Update app.py

app.py

@@ -1,24 +1,29 @@
 import gradio as gr
-import chatGPT
-import wolframalpha
+from transformers import Conversation, pipeline
+from wolframclient.evaluation import WolframLanguageSession
+from wolframclient.language import wl, wlexpr
 
-# Create a ChatGPT instance
-chatGPT_model = chatGPT.ChatGPT()
+# Initialize Hugging Face model
+conversational_pipeline = pipeline("text2text-generation", model="EleutherAI/chatgpt-3.5-turbo")
 
-# Create a Wolfram Alpha API instance
-wolframalpha_client = wolframalpha.Client()
+# Initialize Wolfram session
+wolfram_session = WolframLanguageSession()
+wolfram_session.start()
 
-# Define the gradio app
-@gr.app(title="ChatGPT with Wolfram Alpha", description="A chat app that uses ChatGPT and Wolfram Alpha")
-def chat_with_wolframalpha(text):
-    # Get the response from ChatGPT
-    gpt_response = chatGPT_model.generate_response(text)
+def chat_with_gpt3_and_wolfram(input_text):
+    # Create a conversation with the input text
+    conversation = Conversation(input_text)
 
-    # Ask Wolfram Alpha for the answer to the question
-    wolfram_response = wolframalpha_client.query(gpt_response)
+    # Generate a response using the Hugging Face model
+    response = conversational_pipeline(conversation)
 
-    # Return the combined response
-    return gpt_response, wolfram_response
+    # 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
 
-# Run the gradio app
-gr.server(chat_with_wolframalpha, port=8000)
+# Create Gradio interface
+iface = gr.Interface(fn=chat_with_gpt3_and_wolfram, inputs="text", outputs="text")
+iface.launch()