Update app.py

app.py

@@ -9,46 +9,65 @@ except ImportError:
 from transformers import pipeline
 import gradio as gr
 
-# Initialize sentiment_analysis as None
-sentiment_analysis = None
+# Initialize models as None
+model1 = None
+model2 = None
 
-# Attempt to load the model and run a test prediction
+# Attempt to load the models and run test predictions
 try:
-    # Explicitly specify the model
-    model_name = "distilbert-base-uncased-finetuned-sst-2-english"
-    print("Loading model...")
-    sentiment_analysis = pipeline("sentiment-analysis", model=model_name)
-    test_output = sentiment_analysis("Testing the model with a simple sentence.")
-    print("Model test output:", test_output)
+    model1_name = "JimminDev/jim-text-class"
+    model2_name = "JimminDev/Depressive-detector"
+    print("Loading models...")
+    
+    model1 = pipeline("text-classification", model=model1_name)
+    test_output1 = model1("Testing the first model with a simple sentence.")
+    print("Model 1 test output:", test_output1)
+    
+    model2 = pipeline("text-classification", model=model2_name)
+    test_output2 = model2("Testing the second model with a simple sentence.")
+    print("Model 2 test output:", test_output2)
 except Exception as e:
-    print(f"Failed to load or run model: {e}")
+    print(f"Failed to load or run models: {e}")
 
-# Prediction function with error handling
-def predict_sentiment(text):
+# Prediction function with model selection and error handling
+def predict_sentiment(text, model_choice):
     try:
-        if sentiment_analysis is None:
-            raise ValueError("Model not loaded.")
-        predictions = sentiment_analysis(text)
+        if model_choice == "Model 1":
+            if model1 is None:
+                raise ValueError("Model 1 not loaded.")
+            predictions = model1(text)
+        elif model_choice == "Model 2":
+            if model2 is None:
+                raise ValueError("Model 2 not loaded.")
+            predictions = model2(text)
+        else:
+            raise ValueError("Invalid model choice.")
+        
         return f"Label: {predictions[0]['label']}, Score: {predictions[0]['score']:.4f}"
     except Exception as e:
         return f"Error processing input: {e}"
 
 # Define example inputs
 examples = [
-    "I absolutely love this product! It has changed my life.",
-    "This is the worst movie I have ever seen. Completely disappointing.",
-    "I'm not sure how I feel about this new update. It has some good points, but also many drawbacks.",
-    "The customer service was fantastic! Very helpful and polite.",
-    "Honestly, this was quite a mediocre experience. Nothing special."
+    ["I absolutely love this product! It has changed my life.", "Model 1"],
+    ["This is the worst movie I have ever seen. Completely disappointing.", "Model 1"],
+    ["I'm not sure how I feel about this new update. It has some good points, but also many drawbacks.", "Model 2"],
+    ["The customer service was fantastic! Very helpful and polite.", "Model 2"],
+    ["Honestly, this was quite a mediocre experience. Nothing special.", "Model 1"]
 ]
 
 # Gradio interface setup
-iface = gr.Interface(fn=predict_sentiment,
-                     title="Sentiment Analysis",
-                     description="Enter text to analyze sentiment. Powered by Hugging Face Transformers.",
-                     inputs="text",
-                     outputs="text",
-                     examples=examples)
+iface = gr.Interface(
+    fn=predict_sentiment,
+    title="Sentiment Analysis",
+    description="Enter text to analyze sentiment. Powered by Hugging Face Transformers.",
+    inputs=[
+        gr.inputs.Textbox(lines=2, placeholder="Enter text here..."),
+        gr.inputs.Radio(choices=["Model 1", "Model 2"], label="Select Model")
+    ],
+    outputs="text",
+    examples=examples
+)
 
 if __name__ == "__main__":
-    iface.launch()
+    iface.launch()