Initial commit

app.py

@@ -0,0 +1,90 @@
+import gradio as gr
+from transformers import pipeline
+import plotly.express as px
+import pandas as pd
+
+MODEL_LIST = [
+    "thethinkmachine/immune-resin",
+]
+
+pipeline_cache = {}
+
+def get_pipeline(model_name):
+    if model_name not in pipeline_cache:
+        pipeline_cache[model_name] = pipeline(
+            "text-classification",
+            model=model_name,
+            return_all_scores=True
+        )
+    return pipeline_cache[model_name]
+
+
+def classify(text, model_name, chart_type, threshold):
+    if not text.strip():
+        return "", None, None
+
+    clf = get_pipeline(model_name)
+    results = clf(text)[0]
+    df = pd.DataFrame(results)
+
+    # Sort labels by probability
+    df = df.sort_values(by="score", ascending=False).reset_index(drop=True)
+
+    # Highlight labels above threshold
+    df["highlight"] = df["score"].apply(lambda x: "High" if x >= threshold else "Low")
+
+    if chart_type == "Radar Chart":
+        fig = px.line_polar(df, r="score", theta="label", line_close=True,
+                            color="highlight",
+                            title=f"Label Probabilities - {model_name}")
+        fig.update_traces(fill='toself')
+        fig.update_layout(polar=dict(radialaxis=dict(range=[0, 1])))
+    else:
+        fig = px.bar(df, x="label", y="score", color="highlight",
+                      title=f"Label Probabilities - {model_name}")
+        fig.update_layout(yaxis=dict(range=[0, 1]))
+
+    return df, fig, None
+
+
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# LetMeGuessYourMood-5000")
+
+    with gr.Row():
+        model_dropdown = gr.Dropdown(
+            choices=MODEL_LIST,
+            label="Select Model",
+            value=MODEL_LIST[0]
+        )
+        chart_dropdown = gr.Dropdown(
+            choices=["Radar Chart", "Bar Chart"],
+            label="Chart Type",
+            value="Radar Chart"
+        )
+        threshold_slider = gr.Slider(0, 1, value=0.5, step=0.01, label="Highlight Threshold")
+
+    text_input = gr.Textbox(label="Input Text")
+
+    with gr.Row():
+        output_table = gr.DataFrame(label="Scores Table")
+        output_plot = gr.Plot(label="Probability Chart")
+
+    classify_btn = gr.Button("Run Classification")
+
+    classify_btn.click(
+        classify,
+        inputs=[text_input, model_dropdown, chart_dropdown, threshold_slider],
+        outputs=[output_table, output_plot, None]
+    )
+
+    gr.Examples(
+        examples=[
+            "Sorry for not uninstalling you faster",
+            "hurr durr I like using reddit, and anyone who doesn't agree with me is a retard",
+            "Considering I haven't eaten or drunk anything in about twenty hours, plus I only slept for about four of them before getting back to beating it, I'm gonna say too much. My head hurts.",
+        ],
+        inputs=[text_input]
+    )
+
+if __name__ == "__main__":
+    demo.launch()