Update app.py

app.py

@@ -2,7 +2,6 @@ import gradio as gr
 from transformers import AutoTokenizer, AutoModel
 from sklearn.metrics.pairwise import cosine_similarity
 import torch
-import numpy as np
 from gradio_client import Client
 from functools import lru_cache
 
@@ -43,7 +42,7 @@ def precompute_label_embeddings():
 label_embeddings = precompute_label_embeddings()
 
 # Function to detect context
-def detect_context(input_text, fallback_threshold=0.8, max_results=3):
+def detect_context(input_text, fallback_threshold=0.5):  # Lowered threshold for debugging
     # Encode the input text
     inputs = tokenizer([input_text], padding=True, truncation=True, return_tensors="pt")
     with torch.no_grad():
@@ -53,41 +52,48 @@ def detect_context(input_text, fallback_threshold=0.8, max_results=3):
     # Compute cosine similarities
     similarities = cosine_similarity(input_embedding, label_embeddings)[0]
 
-    # Check for fallback matches
-    fallback_labels = [(labels[i], score) for i, score in enumerate(similarities) if score >= fallback_threshold]
-    fallback_labels = sorted(fallback_labels, key=lambda x: x[1], reverse=True)[:max_results]
-    return fallback_labels
+    # Debugging: Print all labels and their similarity scores
+    print("Debug: Similarity scores for all labels:")
+    for label, score in zip(labels, similarities):
+        print(f"{label}: {score:.4f}")
+
+    # Filter contexts with confidence >= fallback_threshold
+    high_confidence_contexts = [(labels[i], score) for i, score in enumerate(similarities) if score >= fallback_threshold]
+
+    # If no contexts meet the threshold, include "general" as a fallback
+    if not high_confidence_contexts:
+        high_confidence_contexts = [("general", 1.0)]  # Assign a default score of 1.0 for "general"
+
+    return high_confidence_contexts
 
 # Translation client
 translation_client = Client("Frenchizer/space_7")
 
-def translate_text(input_text, context="general"):
-    # Append the context to the input text for the translation client (if needed)
+def translate_text(input_text):
+    # Translate the input text
     return translation_client.predict(input_text)
 
 def process_request(input_text):
-    # Step 1: Return the general translation immediately
-    general_translation = translate_text(input_text, context="general")
+    # Step 1: Translate the text
+    translation = translate_text(input_text)
 
-    # Step 2: Detect context in the background
+    # Step 2: Detect context
     context_results = detect_context(input_text)
 
-    # Step 3: Generate additional translations for high-confidence contexts
-    additional_translations = {}
-    for context, score in context_results:
-        if context != "general":
-            additional_translations[context] = translate_text(input_text, context=context)
+    # Step 3: Print the list of high-confidence contexts
+    print("High-confidence contexts:", context_results)
 
-    # Return the general translation and additional context translations
-    return general_translation, additional_translations
+    # Return the translation and contexts
+    return translation, context_results
 
-# Gradio interface with multiple outputs
+# Gradio interface
 def gradio_interface(input_text):
-    general_translation, additional_translations = process_request(input_text)
-    outputs = f"{general_translation}\n\n"
-    for context, translation in additional_translations.items():
-        outputs += f"Context ({context}): {translation}\n\n"
-    return outputs.strip()
+    translation, contexts = process_request(input_text)
+    # Format the output
+    output = f"Translation: {translation}\n\nDetected Contexts:\n"
+    for context, score in contexts:
+        output += f"- {context} (confidence: {score:.2f})\n"
+    return output.strip()
 
 # Create the Gradio interface
 interface = gr.Interface(
@@ -95,7 +101,7 @@ interface = gr.Interface(
     inputs="text",
     outputs="text",
     title="Frenchizer",
-    description="Translate text from English to French with optimized context detection and MarianMT model."
+    description="Translate text from English to French with context detection."
 )
 
 interface.launch()