Update app.py

app.py

@@ -21,26 +21,24 @@ qa_relevance = Feedback(feedback_openai.relevance, name="Answer Relevance").on_i
 # Create a Tru object
 tru = Tru()
 
-# Initialize the HuggingFacePipeline for local LLM
-local_llm = HuggingFacePipeline.from_model_id(
-    model_id="Tonic/stablemed",
-    task="text-generation",
-    model_kwargs={"temperature": 0.2, "top_p": 0.95, "max_length": 256}
-)
-
 # Set the window memory to go back 4 turns
 window_memory = ConversationBufferWindowMemory(k=4)
 
-# Create the ConversationChain with the given window memory
+# Define a custom function to interact with the Gradio client
+def gradio_client_interaction(prompt):
+    client = Client("https://tonic-stablemed-chat.hf.space/")
+    result = client.predict(prompt, prompt, api_name="/predict")
+    return result['data'][0]  # Assuming the response format
+
+# Create the ConversationChain with the custom function
 conversation = ConversationChain(
-    llm=local_llm,
+    llm=gradio_client_interaction,
     verbose=True,
     memory=window_memory
 )
 
-# Update the conversation prompt template to prime it as a gardening expert
+# Update the conversation prompt template
 conversation.prompt.template = '''The following is a consult between a clinical consultant and a public health and medical expert. The AI is an expert on medicine and public health and gives recommendations specific to location and conditions. If the AI does not know the answer to a question, it truthfully says it does not know.
-
 Current conversation:
 {history}
 Human: {input}
@@ -49,17 +47,13 @@ AI:'''
 # Wrap the conversation with TruChain to instrument it
 tc_conversation = tru.Chain(conversation, app_id='Trulens-StableMed', feedbacks=[qa_relevance])
 
-# Initialize Gradio Client
-client = Client("https://tonic-stablemed-chat.hf.space/")
-
 # Make a prediction using the wrapped conversation
-result = client.predict(
-    "Howdy!",  # str in 'user_input' Textbox component
-    "Howdy!",  # str in 'system_prompt' Textbox component
-    api_name="/predict"
-)
+user_input = "Howdy!"
+system_prompt = "Howdy!"
+result = tc_conversation.generate(user_input, system_prompt=system_prompt)
 
 # Print the result
 print(result)
 
+# Run the TruLens dashboard
 tru.run_dashboard()