Update posts_categorization.py

posts_categorization.py

@@ -16,6 +16,9 @@ class SummaryOutput(BaseModel):
 class ClassificationOutput(BaseModel):
     category: str
 
+PRIMARY_SUMMARIZER_MODEL = "deepseek-r1-distill-llama-70b" 
+FALLBACK_SUMMARIZER_MODEL = "llama-3.3-70b-versatile"
+
 # Summarize post text
 def summarize_post(text):
     if pd.isna(text) or text is None:
@@ -32,21 +35,40 @@ def summarize_post(text):
     """
 
     try:
+        # Attempt with primary model
+        print(f"Attempting summarization with primary model: {PRIMARY_SUMMARIZER_MODEL}")
         response = client.chat.completions.create(
-            model="deepseek-r1-distill-llama-70b",
+            model=PRIMARY_SUMMARIZER_MODEL,
             response_model=SummaryOutput,
-            messages=[
-                {"role": "system", "content": "You are a precise summarizer. Only return a JSON object with a 'summary' string."},
-                {"role": "user", "content": prompt}
-            ],
+            messages=messages,
             temperature=0.3
         )
         return response.summary
-    except Exception as e:
-        print(f"Summarization error: {e}")
+    except RateLimitError:
+        print(f"Rate limit hit for primary summarizer model: {PRIMARY_SUMMARIZER_MODEL}. Trying fallback: {FALLBACK_SUMMARIZER_MODEL}")
+        try:
+            # Attempt with fallback model
+            response = client.chat.completions.create(
+                model=FALLBACK_SUMMARIZER_MODEL,
+                response_model=SummaryOutput,
+                messages=messages,
+                temperature=0.3 # Keep temperature consistent or adjust as needed for fallback
+            )
+            print(f"Summarization successful with fallback model: {FALLBACK_SUMMARIZER_MODEL}")
+            return response.summary
+        except RateLimitError as rle_fallback:
+            print(f"Rate limit hit for fallback summarizer model ({FALLBACK_SUMMARIZER_MODEL}): {rle_fallback}. Summarization failed.")
+            return None
+        except Exception as e_fallback:
+            print(f"Error during summarization with fallback model ({FALLBACK_SUMMARIZER_MODEL}): {e_fallback}")
+            return None
+    except Exception as e_primary:
+        print(f"Error during summarization with primary model ({PRIMARY_SUMMARIZER_MODEL}): {e_primary}")
+        # You could also try fallback here for non-rate-limit errors if desired
         return None
 
 
+
 # Classify post summary into structured categories
 def classify_post(summary, labels):
     if pd.isna(summary) or summary is None: