ui changes

app.py

@@ -1,7 +1,32 @@
+# import gradio as gr
+
+# def greet(name):
+#     return "Hello " + name + "!!"
+
+# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+# iface.launch()
+# model = AutoModelForSequenceClassification.from_pretrained("tabibu-ai/mental-health-chatbot")
+
+# write a gradio interface for tabibu-ai/mental-health-chatbot in huggingfacehub
+
+# Path: app.py
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("tabibu-ai/mental-health-chatbot")
+model = AutoModelForSequenceClassification.from_pretrained("tabibu-ai/mental-health-chatbot")
+
+def classify_text(inp):
+    input_ids = tokenizer.encode(inp, return_tensors='pt')
+    output = model(input_ids)
+    return output.logits.argmax().item()
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+iface = gr.Interface(fn=classify_text, inputs="text", outputs="label",
+                        interpretation="default", examples=[
+                            ["I am feeling depressed"],
+                            ["I am feeling anxious"],
+                            ["I am feeling stressed"],
+                            ["I am feeling sad"],
+                        ])
+iface.launch()

model.ipynb

@@ -74,6 +74,8 @@
     "\n",
     "model.push_to_hub(\"tabibu-ai/mental-health-chatbot\")\n",
     "\n",
+    "# load model from hub\n",
+    "\n",
     "# Step 5: Evaluate the model\n",
     "y_pred = model.predict(X_test)\n",
     "accuracy = accuracy_score(y_test, y_pred)\n",