Upload app.py

app.py

@@ -57,7 +57,7 @@ def retrieve(retriever_engine,query_str):
 		else:C.append(A.text)
 	return B,C
 qa_tmpl_str='Given the provided information, including relevant images and retrieved context from the video,  accurately and precisely answer the query without any additional prior knowledge.\nPlease ensure honesty and responsibility, refraining from any racist or sexist remarks.\n---------------------\nContext: {context_str}\nMetadata for video: {metadata_str} \n---------------------\nQuery: {query_str}\nAnswer: '
-available_models=[{_C:'meta-llama/llama-3.3-70b-instruct:free',_B:'VegaAI'},{_C:'meta-llama/llama-3.3-70b-instruct:free',_B:'Llama'},{_C:'qwen/qwen2.5-vl-32b-instruct:free',_B:'Qwen'},{_C:'google/gemma-3-27b-it:free',_B:'Gemma'},{_C:'moonshotai/kimi-k2:free',_B:'Kimi'},{_C:'google/gemini-2.0-flash-exp:free',_B:'Gemini'}]
+available_models=[{_C:'meta-llama/llama-4-maverick:free',_B:'Llama'},{_C:'qwen/qwen2.5-vl-72b-instruct:free',_B:'Qwen'},{_C:'google/gemma-3-27b-it:free',_B:'Gemma'},{_C:'moonshotai/kimi-vl-a3b-thinking:free',_B:'Kimi'},{_C:'google/gemini-2.0-flash-exp:free',_B:'Gemini'}]
 model_value_to_label={A[_C]:A[_B]for A in available_models}
 model_label_to_value={A[_B]:A[_C]for A in available_models}
 def gradio_chat(query,model_label):