Create app.py

app.py

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+import streamlit as st
+import time
+# REMOVED: import os (Not needed as it's in Dockerfile)
+from transformers import T5Tokenizer, TFT5ForConditionalGeneration 
+
+# --- Configuration (Unchanged) ---
+MODEL_NAME = "google/flan-t5-base" 
+
+# -------------------- Model Logic --------------------
+
+# CRITICAL FIX: Simplified and highly directive prompt for the smallest model
+# sys_prompt = "Classify the sentiment of the following customer review as either 'positive', 'negative', or 'neutral'. Respond with only one word." 
+sys_prompt = """
+        Classify the sentiment of the following customer review as either 'positive', 'negative', or 'neutral'. Respond with only one word.
+        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 Us Airways. 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():
+    # ... (load_llm function remains identical) ...
+    device = "CPU (TensorFlow)"
+    try:
+        with st.spinner(f"Loading tokenizer and model ({MODEL_NAME}) on {device}..."):
+            st.info(f"Using device: **{device}**. Starting model download...")
+            start_time = time.time()
+            tokenizer = T5Tokenizer.from_pretrained(MODEL_NAME)
+            model = TFT5ForConditionalGeneration.from_pretrained(MODEL_NAME)
+            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("Model load failed.")
+        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."
+    
+    input_ids = tokenizer(prompt, return_tensors="tf").input_ids
+    # Set max_length=1 to force a single token output if possible, but 2 is safer for labels
+    outputs = model.generate(input_ids, max_length=3, do_sample=False) 
+    
+    return tokenizer.decode(outputs[0], skip_special_tokens=True).strip()
+
+def predict_review_sentiment(tokenizer, model, device, review):
+    """
+    CLEANED PROMPT FORMATTING.
+    The final prompt sent to the model is simple: 
+    "Classify the sentiment... Respond with only one word. Review: {review text}"
+    """
+    # FIX: Combine the strict system prompt and the review text clearly
+    full_prompt = f"{sys_prompt} Review: '{review}'" 
+    
+    # Run the prediction and convert the output to standard casing
+    response = llm_response(tokenizer, model, device, full_prompt)
+    
+    # Attempt to normalize the model's output to the three categories
+    normalized_response = response.lower().strip()
+    
+    if "positive" in normalized_response:
+        return "Positive"
+    elif "negative" in normalized_response:
+        return "Negative"
+    elif "neutral" in normalized_response:
+        return "Neutral"
+    else:
+        # For non-classification outputs like 'hi', return the raw response
+        return response
+
+
+# -------------------- Streamlit UI --------------------
+
+# --- 1. Load Resources ---
+tokenizer, model, device = load_llm()
+
+# --- 2. System Info Message (Conditional Display) ---
+st.sidebar.markdown("## 📊 Deployment Status")
+
+if tokenizer is not None:
+    st.sidebar.info(
+        f"Model: **{MODEL_NAME}**\n\n"
+        f"Framework: **{device}**\n\n"
+        "Status: **Local Inference Ready**"
+    )
+
+    # --- 3. Main Application ---
+    st.title("✈️ Customer Review Sentiment Analyzer")
+    st.markdown(f"Using the TensorFlow-backed **{MODEL_NAME}** model.")
+
+    st.subheader("Enter the Customer Review:")
+
+    review_text = st.text_area(
+        "Customer Review:", 
+        height=150, 
+        placeholder="E.g., The flight was delayed, but the crew was excellent."
+    )
+
+    # Predict button
+    if st.button("Predict Sentiment"):
+        if not review_text:
+            st.warning("Please enter a customer review to predict the sentiment.")
+        else:
+            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 (Model Response):** {pred_sent}")
+else:
+    # Display a clear error on the main page if the model load failed
+    st.error("Application Failed to Initialize. Model load aborted (likely missing TensorFlow). Check Space logs.")