Update app.py

app.py

@@ -6,21 +6,16 @@ from safetensors.torch import load_file as load_safetensors
 from transformers import AutoTokenizer
 from openvino.runtime import Core
 import gradio as gr
+import pandas as pd
 
-# 1) Model repo on HF Hub
+# Model & tokenizer loading (same as before)
 HF_MODEL = "Kaiyeee/goemotions-multilabel"
-
-# 2) Load tokenizer once
 tokenizer = AutoTokenizer.from_pretrained("roberta-base")
-
-# 3) Load and compile ONNX with OpenVINO on first request
 core = Core()
-# download and cache the .onnx from the model repo
 onnx_path = hf_hub_download(repo_id=HF_MODEL, filename="goemotions_multilabel.onnx")
 ov_model = core.read_model(model=onnx_path)
 compiled = core.compile_model(model=ov_model, device_name="CPU")
 
-# 4) Emotion labels
 emotion_labels = [
     "admiration","amusement","anger","annoyance","approval","caring","confusion",
     "curiosity","desire","disappointment","disapproval","disgust","embarrassment",
@@ -29,27 +24,51 @@ emotion_labels = [
 ]
 
 def predict(texts, threshold=0.3):
-    # tokenize to numpy
     toks = tokenizer(texts, padding="max_length", truncation=True, max_length=128, return_tensors="np")
     outs = compiled([toks["input_ids"], toks["attention_mask"]])
     logits = outs[compiled.output(0)]
-    probs  = 1 / (1 + np.exp(-logits))
-    preds  = (probs > threshold).astype(int)
+    probs = 1 / (1 + np.exp(-logits))
+    preds = (probs > threshold).astype(int)
 
-    # map back
     results = []
     for i, ps in enumerate(preds):
         fired = [emotion_labels[j] for j, flag in enumerate(ps) if flag]
         results.append(", ".join(fired) or "none")
     return results
 
-# 5) Gradio UI
+# Process multiline text input
+def predict_bulk(texts_str, threshold=0.3):
+    texts = [line.strip() for line in texts_str.split("\n") if line.strip()]
+    results = predict(texts, threshold)
+    return "\n".join(f"{t}: {r}" for t, r in zip(texts, results))
+
+# Process CSV file upload
+def predict_file(file_obj, threshold=0.3):
+    df = pd.read_csv(file_obj.name)
+    if 'text' not in df.columns:
+        return "CSV must have a 'text' column."
+    texts = df['text'].astype(str).tolist()
+    results = predict(texts, threshold)
+    df['emotions'] = results
+    out_path = "predictions.csv"
+    df.to_csv(out_path, index=False)
+    return out_path
+
 with gr.Blocks() as demo:
     gr.Markdown("# 👀 GoEmotions Multi-Label Demo")
-    inp = gr.Textbox(label="Enter text", lines=3, placeholder="How are you feeling today?")
+
     thr = gr.Slider(0.1, 0.9, 0.3, label="Threshold")
-    out = gr.Textbox(label="Predicted emotions")
-    btn = gr.Button("Analyze")
-    btn.click(fn=predict, inputs=[inp, thr], outputs=out)
+
+    with gr.Tab("Paste Text (one per line)"):
+        inp = gr.Textbox(label="Enter texts (one per line)", lines=10, placeholder="Enter sentences here")
+        out = gr.Textbox(label="Predicted emotions")
+        btn = gr.Button("Analyze")
+        btn.click(fn=predict_bulk, inputs=[inp, thr], outputs=out)
+
+    with gr.Tab("Upload CSV"):
+        file_inp = gr.File(label="Upload CSV with a 'text' column")
+        out_file = gr.File(label="Download CSV with emotions")
+        file_btn = gr.Button("Analyze CSV")
+        file_btn.click(fn=predict_file, inputs=[file_inp, thr], outputs=out_file)
 
 demo.launch()