Update app.py

app.py

@@ -9,36 +9,24 @@ import requests
 nlp = spacy.load("en_core_web_sm")  # Use "en_core_web_trf" if more accuracy is needed
 spell_checker = pipeline("text2text-generation", model="oliverguhr/spelling-correction-english-base")
 
-def preprocess_and_forward(text: str) -> str:
+def preprocess_and_forward(text: str) -> dict:
+    """
+    Processes the input text and forwards it to the Gradio client for space_17.
+    """
+    # Preprocess the text
     processed_text, preprocessing_results = preprocess_text(text)
 
-    try:
-        # Forward preprocessed text to context detection
-        context_response = requests.post(
-            "https://api.gradio.app/v2/Frenchizer/space_17/predict",
-            json={"data": [processed_text]}
-        ).json()
-        
-        if "error" in context_response:
-            return json.dumps({
-                "error": "Context detection failed",
-                "preprocessing_results": preprocessing_results
-            })
-        
-        context = context_response["data"][0]
-
-        # Return preprocessing and detected context
-        result = {
-            "preprocessing": preprocessing_results,
-            "context": context
-        }
-        return json.dumps(result)
+    # Instantiate the Gradio Client for space_17
+    client = gr.Client("Frenchizer/space_17")
 
+    # Forward preprocessed text to the Gradio client for space_17
+    try:
+        context_response = client.predict(processed_text)
+        preprocessing_results["context_response"] = context_response
     except Exception as e:
-        return json.dumps({
-            "error": str(e),
-            "preprocessing_results": preprocessing_results
-        })
+        preprocessing_results["context_error"] = str(e)
+
+    return preprocessing_results
 
 def preprocess_text(text: str):
     result = {