Delete app.py

app.py

@@ -1,428 +0,0 @@
-import os
-import torch
-import gradio as gr
-
-from transformers import (
-    AutoTokenizer,
-    AutoModelForSequenceClassification
-)
-
-from peft import PeftModel
-
-
-# ================================================================
-# CONFIG
-# ================================================================
-
-BASE_MODEL = "distilbert-base-uncased"
-ADAPTER_PATH = "."
-
-MAX_LENGTH = 128
-
-LABELS = {
-    0: "negative",
-    1: "neutral",
-    2: "positive"
-}
-
-
-# ================================================================
-# DEVICE
-# ================================================================
-
-DEVICE = torch.device(
-    "cuda" if torch.cuda.is_available() else "cpu"
-)
-
-print("=" * 60)
-print("INSURANCE SENTIMENT AI")
-print("=" * 60)
-
-print("Device:", DEVICE)
-
-if torch.cuda.is_available():
-    print("GPU:", torch.cuda.get_device_name(0))
-
-
-# ================================================================
-# CHECK FILES
-# ================================================================
-
-print("\nChecking model files...")
-
-required_files = [
-    "adapter_config.json",
-    "adapter_model.safetensors",
-    "tokenizer.json",
-    "tokenizer_config.json"
-]
-
-for file in required_files:
-
-    path = os.path.join(
-        ADAPTER_PATH,
-        file
-    )
-
-    print(
-        file,
-        "->",
-        "OK" if os.path.exists(path) else "MISSING"
-    )
-
-
-# ================================================================
-# TOKENIZER
-# ================================================================
-
-print("\nLoading tokenizer...")
-
-tokenizer = AutoTokenizer.from_pretrained(
-    ADAPTER_PATH,
-    use_fast=True
-)
-
-print("Tokenizer loaded.")
-
-
-# ================================================================
-# BASE MODEL
-# ================================================================
-
-print("\nLoading base DistilBERT...")
-
-base_model = AutoModelForSequenceClassification.from_pretrained(
-    BASE_MODEL,
-    num_labels=3,
-    id2label=LABELS,
-    label2id={
-        "negative": 0,
-        "neutral": 1,
-        "positive": 2
-},
-    ignore_mismatched_sizes=True
-)
-
-print("Base model loaded.")
-
-
-# ================================================================
-# LOAD LoRA
-# ================================================================
-
-print("\nLoading LoRA adapter...")
-
-model = PeftModel.from_pretrained(
-    base_model,
-    ADAPTER_PATH
-)
-
-print("LoRA adapter loaded.")
-
-
-# ================================================================
-# MERGE LoRA
-# ================================================================
-
-print("\nMerging LoRA adapter...")
-
-try:
-
-    model = model.merge_and_unload()
-
-    print("LoRA merged successfully.")
-
-except Exception as e:
-
-    print("Merge skipped:", str(e))
-
-
-# ================================================================
-# DEVICE
-# ================================================================
-
-model = model.to(DEVICE)
-
-model.eval()
-
-print("\nModel ready.")
-
-
-# ================================================================
-# PREDICTION
-# ================================================================
-
-def predict_sentiment(text):
-
-    if text is None:
-        return {
-            "negative": 0.0,
-            "neutral": 0.0,
-            "positive": 0.0
-        }
-
-    text = str(text).strip()
-
-    if not text:
-
-        return {
-            "negative": 0.0,
-            "neutral": 0.0,
-            "positive": 0.0
-        }
-
-    # ------------------------------------------------------------
-    # Tokenize
-    # ------------------------------------------------------------
-
-    inputs = tokenizer(
-        text,
-        return_tensors="pt",
-        truncation=True,
-        max_length=MAX_LENGTH,
-        padding=True
-    )
-
-    inputs = {
-        key: value.to(DEVICE)
-        for key, value in inputs.items()
-    }
-
-    # ------------------------------------------------------------
-    # Inference
-    # ------------------------------------------------------------
-
-    with torch.inference_mode():
-
-        outputs = model(
-            **inputs
-        )
-
-        probabilities = torch.softmax(
-            outputs.logits,
-            dim=-1
-        )[0]
-
-    # ------------------------------------------------------------
-    # Results
-    # ------------------------------------------------------------
-
-    result = {
-        "negative": float(
-            probabilities[0].item()
-        ),
-
-        "neutral": float(
-            probabilities[1].item()
-        ),
-
-        "positive": float(
-            probabilities[2].item()
-        )
-    }
-
-    return result
-
-
-# ================================================================
-# DETAILED PREDICTION
-# ================================================================
-
-def analyze_news(text):
-
-    if text is None or not str(text).strip():
-
-        return (
-            "Please enter an insurance news headline.",
-            "",
-            ""
-        )
-
-    text = str(text).strip()
-
-    probabilities = predict_sentiment(text)
-
-    predicted_label = max(
-        probabilities,
-        key=probabilities.get
-    )
-
-    confidence = (
-        probabilities[predicted_label] * 100
-    )
-
-    result_text = (
-        f"Prediction: {predicted_label.upper()}\n"
-        f"Confidence: {confidence:.2f}%"
-    )
-
-    probability_text = (
-        f"Negative: "
-        f"{probabilities['negative'] * 100:.2f}%\n"
-        f"Neutral: "
-        f"{probabilities['neutral'] * 100:.2f}%\n"
-        f"Positive: "
-        f"{probabilities['positive'] * 100:.2f}%"
-    )
-
-    return (
-        result_text,
-        probability_text,
-        text
-    )
-
-
-# ================================================================
-# EXAMPLES
-# ================================================================
-
-examples = [
-
-    [
-        "Insurance companies reported strong financial results."
-    ],
-
-    [
-        "The insurer suffered major losses after severe claims."
-    ],
-
-    [
-        "The insurance company maintained stable operations."
-    ],
-
-    [
-        "The insurer reported record profits and strong growth."
-    ],
-
-    [
-        "Insurance claims increased significantly after the disaster."
-    ]
-]
-
-
-# ================================================================
-# GRADIO UI
-# ================================================================
-
-DESCRIPTION = """
-# 🛡️ Insurance Sentiment AI
-
-AI-powered sentiment analysis for **insurance news headlines**.
-
-### Classes
-
-🟢 **Positive** — favorable insurance/business news  
-⚪ **Neutral** — factual or balanced news  
-🔴 **Negative** — unfavorable insurance/business news
-
-Model: **DistilBERT + LoRA**
-
-Validation Macro F1: **78.06%**
-"""
-
-
-with gr.Blocks(
-    title="Insurance Sentiment AI"
-) as demo:
-
-    gr.Markdown(
-        DESCRIPTION
-    )
-
-    gr.Markdown(
-        "Enter an English insurance news headline:"
-    )
-
-    with gr.Row():
-
-        with gr.Column():
-
-            text_input = gr.Textbox(
-                label="Insurance News",
-                placeholder=(
-                    "Example: "
-                    "Insurance companies reported "
-                    "strong financial results."
-                ),
-                lines=5
-            )
-
-            analyze_button = gr.Button(
-                "🔍 Analyze Sentiment",
-                variant="primary"
-            )
-
-        with gr.Column():
-
-            prediction_output = gr.Textbox(
-                label="Prediction",
-                lines=3
-            )
-
-            probability_output = gr.Textbox(
-                label="Probabilities",
-                lines=4
-            )
-
-            analyzed_text = gr.Textbox(
-                label="Analyzed Headline",
-                lines=3
-            )
-
-    analyze_button.click(
-        fn=analyze_news,
-        inputs=text_input,
-        outputs=[
-            prediction_output,
-            probability_output,
-            analyzed_text
-        ]
-    )
-
-    text_input.submit(
-        fn=analyze_news,
-        inputs=text_input,
-        outputs=[
-            prediction_output,
-            probability_output,
-            analyzed_text
-        ]
-    )
-
-    gr.Examples(
-        examples=examples,
-        inputs=text_input
-    )
-
-    gr.Markdown(
-        """
----
-
-### Model Information
-
-- **Base:** DistilBERT
-- **Fine-tuning:** LoRA / PEFT
-- **Task:** Insurance News Sentiment Classification
-- **Labels:** Negative / Neutral / Positive
-- **Validation Accuracy:** 79.75%
-- **Validation Macro F1:** 78.06%
-"""
-    )
-
-
-# ================================================================
-# LAUNCH
-# ================================================================
-
-if __name__ == "__main__":
-
-    demo.launch(
-        server_name="0.0.0.0",
-        server_port=int(
-            os.environ.get(
-                "PORT",
-                7860
-            )
-        ),
-        show_error=True
-    )