Update app.py

app.py

@@ -1,29 +1,20 @@
 import gradio as gr
-from transformers import Conversation, pipeline
-from wolframclient.evaluation import WolframLanguageSession
-from wolframclient.language import wl, wlexpr
+import wolframalpha
 
-# Initialize Hugging Face model
-conversational_pipeline = pipeline("text2text-generation", model="EleutherAI/chatgpt-3.5-turbo")
+app = gr.App(title="Math Teacher", description="A simple math teacher that can help you solve math problems.")
 
-# Initialize Wolfram session
-wolfram_session = WolframLanguageSession()
-wolfram_session.start()
+@app.callback(
+    output="text",
+    inputs="text",
+)
+def solve_math_problem(math_problem):
+    """Solve a math problem using Wolfram Alpha."""
+    app_id = "" # Your Wolfram Alpha App ID token here
+    client = wolframalpha.Client(app_id)
+    try:
+        result = client.query(math_problem)
+        return result.pods[0].text
+    except Exception:
+        return "Sorry, I couldn't solve your math problem."
 
-def chat_with_gpt3_and_wolfram(input_text):
-    # Create a conversation with the input text
-    conversation = Conversation(input_text)
-
-    # Generate a response using the Hugging Face model
-    response = conversational_pipeline(conversation)
-
-    # If the response contains a question, use the Wolfram API to answer it
-    if "?" in response:
-        wolfram_response = wolfram_session.evaluate(wl.WolframAlpha(response, "Result"))
-        return wolfram_response
-    else:
-        return response
-
-# Create Gradio interface
-iface = gr.Interface(fn=chat_with_gpt3_and_wolfram, inputs="text", outputs="text")
-iface.launch()
+app.launch()