Update app.py

app.py

@@ -328,58 +328,23 @@ textarea::placeholder {
 }
 """
 
-# def process_question(question: str) -> Iterator[str]:
-#     """
-#     Process the question and return a response generator for streaming.
-#     """
-#     if question in question_cache:
-#         yield question_cache[question][0]
-#         return
-
-#     relevant_docs = retriever(question)
-#     context = "\n".join([doc.page_content for doc in relevant_docs])
-
-#     prompt = prompt_template.format_messages(
-#         context=context,
-#         question=question
-#     )
-
-#     full_response = ""
-#     try:
-#         for chunk in llm.stream(prompt):
-#             if isinstance(chunk, str):
-#                 current_chunk = chunk
-#             else:
-#                 current_chunk = chunk.content
-
-#             full_response += current_chunk
-#             yield full_response  # Send the updated response in streaming
-
-#         question_cache[question] = (full_response, context)
-#     except Exception as e:
-#         yield f"Erreur lors du traitement : {str(e)}"
-
-def process_question(question: str) -> tuple[str, list[str]]:
-    # Check cache first
+def process_question(question: str) -> Iterator[str]:
+    """
+    Process the question and return a response generator for streaming.
+    """
     if question in question_cache:
-        return question_cache[question]
+        yield question_cache[question][0]
+        return
 
-    # Get relevant documents using the retriever
     relevant_docs = retriever(question)
-    
-    # Extract the content and sources
-    context = "\n".join(doc.page_content for doc in relevant_docs)
-    sources = [doc.metadata["source"] for doc in relevant_docs]
-    sources = os.path.splitext(sources[0])[0] if sources else "غير معروف"
-    
-    # Generate the prompt with the context
-    prompt = prompt_template.format(
+    context = "\n".join([doc.page_content for doc in relevant_docs])
+
+    prompt = prompt_template.format_messages(
         context=context,
         question=question
     )
 
     full_response = ""
-    
     try:
         for chunk in llm.stream(prompt):
             if isinstance(chunk, str):
@@ -388,12 +353,47 @@ def process_question(question: str) -> tuple[str, list[str]]:
                 current_chunk = chunk.content
 
             full_response += current_chunk
-            yield full_response, sources   # Send the updated response in streaming
+            yield full_response  # Send the updated response in streaming
 
-        question_cache[question,sources] = (full_response, context)
+        question_cache[question] = (full_response, context)
     except Exception as e:
         yield f"Erreur lors du traitement : {str(e)}"
 
+# def process_question(question: str) -> tuple[str, list[str]]:
+#     # Check cache first
+#     if question in question_cache:
+#         return question_cache[question]
+
+#     # Get relevant documents using the retriever
+#     relevant_docs = retriever(question)
+    
+#     # Extract the content and sources
+#     context = "\n".join(doc.page_content for doc in relevant_docs)
+#     sources = [doc.metadata["source"] for doc in relevant_docs]
+#     sources = os.path.splitext(sources[0])[0] if sources else "غير معروف"
+    
+#     # Generate the prompt with the context
+#     prompt = prompt_template.format(
+#         context=context,
+#         question=question
+#     )
+
+#     full_response = ""
+    
+#     try:
+#         for chunk in llm.stream(prompt):
+#             if isinstance(chunk, str):
+#                 current_chunk = chunk
+#             else:
+#                 current_chunk = chunk.content
+
+#             full_response += current_chunk
+#             yield full_response, sources   # Send the updated response in streaming
+
+#         question_cache[question,sources] = (full_response, context)
+#     except Exception as e:
+#         yield f"Erreur lors du traitement : {str(e)}"
+
 
 
 def gradio_stream(question: str, chat_history: list) -> Iterator[list]: