Update app.py

app.py

@@ -53,46 +53,46 @@ def getPrediction(input):
 def getSentiment(idx):
    return {0: "Negative", 1: "Positive", 2: "Neutral"}.get(idx, "Neutral")
        
-# # Streamlit UI
-# st.title("Sentiment Analysis")
-# text = st.text_area("Enter Text...")
-
-# if text:
-#     prediction, confidence_score = getPrediction([text])  # Modify if preprocessing is needed
-#     # Convert prediction to a human-readable format
-#     response = {"prediction": getSentiment(prediction[0]) + " Statement",
-#                 "confidence": "{:.2f}".format(float(confidence_score[0] * 100)) + "%"}  # Adjust as necessary for output formatting
-#     st.json(response)
-
-# # Add a POST endpoint
-# def api_predict(text):
-#     prediction, confidence_score = getPrediction(text)
-#     return {
-#         "prediction": getSentiment(prediction) + " Statement",
-#         "confidence": f"{confidence_score * 100:.2f}%"
-#     }
-
-# st.query_params(api_predict=api_predict)
-
 # Streamlit UI
 st.title("Sentiment Analysis")
+text = st.text_area("Enter Text...")
 
-# Use st.query_params to get the 'text' parameter from the URL
-query_text = st.query_params.get("text", [""])[0]  # Accessing 'text' from query params
+if text:
+    prediction, confidence_score = getPrediction([text])  # Modify if preprocessing is needed
+    # Convert prediction to a human-readable format
+    response = {"prediction": getSentiment(prediction[0]) + " Statement",
+                "confidence": "{:.2f}".format(float(confidence_score[0] * 100)) + "%"}  # Adjust as necessary for output formatting
+    st.json(response)
 
-if query_text:
-    # If text is passed in the URL, process it
-    prediction, confidence_score = getPrediction(query_text)
-    st.json({
+# Add a POST endpoint
+def api_predict(text):
+    prediction, confidence_score = getPrediction(text)
+    return {
         "prediction": getSentiment(prediction) + " Statement",
         "confidence": f"{confidence_score * 100:.2f}%"
-    })
-else:
-    # Normal Streamlit app interface for manual text entry
-    input_text = st.text_area("Enter Text for Sentiment Analysis")
-    if input_text:
-        prediction, confidence_score = getPrediction(input_text)
-        st.json({
-            "prediction": getSentiment(prediction) + " Statement",
-            "confidence": f"{confidence_score * 100:.2f}%"
-        })
+    }
+
+st.experimental_set_query_params(api_predict=api_predict)
+
+# # Streamlit UI
+# st.title("Sentiment Analysis")
+
+# # Use st.query_params to get the 'text' parameter from the URL
+# query_text = st.query_params.get("text", [""])[0]  # Accessing 'text' from query params
+
+# if query_text:
+#     # If text is passed in the URL, process it
+#     prediction, confidence_score = getPrediction(query_text)
+#     st.json({
+#         "prediction": getSentiment(prediction) + " Statement",
+#         "confidence": f"{confidence_score * 100:.2f}%"
+#     })
+# else:
+#     # Normal Streamlit app interface for manual text entry
+#     input_text = st.text_area("Enter Text for Sentiment Analysis")
+#     if input_text:
+#         prediction, confidence_score = getPrediction(input_text)
+#         st.json({
+#             "prediction": getSentiment(prediction) + " Statement",
+#             "confidence": f"{confidence_score * 100:.2f}%"
+#         })