Uploading blood request emergency text classifier demo app.py

app.py

@@ -7,7 +7,7 @@ from typing import Dict, Tuple
 from transformers import pipeline
 
 # 2. Define function to use our model on given text
-def blood_request_classifier(text: str) -> Tuple[str, Dict[str, float]]:
+def blood_request_classifier(text: str) -> Dict[str, float]:
     # Set up text classification pipeline
     classifier = pipeline(
         task="text-classification",
@@ -15,26 +15,12 @@ def blood_request_classifier(text: str) -> Tuple[str, Dict[str, float]]:
         device="cuda" if torch.cuda.is_available() else "cpu",
         top_k=None
     )
-
-    # Get outputs from pipeline
-    outputs = classifier(text)[0]
-
-    # Build probability scores dict + find top prediction
-    prob_scores = {}
-    top_label = ""
-    top_score = 0.0
-
+    output_dict = {}
     for item in outputs:
-        label = "🚨 Emergency" if item["label"] == "LABEL_1" else "✅ Non-Emergency"
-        prob_scores[label] = round(item["score"], 4)
-        if item["score"] > top_score:
-            top_score = item["score"]
-            top_label = label
+        output_dict[item["label"]] = item["score"]
 
-    # Build a nice verdict string
-    verdict = f"{top_label}  —  Confidence: {round(top_score * 100, 2)}%"
+    return output_dict
 
-    return verdict, prob_scores
 
 # 3. Create a Gradio interface
 description = """
@@ -46,28 +32,23 @@ Fine-tuned from [DistilBERT](https://huggingface.co/distilbert/distilbert-base-u
 See [source code on GitHub](https://github.com/AshenFdo/Blood-Request-Emergency-Classification-Model).
 """
 
-demo = gr.Interface(
-    fn=blood_request_classifier,
-    inputs=gr.Textbox(
-        lines=4,
-        placeholder="Enter a blood request message here...",
-        label="Blood Request Text"
-    ),
-    outputs=[
-        gr.Textbox(label="🏷️ Verdict"),          # Shows label + confidence %
-        gr.Label(num_top_classes=2, label="📊 Probability Scores")   # Shows both class probs
-    ],
-    title="🩸 Emergency Blood Request Classifier",
-    description=description,
-    examples=[
-        ["Patient is in critical condition after surgery and urgently needs O- blood immediately or they may not survive."],
-        ["Hi, I am looking for a B+ blood donor for my father's scheduled knee replacement surgery next month."],
-        ["URGENT: Accident victim in ER needs AB+ blood NOW. Lives at stake, please respond immediately!"],
-        ["Our hospital is planning a blood donation camp next Saturday. All blood types welcome."],
-        ["A newborn baby in the ICU critically needs O+ blood within the next hour. Please help!"],
-    ]
-  
-)
+demo = gr.Interface(fn=food_not_food_classifier,
+             inputs=gr.Textbox(
+                 lines=4,
+                 placeholder="Enter a blood request message here...",
+                 label="Blood Request Text",
+                 max= 200
+                 ),
+             outputs=gr.Label(num_top_classes=2), # show top 2 classes (that's all we have)
+             title="🩸 Emergency Blood Request Classifier",
+             description=description,
+             examples=[
+                  ["Patient is in critical condition after surgery and urgently needs O- blood immediately or they may not survive."],
+                  ["Hi, I am looking for a B+ blood donor for my father's scheduled knee replacement surgery next month."],
+                  ["URGENT: Accident victim in ER needs AB+ blood NOW. Lives at stake, please respond immediately!"],
+                  ["Our hospital is planning a blood donation camp next Saturday. All blood types welcome."],
+                  ["A newborn baby in the ICU critically needs O+ blood within the next hour. Please help!"],
+                ])
 
 # 4. Launch the interface
 if __name__ == "__main__":