update hf_client

phase/Student_view/chatbot.py

@@ -32,16 +32,42 @@ def _trim_turn(text: str) -> str:
             return text.split(cp, 1)[0].strip()
     return text.strip()
 
+def _history_as_chat_messages(messages: list[dict]) -> list[dict]:
+    # Convert your session history to HF chat format
+    msgs = [{"role": "system", "content": TUTOR_PROMPT}]
+    for m in messages:
+        txt = (m.get("text") or "").strip()
+        if not txt:
+            continue
+        role = "assistant" if m.get("sender") == "assistant" else "user"
+        msgs.append({"role": role, "content": txt})
+    return msgs
+
+def _extract_chat_text(chat_resp) -> str:
+    # Works for both dict-like and object-like responses
+    try:
+        # huggingface_hub >= 0.23 style
+        return chat_resp.choices[0].message["content"] if isinstance(chat_resp.choices[0].message, dict) \
+            else chat_resp.choices[0].message.content
+    except Exception:
+        # fallback for dict payloads
+        try:
+            return chat_resp["choices"][0]["message"]["content"]
+        except Exception:
+            return str(chat_resp)
+
 def _reply_with_hf():
     if "client" not in globals():
         raise RuntimeError("HF client not initialized")
 
+    # Text-generation prompt (for providers that support it)
     convo = _format_history_for_flan(st.session_state.get("messages", []))
-    prompt = f"{TUTOR_PROMPT}\n\n{convo}\n\nTutor:"
+    tg_prompt = f"{TUTOR_PROMPT}\n\n{convo}\n\nTutor:"
 
     try:
-        response = client.text_generation(
-            prompt,
+        # 1) Try text-generation first (many backends support this)
+        resp = client.text_generation(
+            tg_prompt,
             max_new_tokens=220,
             temperature=0.2,
             top_p=0.9,
@@ -49,9 +75,25 @@ def _reply_with_hf():
             return_full_text=False,
             stream=False,
         )
-        if not isinstance(response, str):
-            response = response.get("generated_text") or str(response)
-        return _trim_turn(response)
+        text = resp.get("generated_text") if isinstance(resp, dict) else resp
+        return _trim_turn(str(text or "").strip())
+
+    except ValueError as ve:
+        # 2) If the provider says "Supported task: conversational", use HF chat
+        if "Supported task: conversational" in str(ve):
+            msgs = _history_as_chat_messages(st.session_state.get("messages", []))
+            chat = client.chat.completions.create(
+                model=GEN_MODEL,          # still HF, not OpenAI
+                messages=msgs,
+                max_tokens=220,
+                temperature=0.2,
+                top_p=0.9,
+            )
+            return _trim_turn(_extract_chat_text(chat))
+
+        # Some other ValueError — rethrow with the original message
+        raise
+
     except Exception as e:
         err_text = ''.join(traceback.format_exception_only(type(e), e)).strip()
         raise RuntimeError(f"Hugging Face API Error: {err_text}")