 2. System Info Message (Simplified) ---
+st.sidebar.markdown("## 📊 System Info (Check Space Logs for Live Monitor)")
+st.sidebar.info(
+    f"Model is: **{MODEL_NAME}**\n\n"
+    f"Device: **{device.upper()}**\n\n"
+    "Status: **App Fully Loaded**"
+)
+
+# --- 3. Main Application ---
+st.title("✈️ Customer Review Sentiment Analyzer")
+st.markdown(f"Using the ultra-lightweight **{MODEL_NAME}** model for fast sentiment classification.")
+
+st.subheader("Enter the Customer Review:")
+
+# Collect user input
+review_text = st.text_area(
+    "Customer Review:", 
+    height=150, 
+    placeholder="E.g., I'm stuck on the tarmac for two hours. This is unacceptable! #SkyWingsfail"
+)
+
+# Predict button
+if st.button("Predict Sentiment"):
+    if not review_text:
+        st.warning("Please enter a customer review to predict the sentiment.")
+    elif tokenizer is not None:
+        with st.spinner('Analyzing sentiment...'):
+            pred_sent = predict_review_sentiment(tokenizer, model, device, review_text)
+            
+            # Display result with color coding
+            sentiment_text = pred_sent.lower()
+            if 'positive' in sentiment_text:
+                st.success(f"**Predicted Sentiment:** {pred_sent}")
+            elif 'negative' in sentiment_text:
+                st.error(f"**Predicted Sentiment:** {pred_sent}")
+            elif 'neutral' in sentiment_text:
+                st.info(f"**Predicted Sentiment:** {pred_sent}")
+            else:
+                st.warning(f"**Predicted Sentiment (Unknown Category):** {pred_sent}")

requirements.txt

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+# Core libraries
+streamlit
+pandas==2.2.2
+numpy==2.0.2
+
+# Hugging Face Transformers and LLM dependencies
+transformers==4.56.2
+sentencepiece==0.2.1
+torch==2.8.0
+accelerate==1.10.1
+bitsandbytes==0.48.1