Add OpenAI compatibility fallback

README.md

@@ -25,6 +25,7 @@ Use a free Python container host and keep the service public:
 3. Set `MINDVIS_REPO=/app/mindVisualizer`.
 4. Set `CORS_ORIGINS=https://pixedar.github.io,http://localhost:3000`.
 5. In the GitHub Pages build, set `NEXT_PUBLIC_MINDVIS_API_URL=https://your-backend-url`.
+6. Optional: set `OPENAI_API_KEY` as a Space secret and `OPENAI_MODEL` as a Space variable.
 
 Current Space:
 

app.py

@@ -129,9 +129,7 @@ def explain(req: ExplainRequest):
 
     text = ""
     if req.callLlm and os.environ.get("OPENAI_API_KEY"):
-        from src.roi_flow import ROIFlowLLM
-
-        text = ROIFlowLLM(debug=False).interpret_roi_flow(context)
+        text = call_openai_interpreter(context)
 
     if not text:
         analysis = analyzer.analyze_flow_pattern(delta)
@@ -166,3 +164,46 @@ def denormalized(points: np.ndarray, amin: np.ndarray, span: np.ndarray) -> np.n
 
 def normalized(points: np.ndarray, amin: np.ndarray, span: np.ndarray) -> np.ndarray:
     return (points.astype(np.float32) - amin[None, :]) / span[None, :]
+
+
+def call_openai_interpreter(context: str) -> str:
+    from openai import OpenAI
+
+    model = os.environ.get("OPENAI_MODEL", "gpt-4o-mini")
+    instructions = (
+        "You are a neuroscientist interpreting brain state dynamics from a "
+        "manifold flow simulation. The user traced a path through a learned "
+        "neural manifold. You are given how each ROI's contribution changed "
+        "along this path.\n\n"
+        "The delta values do not mean simple activation. They measure how each "
+        "ROI's contribution to the overall brain state shifted. Positive means "
+        "the region became a stronger contributor; negative means it became a "
+        "weaker contributor.\n\n"
+        "Do not list every ROI. Synthesize the spatial pattern, directionality, "
+        "and network shift into one coherent interpretation. Keep the response "
+        "to 2 concise paragraphs."
+    )
+
+    try:
+        client = OpenAI(timeout=60.0)
+        if hasattr(client, "responses"):
+            response = client.responses.create(
+                model=model,
+                instructions=instructions,
+                input=context,
+                max_output_tokens=900,
+            )
+            return (response.output_text or "").strip()
+
+        response = client.chat.completions.create(
+            model=model,
+            messages=[
+                {"role": "system", "content": instructions},
+                {"role": "user", "content": context},
+            ],
+            max_tokens=900,
+            temperature=0.3,
+        )
+        return (response.choices[0].message.content or "").strip()
+    except Exception as exc:
+        return f"[GPT ERROR] {exc}"