Update app.py

app.py

@@ -94,8 +94,8 @@ def calculate_acreage_and_usable_land(intersections, merged_geometry):
 
 # Step 9: Summarize Data using GPT-2
 def summarize_flood_data_gpt2(flood_zone_areas, non_flooded_area, total_acreage):
-    # Use Hugging Face's hosted pipeline for GPT-2 text generation
-    chatbot = pipeline("summarization", model="t5-small")
+    # Use Hugging Face's hosted pipeline for T5 small
+    summarizer = pipeline("summarization", model="t5-small")
 
     flood_prone_zones = ['A', 'AE', 'AH', 'AO', 'VE']
     flood_zone_summary = "\n".join([f"  Zone {zone}: {flood_zone_areas.get(zone, 0):.2f} acres" for zone in flood_prone_zones])
@@ -105,12 +105,14 @@ def summarize_flood_data_gpt2(flood_zone_areas, non_flooded_area, total_acreage)
     **Usable Area**: {non_flooded_area:.2f} acres
     **Flood-prone Zones**:
     {flood_zone_summary}
-    Summarize the above given data in 2-3 sentences.
+    Summarize the above given data in a 2-3 sentences.
     """
     
-    response = chatbot(prompt, max_length=200, num_return_sequences=1)
+    response = summarizer(prompt, max_length=200, min_length=30, do_sample=False)
     
-    return response[0]['generated_text']
+    # Use summary_text instead of generated_text for T5 model output
+    return response[0]['summary_text']
+
 
 if uploaded_file is not None:
     # Step 1: Read in the shapefiles