create app.py

app.py

@@ -0,0 +1,53 @@
+import streamlit as st
+import requests
+
+st.title("How do you feel ?")
+
+API_URL = "https://api-inference.huggingface.co/models/lxyuan/distilbert-base-multilingual-cased-sentiments-student"
+headers = {"Authorization": "Bearer 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"}
+
+def query(payload):
+	response = requests.post(API_URL, headers=headers, json=payload)
+	return response.json()
+
+
+def analyze_sentiment_Transformer(text):
+    # Perform sentiment analysis
+    results = query(text)
+    first_dict = results[0]
+    first_label = first_dict[0]
+    sentiment = first_label['label']
+    score = first_label['score']
+    return {
+         "sentiment":sentiment,
+         "score":score
+    }
+
+
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+if prompt := st.chat_input("Tell me how you feel, whatever language"):
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    with st.chat_message("assistant"):
+        response = analyze_sentiment_Transformer(prompt)
+        sentiment = response['sentiment']
+        score = response['score']
+        if(sentiment == "positive"):
+            st.balloons()
+            fullresponse = f'happy to know you feel good with a score of '+str(score)
+        elif (sentiment == "negative"):
+            fullresponse = f'sorry to know you feel bad with a score of '+str(score)
+            st.snow()
+        else:
+            fullresponse = f'Ok you feel neutral, hoping the best '+str(score)
+        
+        st.markdown(fullresponse)
+    st.session_state.messages.append({"role": "assistant", "content": response})