Create app.py

app.py

@@ -0,0 +1,52 @@
+import streamlit as st
+import torch
+from transformers import RobertaForSequenceClassification, RobertaTokenizer
+import pandas as pd
+
+def model1():
+    # Your Model 1 code here
+    st.title("Emotion Analysis")
+    model_path = "mymodel.pth"  # Replace with the actual path to your trained model
+    tokenizer = RobertaTokenizer.from_pretrained(model_path)
+    model = RobertaForSequenceClassification.from_pretrained(model_path)
+
+    # Set the model to evaluation mode
+    model.eval()
+
+    # Labels for your specific task
+    labels = ['surprise', 'fun',  'anger', 'boredom', 'hate', 'neutral',  'worry',  'enthusiasm', 'sadness','relief', 'empty',  'happiness', 'love']  # Replace with your actual label names
+
+    # Streamlit app
+    user_input = st.text_area("Enter text for analysis:")
+    if st.button("Analyze"):
+        if user_input:
+            # Tokenize and preprocess the input
+            input_ids = tokenizer.encode(user_input, return_tensors="pt")
+            # Make prediction
+            with torch.no_grad():
+                output = model(input_ids)
+            # Get predicted probabilities
+            probabilities = torch.sigmoid(output.logits)
+
+            # Check if the lengths match before creating the DataFrame
+            if len(labels) == len(probabilities[0]):
+                # Display the probabilities as individual bars
+                df = pd.DataFrame({
+                    "Label": labels,
+                    "Probability": probabilities[0].tolist()
+                })
+
+                st.bar_chart(df.set_index("Label"))
+
+                # Display the emotion labels and scores
+                st.subheader("Emotion Analysis Output:")
+                for i, result in enumerate(sorted(zip(labels, probabilities[0]), key=lambda x: x[1], reverse=True)):
+                    label, score = result
+                    st.write(f"{i + 1}. {label.capitalize()}: {score:.4f}")
+            else:
+                st.error("Error: The length of labels and probabilities does not match.")
+        else:
+            st.warning("Please enter text for analysis.")
+
+if __name__ == "__main__":
+    model1()