Fix API client to use Anthropic SDK directly

app.py

@@ -8,7 +8,19 @@ Author: Jocelyn Skillman, LMHC
 
 import gradio as gr
 import os
-from openai import OpenAI
+
+# Try to import API clients
+try:
+    import anthropic
+    ANTHROPIC_AVAILABLE = True
+except ImportError:
+    ANTHROPIC_AVAILABLE = False
+
+try:
+    from openai import OpenAI
+    OPENAI_AVAILABLE = True
+except ImportError:
+    OPENAI_AVAILABLE = False
 
 # ARI Engine System Prompt - The meta-prompt that powers tool generation
 ARI_ENGINE_SYSTEM_PROMPT = """You are the ARI Engine — an Assistive Relational Intelligence tool builder designed to help mental health clinicians create AI-powered tools for their practice. You are not a therapist. You are a clinical design collaborator that translates clinician intent into ethically-grounded, trauma-informed AI tool architectures.
@@ -203,33 +215,58 @@ MODALITIES = [
 ]
 
 def get_client():
-    """Initialize OpenAI-compatible client for various providers."""
-    # Try Anthropic first (via OpenAI-compatible endpoint)
-    if os.environ.get("ANTHROPIC_API_KEY"):
-        return OpenAI(
-            api_key=os.environ["ANTHROPIC_API_KEY"],
-            base_url="https://api.anthropic.com/v1/"
-        ), "claude-3-5-sonnet-20241022"
+    """Initialize API client for various providers."""
+    # Try Anthropic first
+    if os.environ.get("ANTHROPIC_API_KEY") and ANTHROPIC_AVAILABLE:
+        return anthropic.Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"]), "claude-sonnet-4-20250514", "anthropic"
 
     # Try OpenAI
-    if os.environ.get("OPENAI_API_KEY"):
-        return OpenAI(
-            api_key=os.environ["OPENAI_API_KEY"]
-        ), "gpt-4o"
+    if os.environ.get("OPENAI_API_KEY") and OPENAI_AVAILABLE:
+        return OpenAI(api_key=os.environ["OPENAI_API_KEY"]), "gpt-4o", "openai"
 
     # Try HuggingFace Inference
-    if os.environ.get("HF_TOKEN"):
+    if os.environ.get("HF_TOKEN") and OPENAI_AVAILABLE:
         return OpenAI(
             api_key=os.environ["HF_TOKEN"],
             base_url="https://api-inference.huggingface.co/v1/"
-        ), "meta-llama/Llama-3.1-70B-Instruct"
+        ), "meta-llama/Llama-3.1-70B-Instruct", "openai"
+
+    return None, None, None
+
+
+def call_llm(client, model, provider, system_prompt, messages):
+    """Call LLM with provider-specific formatting."""
+    if provider == "anthropic":
+        # Convert messages to Anthropic format
+        anthropic_messages = []
+        for msg in messages:
+            anthropic_messages.append({
+                "role": msg["role"],
+                "content": msg["content"]
+            })
+
+        response = client.messages.create(
+            model=model,
+            max_tokens=4000,
+            system=system_prompt,
+            messages=anthropic_messages
+        )
+        return response.content[0].text
 
-    return None, None
+    else:  # OpenAI-compatible
+        all_messages = [{"role": "system", "content": system_prompt}] + messages
+        response = client.chat.completions.create(
+            model=model,
+            messages=all_messages,
+            max_tokens=4000,
+            temperature=0.7
+        )
+        return response.choices[0].message.content
 
 def generate_tool(description, category, population, modality, risk_level, additional_context, history):
     """Generate an ARI tool based on clinician input."""
 
-    client, model = get_client()
+    client, model, provider = get_client()
 
     if not client:
         return """## API Key Required
@@ -241,7 +278,7 @@ To generate tools, please set one of these environment variables:
 
 For local testing, you can set these in a `.env` file or export them in your terminal.
 
-For HuggingFace Spaces deployment, add these as secrets in your Space settings.""", "", "", history
+For HuggingFace Spaces deployment, add these as secrets in your Space settings.""", "", "", "", history
 
     # Build the user prompt
     user_prompt = f"""A clinician is requesting help building an ARI tool. Please guide them through the process.
@@ -272,7 +309,7 @@ Format your output clearly with markdown headers for each section.
 Remember: Every tool must pass the ARI Litmus Test - building capacity for human relationship, having clear exits toward humans, being safe for crisis, preventing dependency, and honoring clinical expertise."""
 
     # Build conversation history for context
-    messages = [{"role": "system", "content": ARI_ENGINE_SYSTEM_PROMPT}]
+    messages = []
 
     for h in history:
         messages.append({"role": "user", "content": h[0]})
@@ -282,14 +319,7 @@ Remember: Every tool must pass the ARI Litmus Test - building capacity for human
     messages.append({"role": "user", "content": user_prompt})
 
     try:
-        response = client.chat.completions.create(
-            model=model,
-            messages=messages,
-            max_tokens=4000,
-            temperature=0.7
-        )
-
-        assistant_response = response.choices[0].message.content
+        assistant_response = call_llm(client, model, provider, ARI_ENGINE_SYSTEM_PROMPT, messages)
 
         # Update history
         new_history = history + [[user_prompt, assistant_response]]
@@ -319,12 +349,12 @@ Remember: Every tool must pass the ARI Litmus Test - building capacity for human
 def continue_conversation(user_message, history):
     """Continue the conversation with the ARI Engine."""
 
-    client, model = get_client()
+    client, model, provider = get_client()
 
     if not client:
         return "Please set an API key to continue.", history
 
-    messages = [{"role": "system", "content": ARI_ENGINE_SYSTEM_PROMPT}]
+    messages = []
 
     for h in history:
         messages.append({"role": "user", "content": h[0]})
@@ -334,14 +364,7 @@ def continue_conversation(user_message, history):
     messages.append({"role": "user", "content": user_message})
 
     try:
-        response = client.chat.completions.create(
-            model=model,
-            messages=messages,
-            max_tokens=4000,
-            temperature=0.7
-        )
-
-        assistant_response = response.choices[0].message.content
+        assistant_response = call_llm(client, model, provider, ARI_ENGINE_SYSTEM_PROMPT, messages)
         new_history = history + [[user_message, assistant_response]]
 
         return assistant_response, new_history
@@ -355,25 +378,15 @@ def preview_tool(system_prompt, test_input):
     if not system_prompt.strip():
         return "Please generate a tool first, then copy the System Prompt here to preview it."
 
-    client, model = get_client()
+    client, model, provider = get_client()
 
     if not client:
         return "Please set an API key to preview tools."
 
-    messages = [
-        {"role": "system", "content": system_prompt},
-        {"role": "user", "content": test_input}
-    ]
+    messages = [{"role": "user", "content": test_input}]
 
     try:
-        response = client.chat.completions.create(
-            model=model,
-            messages=messages,
-            max_tokens=1000,
-            temperature=0.7
-        )
-
-        return response.choices[0].message.content
+        return call_llm(client, model, provider, system_prompt, messages)
 
     except Exception as e:
         return f"Error in preview: {str(e)}"

requirements.txt

@@ -1,3 +1,4 @@
 gradio>=4.44.0,<5.0.0
+anthropic>=0.39.0
 openai>=1.0.0
 huggingface_hub>=0.24.0,<1.0.0