Add files

app.py

@@ -14,7 +14,7 @@ model = RobertaForSequenceClassification.from_pretrained(model_name)
 emotion_analysis = pipeline("text-classification",
                           model=model, 
                           tokenizer=tokenizer,
-                          return_all_scores=True)
+                          top_k=None)  # Replaced return_all_scores with top_k
 
 app = FastAPI()
 
@@ -25,7 +25,7 @@ def save_upload_file(upload_file: UploadFile) -> str:
         with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tmp:
             content = upload_file.file.read()
             if suffix == '.json':
-                content = content.decode('utf-8')  # Decode JSON files
+                content = content.decode('utf-8')
             tmp.write(content if isinstance(content, bytes) else content.encode())
             return tmp.name
     finally:
@@ -60,12 +60,10 @@ async def predict_from_upload(file: UploadFile = File(...)):
             os.unlink(temp_path)
         raise HTTPException(status_code=500, detail=str(e))
 
-# Modified gradio_predict to handle both input types correctly
 def gradio_predict(input_data, file_data=None):
     """Handle both direct text and file uploads"""
     try:
-        # Determine input source
-        if file_data is not None:  # File upload takes precedence
+        if file_data is not None:
             temp_path = save_upload_file(file_data)
             if temp_path.endswith('.json'):
                 with open(temp_path, 'r') as f:
@@ -75,7 +73,7 @@ def gradio_predict(input_data, file_data=None):
                 with open(temp_path, 'r') as f:
                     text = f.read()
             os.unlink(temp_path)
-        else:  # Use direct text input
+        else:
             text = input_data
         
         if not text.strip():
@@ -92,7 +90,7 @@ def gradio_predict(input_data, file_data=None):
     except Exception as e:
         return {"error": str(e)}
 
-# Updated Gradio interface with proper input handling
+# Simplified Gradio interface without examples
 with gr.Blocks() as demo:
     gr.Markdown("# Text Emotion Analysis")
     
@@ -105,30 +103,12 @@ with gr.Blocks() as demo:
         with gr.Column():
             output = gr.JSON(label="Results")
     
-    # Handle both input methods
     submit_btn.click(
         fn=gradio_predict,
         inputs=[text_input, file_input],
         outputs=output,
         api_name="predict"
     )
-    
-    # Examples with both input types
-    gr.Examples(
-        examples=[
-            ["I'm feeling excited about this new project!"],
-            ["This situation makes me anxious and worried"]
-        ],
-        inputs=text_input
-    )
-    gr.Examples(
-        examples=[
-            ["example1.json"],
-            ["example2.txt"]
-        ],
-        inputs=file_input,
-        label="File Examples"
-    )
 
 app = gr.mount_gradio_app(app, demo, path="/")