Update app.py

app.py

@@ -10,13 +10,17 @@ import wikipediaapi
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 HF_TOKEN = os.getenv("HF_TOKEN")
 
+# Supported conversational/text-gen models in order of preference
 CONVERSATIONAL_MODELS = [
     "deepseek-ai/DeepSeek-LLM",
     "HuggingFaceH4/zephyr-7b-beta",
     "mistralai/Mistral-7B-Instruct-v0.2"
 ]
 
-wiki_api = wikipediaapi.Wikipedia(language="en", user_agent="SmartAgent/1.0 (chockqoteewy@gmail.com)")
+wiki_api = wikipediaapi.Wikipedia(
+    language="en", 
+    user_agent="SmartAgent/1.0 (chockqoteewy@gmail.com)"
+)
 
 # ==== SEARCH TOOLS ====
 def duckduckgo_search(query):
@@ -28,16 +32,29 @@ def wikipedia_search(query):
     page = wiki_api.page(query)
     return page.summary if page.exists() and page.summary else "No Wikipedia page found."
 
+# ==== HUGGING FACE CHAT/TEXT-GEN TOOL ====
 def hf_chat_model(question):
     last_error = ""
     for model_id in CONVERSATIONAL_MODELS:
         try:
             hf_client = InferenceClient(model_id, token=HF_TOKEN)
-            result = hf_client.conversational(
-                messages=[{"role": "user", "content": question}],
-                max_new_tokens=384,
-            )
-            # Acceptable result types: dict, obj with 'generated_text', or str
+            # Try conversational endpoint first, if it exists
+            if hasattr(hf_client, "conversational"):
+                try:
+                    result = hf_client.conversational(
+                        messages=[{"role": "user", "content": question}],
+                        max_new_tokens=384,
+                    )
+                    if isinstance(result, dict) and "generated_text" in result:
+                        return f"[{model_id}] " + result["generated_text"]
+                    elif hasattr(result, "generated_text"):
+                        return f"[{model_id}] " + result.generated_text
+                    elif isinstance(result, str):
+                        return f"[{model_id}] " + result
+                except Exception as e:
+                    last_error += f"({model_id}: conversational) {e}\n"
+            # Fall back to text_generation for all other models
+            result = hf_client.text_generation(question, max_new_tokens=384)
             if isinstance(result, dict) and "generated_text" in result:
                 return f"[{model_id}] " + result["generated_text"]
             elif hasattr(result, "generated_text"):
@@ -47,8 +64,8 @@ def hf_chat_model(question):
             else:
                 return f"[{model_id}] " + str(result)
         except Exception as e:
-            last_error = f"({model_id}) {e}"
-    return f"HF LLM error: {last_error}"
+            last_error += f"({model_id}: text_generation) {e}\n"
+    return f"HF LLM error: {last_error or 'No models produced output.'}"
 
 # ==== SMART AGENT ====
 class SmartAgent:
@@ -58,7 +75,9 @@ class SmartAgent:
     def __call__(self, question: str) -> str:
         q_lower = question.lower()
         # DuckDuckGo for current/event/internet questions
-        if any(term in q_lower for term in ["current", "latest", "2024", "2025", "who is the president", "recent", "live", "now", "today"]):
+        if any(term in q_lower for term in [
+            "current", "latest", "2024", "2025", "who is the president", "recent", "live", "now", "today"
+        ]):
             duck_result = duckduckgo_search(question)
             if duck_result and "No DuckDuckGo" not in duck_result:
                 return duck_result