Update app.py

app.py

@@ -111,25 +111,17 @@ def extract_hashtags(text):
 # -------------------------------------------------------------------
 
 def get_frame_category_mapping(text):
-    """
-    For each frame defined in frame_categories, this function uses a zero-shot classification
-    approach to assess qualitatively how strongly the text discusses the frame.
-    It builds a batched list of hypothesis templates and returns a mapping from frame to the best label.
-    """
     frames = list(frame_categories.keys())
-    # Build hypothesis templates—one per frame:
-    hypothesis_templates = [f"This text is {{}} about {frame}." for frame in frames]
-    # Repeat the same input text for each frame in the batch.
-    texts = [text] * len(frames)
-    # Batch call: each element of the batch uses its own hypothesis_template.
-    results = classifier(
-        texts,
-        candidate_labels=candidate_labels,
-        hypothesis_template=hypothesis_templates,
-        batch_size=len(frames)  # process all at once
-    )
     mapping = {}
-    for frame, result in zip(frames, results):
+    for frame in frames:
+        # Create a hypothesis template for this frame
+        hypothesis_template = f"This text is {{}} about {frame}."
+        # Run classifier for the given text and hypothesis_template
+        result = classifier(
+            text,
+            candidate_labels=candidate_labels,
+            hypothesis_template=hypothesis_template
+        )
         mapping[frame] = result["labels"][0]
     return mapping