Update app.py

app.py

@@ -60,7 +60,7 @@ def softmax(x):
     return exp_x / exp_x.sum()
 
 # Function to detect context
-def detect_context(input_text, threshold=0.075):
+def detect_context(input_text, threshold=0.0275):
     # Encode the input text
     inputs = tokenizer([input_text], padding=True, truncation=True, return_tensors="pt")
     with torch.no_grad():
@@ -100,7 +100,7 @@ def process_request(input_text):
     context_results = detect_context(input_text)
 
     # Step 3: Print the list of high-confidence contexts
-    print("High-confidence contexts (score >= 0.0275):", context_results)
+    print("Contexts:", context_results)
 
     # Return the translation and contexts
     return translation, context_results
@@ -109,9 +109,9 @@ def process_request(input_text):
 def gradio_interface(input_text):
     translation, contexts = process_request(input_text)
     # Format the output
-    output = f"Translation: {translation}\n\nDetected Contexts (score >= 0.022):\n"
-    for context, score in contexts:
-        output += f"- {context} (confidence: {score:.4f})\n"
+    output = f"Translation: {translation}\n\nContexts:\n"
+    for context in contexts:
+        output += f"- {context}\n"
     return output.strip()
 
 # Create the Gradio interface
@@ -120,7 +120,7 @@ interface = gr.Interface(
     inputs="text",
     outputs="text",
     title="Frenchizer",
-    description="Translate text from English to French with context detection."
+    description="Translate text from English to French with context detection and threshold."
 )
 
 interface.launch()