Update app.py

app.py

@@ -46,82 +46,110 @@ def get_active_llm_provider():
 
 def get_llm_summary(prompt: str, context: str = "") -> str:
     """
-    Generate an expert summary using the AI/ML API.
-    Uses AI_ML_API_KEY (reads from Streamlit secrets or env var).
-    Robustly handles 200/201 responses and different response content shapes.
+    Robust LLM summary using AIML /responses.
+    - Requests text response_format and low reasoning effort by default.
+    - Parses: output_text, output -> message/content (output_text/text), and reasoning -> summary.
+    - If only reasoning is returned, retries once with safer params.
     """
-    full_prompt = f"{context}\n\n{prompt}" if context else prompt
+    full_prompt = f"{context}
 
-    # Prefer Streamlit secret then env var (keeps same convention used elsewhere)
+{prompt}" if context else prompt
     api_key = get_env_or_secret("AI_ML_API_KEY") or os.getenv("AI_ML_API_KEY")
     if not api_key:
         return (
-            "AI Analysis unavailable — AI_ML_API_KEY not configured.\n"
+            "AI Analysis unavailable — AI_ML_API_KEY not configured.
+"
             "Go to Settings → Secrets → Create secret 'AI_ML_API_KEY' with your AI/ML API key."
         )
 
     url = "https://api.aimlapi.com/v1/responses"
-    headers = {
-        "Content-Type": "application/json",
-        "Authorization": f"Bearer {api_key}"
-    }
-    payload = {
+    headers = {"Content-Type": "application/json", "Authorization": f"Bearer {api_key}"}
+
+    def call_api(payload):
+        try:
+            r = requests.post(url, headers=headers, json=payload, timeout=90)
+            if r.status_code not in (200, 201):
+                return None, f"AI Analysis Error (AIML API {r.status_code}): {r.text}"
+            return r.json(), None
+        except requests.exceptions.RequestException as e:
+            return None, f"AI Analysis Request Failed (network): {e}"
+        except Exception as e:
+            return None, f"AI Analysis Request Failed (parsing): {e}"
+
+    base_payload = {
         "model": "openai/gpt-5-2025-08-07",
         "input": full_prompt,
-        # Optionally tune these: 'max_output_tokens': 512, 'temperature': 0.7
+        "max_output_tokens": 1024,
+        "temperature": 0.0,
+        "reasoning_effort": "low",
+        "response_format": {"type": "text"},
     }
 
-    try:
-        resp = requests.post(url, headers=headers, json=payload, timeout=60)
-        # AIML API commonly returns 201 on success for /responses
-        if resp.status_code not in (200, 201):
-            return f"AI Analysis Error (AIML API {resp.status_code}): {resp.text}"
-
-        data = resp.json()
-
-        # 1) Try the helper top-level output_text if present (docs show this field).
-        if data.get("output_text"):
-            return data["output_text"]
+    data, error = call_api(base_payload)
+    if error:
+        return error
 
-        # 2) Otherwise, inspect `output` array for message items with content → output_text
-        output_items = data.get("output", []) or []
+    def extract_text_from_response(data_dict):
         texts = []
-        for item in output_items:
-            # item might be 'message' with 'content' list
-            if item.get("type") == "message" and isinstance(item.get("content"), list):
+
+        if data_dict.get("output_text"):
+            texts.append(data_dict["output_text"])
+
+        for item in data_dict.get("output", []) or []:
+            itype = item.get("type", "")
+            if itype == "reasoning":
+                for s in item.get("summary", []) or []:
+                    t = s.get("text") or s.get("summary") or ""
+                    if isinstance(t, str) and t.strip():
+                        texts.append(t.strip())
+            if itype == "message" and isinstance(item.get("content"), list):
                 for c in item["content"]:
-                    if c.get("type") in ("output_text", "text") and c.get("text"):
-                        texts.append(c["text"])
-            # some items can include 'text' directly (defensive)
-            elif isinstance(item.get("text"), str) and item.get("text").strip():
-                texts.append(item.get("text"))
-
-        if texts:
-            return "\n\n".join(texts)
-
-        # 3) Some models may include 'responses' list in other shapes; attempt fallback
-        if "responses" in data and isinstance(data["responses"], list) and len(data["responses"]) > 0:
-            first = data["responses"][0]
-            # try a few typical fields
-            for field in ("text", "output_text", "content"):
-                v = first.get(field)
-                if isinstance(v, str) and v.strip():
-                    return v
-                if isinstance(v, list):
-                    # flatten content lists if present
-                    for el in v:
-                        if isinstance(el, dict) and el.get("text"):
-                            texts.append(el["text"])
-            if texts:
-                return "\n\n".join(texts)
-
-        # If we reach here, the response had no text content we could extract
-        return "AI Analysis returned no text output. Raw response excerpt: " + json.dumps(data)[:1200]
+                    if isinstance(c, dict):
+                        txt = c.get("text") or c.get("output_text") or ""
+                        if isinstance(txt, str) and txt.strip():
+                            texts.append(txt.strip())
+            if isinstance(item.get("text"), str) and item.get("text").strip():
+                texts.append(item.get("text").strip())
+
+        if not texts and "responses" in data_dict:
+            for r in data_dict["responses"]:
+                if isinstance(r, dict):
+                    for f in ("output_text", "text"):
+                        v = r.get(f)
+                        if isinstance(v, str) and v.strip():
+                            texts.append(v.strip())
+                    cont = r.get("content")
+                    if isinstance(cont, list):
+                        for c in cont:
+                            if isinstance(c, dict) and c.get("text"):
+                                texts.append(c["text"].strip())
+        return "
+
+".join(texts).strip()
+
+    out_text = extract_text_from_response(data)
+    if out_text:
+        return out_text
+
+    retry_payload = base_payload.copy()
+    retry_payload.update({
+        "reasoning_effort": "low",
+        "temperature": 0.0,
+        "max_output_tokens": 2048
+    })
+    data2, err2 = call_api(retry_payload)
+    if err2:
+        return err2
+    out_text2 = extract_text_from_response(data2)
+    if out_text2:
+        return out_text2
 
-    except requests.exceptions.RequestException as e:
-        return f"AI Analysis Request Failed (network): {e}"
-    except Exception as e:
-        return f"AI Analysis Request Failed (parsing): {e}"
+    try:
+        return "AI Analysis returned no text output. Raw response excerpt: " + json.dumps(data)[:1600]
+    except Exception:
+        return "AI Analysis returned no text output and response could not be serialized."
+
+# =============================
 
 
 # =============================