changes

app.py

@@ -1,49 +1,54 @@
+# app.py
 import gradio as gr
-import joblib, numpy as np
+import joblib
+import numpy as np
 from pathlib import Path
 
-VECTORIZER = joblib.load("vectorizer.joblib")
-CANDIDATES = ["knn_model", "svm_model_rbf", "random_forest_model", "logistic_model"]
-models = {}
+# ---- find vectorizer in root or content ----
+def find_file(name):
+    for p in [Path("."), Path("content")]:
+        f = p / name
+        if f.exists():
+            return f
+    raise FileNotFoundError(f"Can't find {name} in repo root or ./content")
 
-for name in CANDIDATES:
-    p = Path(f"/content/{name}.joblib")
-    if p.exists():
-        models[name] = joblib.load(p)
+VECTORIZER = joblib.load(find_file("vectorizer.joblib"))
 
-if not models:
-    raise RuntimeError("No models found. Put joblib models in ./content/")
+# ---- discover models (exclude vectorizer) in root + content ----
+models = {}
+for folder in [Path("."), Path("content")]:
+    for p in folder.glob("*.joblib"):
+        if p.name == "vectorizer.joblib":
+            continue
+        try:
+            obj = joblib.load(p)
+            if hasattr(obj, "predict"):
+                models[p.stem] = obj
+        except Exception:
+            pass
 
-LABELS = ["Negative Comment", "Positive Comment"]
+if not models:
+    raise RuntimeError("No models found. Place your *.joblib next to app.py or in ./content")
 
 def predict(text, model_name):
     if not text.strip():
         return ""
-
     X = VECTORIZER.transform([text])
-    model = models[model_name]
-    y_pred = model.predict(X)[0]
-
-    if y_pred == 1:
-        return "Positive Feedback"
-    else:
-        return "Negative Feedback"
+    y = int(models[model_name].predict(X)[0])
+    return "Positive Feedback" if y == 1 else "Negative Feedback"
 
 with gr.Blocks() as demo:
-    gr.Markdown("## Sentiment Demo")
+    gr.Markdown("# Sentiment Demo")
     with gr.Row():
         with gr.Column():
             txt = gr.Textbox(label="Review Comment", lines=6)
-            mdl = gr.Dropdown(
-                choices=list(models.keys()),
-                value=next(iter(models.keys())),
-                label="method"
-            )
+            mdl = gr.Dropdown(choices=sorted(models.keys()),
+                              value=sorted(models.keys())[0],
+                              label="method")
             btn = gr.Button("Submit")
         with gr.Column():
-            pred = gr.Textbox(label="Predicted Sentiment Class")
-
-    btn.click(predict, inputs=[txt, mdl], outputs=[pred])
+            out = gr.Textbox(label="Predicted Sentiment Class")
+    btn.click(predict, [txt, mdl], out)
 
 if __name__ == "__main__":
     demo.launch()

requirements.txt

@@ -1,5 +1,5 @@
-gradio>=4.0
-scikit-learn
+gradio
+scikit-learn==1.6.1
 pandas
 numpy
 joblib