enhance summarize endpoint to dynamically adjust summary length based on input word count and return parameters used

main.py

@@ -5,16 +5,47 @@ from transformers import pipeline
 app = FastAPI()
 summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
 
-
 class TextInput(BaseModel):
     text: str
 
-
 @app.get("/")
 async def root():
     return {"message": "Welcome to the Text Summarization API!"}
 
 @app.post("/summarize")
 async def summarize_text(input: TextInput):
-    summary = (summarizer(input.text, max_length=130, min_length=30, do_sample=False))
-    return {"summary": summary[0]['summary_text']}
+    # Count approximate number of words (could be improved with tokenizer count in the future)
+    word_count = len(input.text.split())
+    
+
+    if word_count < 50:
+        max_length = max(10, word_count // 2)  
+        min_length = max(3, word_count // 4)   
+    elif word_count < 200:
+        max_length = max(50, word_count // 3)
+        min_length = max(15, word_count // 6)
+    else:
+        max_length = max(100, word_count // 4)
+        min_length = max(30, word_count // 8)
+    
+    # Prevent max_length from being too large (BART has token limits)
+    max_length = min(max_length, 1024)
+    
+    # Generate summary with dynamic parameters
+    summary = summarizer(
+        input.text, 
+        max_length=max_length, 
+        min_length=min_length, 
+        do_sample=True,
+        temperature=0.7,
+        num_beams=4
+    )
+    
+    return {
+        "summary": summary[0]["summary_text"],
+        "parameters_used": {
+            "input_word_count": word_count,
+            "max_length": max_length,
+            "min_length": min_length
+        }
+    }