Update app.py

app.py

@@ -0,0 +1,60 @@
+import requests
+import tiktoken
+import streamlit as st
+import json
+
+# Define the API URL
+API_URL = "https://startrz-proagents.hf.space/api/v1/prediction/9ad2e52d-052f-44e5-b3a0-7fd432d86506"
+
+# Function to query the API
+def query(payload):
+    response = requests.post(API_URL, json=payload)
+    return response.json()
+
+# Function to calculate token usage using tiktoken
+def calculate_tokens(text, model="gpt-3.5-turbo"):
+    encoding = tiktoken.encoding_for_model(model)
+    tokens = encoding.encode(text)
+    return tokens
+
+# Streamlit app
+st.title("API Query with Token Calculation")
+
+# User input for the question
+question = st.text_input("Enter your question:", "Hey, how are you?")
+
+# Button to trigger the API call and calculations
+if st.button("Submit"):
+    with st.spinner("Calculating..."):
+        # Payload for the API
+        payload = {"question": question}
+
+        # Query the API
+        response_data = query(payload)
+
+        # Calculate input tokens
+        input_tokens = calculate_tokens(str(payload))
+
+        # Calculate output tokens from the response (assuming 'result' field contains the output text)
+        output_text = response_data.get("result", "")
+        output_tokens = calculate_tokens(output_text)
+
+        # Total tokens
+        total_tokens = len(input_tokens) + len(output_tokens)
+
+        # Structure the data with token information
+        data = {
+            "response": response_data,
+            "input_tokens": len(input_tokens),
+            "output_tokens": len(output_tokens),
+            "total_tokens": total_tokens
+        }
+
+        # Display the data
+        st.subheader("Response Data")
+        st.json(data)
+
+        st.subheader("Token Information")
+        st.write(f"Input Tokens: {len(input_tokens)}")
+        st.write(f"Output Tokens: {len(output_tokens)}")
+        st.write(f"Total Tokens: {total_tokens}")