Add files

app.py

@@ -16,7 +16,6 @@ emotion_analysis = pipeline("text-classification",
                           tokenizer=tokenizer,
                           return_all_scores=True)
 
-# Create FastAPI app
 app = FastAPI()
 
 def save_upload_file(upload_file: UploadFile) -> str:
@@ -36,47 +35,38 @@ def save_upload_file(upload_file: UploadFile) -> str:
 async def predict_from_upload(file: UploadFile = File(...)):
     """API endpoint for file uploads"""
     try:
-        # Save the uploaded file temporarily
         temp_path = save_upload_file(file)
         
-        # Process based on file type
         if temp_path.endswith('.json'):
             with open(temp_path, 'r') as f:
                 data = json.load(f)
             text = data.get('description', '')
-        else:  # Assume text file
+        else:
             with open(temp_path, 'r') as f:
                 text = f.read()
         
         if not text.strip():
             raise HTTPException(status_code=400, detail="No text content found")
         
-        # Analyze text
         result = emotion_analysis(text)
         emotions = [{'label': e['label'], 'score': float(e['score'])} 
                    for e in sorted(result[0], key=lambda x: x['score'], reverse=True)]
         
-        # Clean up
         os.unlink(temp_path)
-        
-        return {
-            "success": True,
-            "results": emotions
-        }
+        return {"success": True, "results": emotions}
         
     except Exception as e:
         if 'temp_path' in locals() and os.path.exists(temp_path):
             os.unlink(temp_path)
         raise HTTPException(status_code=500, detail=str(e))
 
-# Gradio interface
-def gradio_predict(input_data):
+# 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:
-        if isinstance(input_data, str):  # Direct text input
-            text = input_data
-        else:  # File upload
-            temp_path = save_upload_file(input_data)
+        # Determine input source
+        if file_data is not None:  # File upload takes precedence
+            temp_path = save_upload_file(file_data)
             if temp_path.endswith('.json'):
                 with open(temp_path, 'r') as f:
                     data = json.load(f)
@@ -85,6 +75,8 @@ def gradio_predict(input_data):
                 with open(temp_path, 'r') as f:
                     text = f.read()
             os.unlink(temp_path)
+        else:  # Use direct text input
+            text = input_data
         
         if not text.strip():
             return {"error": "No text content found"}
@@ -100,26 +92,46 @@ def gradio_predict(input_data):
     except Exception as e:
         return {"error": str(e)}
 
-# Create Gradio interface
-demo = gr.Interface(
-    fn=gradio_predict,
-    inputs=[
-        gr.Textbox(label="Enter text directly", lines=5),
-        gr.File(label="Or upload text/JSON file", file_types=[".txt", ".json"])
-    ],
-    outputs=gr.JSON(label="Emotion Analysis"),
-    title="Text Emotion Analysis",
-    description="Analyze emotion in text using RoBERTa model",
-    examples=[
-        ["I'm feeling absolutely thrilled about this new project!"],
-        ["This situation is making me extremely anxious and worried."]
-    ]
-)
+# Updated Gradio interface with proper input handling
+with gr.Blocks() as demo:
+    gr.Markdown("# Text Emotion Analysis")
+    
+    with gr.Row():
+        with gr.Column():
+            text_input = gr.Textbox(label="Enter text directly", lines=5)
+            file_input = gr.File(label="Or upload file", file_types=[".txt", ".json"])
+            submit_btn = gr.Button("Analyze")
+        
+        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"
+    )
 
-# Mount Gradio app
 app = gr.mount_gradio_app(app, demo, path="/")
 
-# For running locally
 if __name__ == "__main__":
     import uvicorn
     uvicorn.run(app, host="0.0.0.0", port=7860)