Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from sklearn.metrics import f1_score
+import numpy as np
+import os
+
+# ----------------------------
+# Load Hugging Face Model
+# ----------------------------
+MODEL_NAME = "cardiffnlp/twitter-roberta-base-sentiment"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
+
+labels = ["negative", "neutral", "positive"]
+
+
+# ----------------------------
+# Sentiment Prediction Function
+# ----------------------------
+def predict_sentiment(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True)
+    outputs = model(**inputs)
+    probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    prediction = torch.argmax(probs).item()
+    return labels[prediction]
+
+
+# ----------------------------
+# Main Processing Function
+# ----------------------------
+def process_file(file):
+    df = pd.read_csv(file.name)
+
+    if "text" not in df.columns:
+        return "CSV must contain a 'text' column.", None
+
+    df["predicted_sentiment"] = df["text"].astype(str).apply(predict_sentiment)
+
+    # Optional evaluation if ground truth exists
+    f1 = None
+    if "sentiment" in df.columns:
+        f1 = f1_score(
+            df["sentiment"],
+            df["predicted_sentiment"],
+            average="weighted"
+        )
+
+    output_path = "results.csv"
+    df.to_csv(output_path, index=False)
+
+    if f1:
+        return f"Processing complete! F1 Score: {round(f1,4)}", output_path
+    else:
+        return "Processing complete! No ground truth column found.", output_path
+
+
+# ----------------------------
+# Gradio Interface
+# ----------------------------
+interface = gr.Interface(
+    fn=process_file,
+    inputs=gr.File(label="Upload CSV File"),
+    outputs=[
+        gr.Textbox(label="Status"),
+        gr.File(label="Download Results")
+    ],
+    title="Aspect-Based Sentiment Analysis App",
+    description="Upload a CSV file with a 'text' column. Optionally include a 'sentiment' column for evaluation."
+)
+
+if __name__ == "__main__":
+    interface.launch()