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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-large" | |
| # -------------------- 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 | |
| """ | |
| 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.") |