Update app.py

app.py

@@ -1,3 +1,144 @@
+# import torch
+# import transformers
+# from transformers import RagRetriever, RagSequenceForGeneration, AutoTokenizer, AutoModelForCausalLM
+# import gradio as gr
+
+# device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+
+# dataset_path = "./5k_index_data/my_knowledge_dataset"
+# index_path = "./5k_index_data/my_knowledge_dataset_hnsw_index.faiss"
+
+# tokenizer = AutoTokenizer.from_pretrained("facebook/rag-sequence-nq")
+# retriever = RagRetriever.from_pretrained("facebook/rag-sequence-nq", index_name="custom",
+#                                             passages_path = dataset_path,
+#                                             index_path = index_path,
+#                                             n_docs = 5)
+# rag_model = RagSequenceForGeneration.from_pretrained('facebook/rag-sequence-nq', retriever=retriever)
+# rag_model.retriever.init_retrieval()
+# rag_model.to(device)
+# model = AutoModelForCausalLM.from_pretrained('HuggingFaceH4/zephyr-7b-beta',
+#                               device_map = 'auto',
+#                               torch_dtype = torch.bfloat16,
+#                              )
+
+
+
+# def strip_title(title):
+#     if title.startswith('"'):
+#         title = title[1:]
+#     if title.endswith('"'):
+#         title = title[:-1]
+    
+#     return title
+
+# # getting the correct format to input in gemma model
+# def input_format(query, context):
+#     sys_instruction = f'Context:\n {context} \n Given the following information, generate answer to the question. Provide links in the answer from the information to increase credebility.'
+#     message = f'Question: {query}'
+
+#     return f'<bos><start_of_turn>\n{sys_instruction}' + f' {message}<end_of_turn>\n'
+
+# # retrieving and generating  answer in one call
+# def retrieved_info(query, rag_model = rag_model, generating_model = model):
+#     # Tokenize Query
+#     retriever_input_ids = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
+#         [query],
+#         return_tensors = 'pt',
+#         padding = True,
+#         truncation = True,
+#     )['input_ids'].to(device)
+
+#     # Retrieve Documents
+#     question_encoder_output = rag_model.rag.question_encoder(retriever_input_ids)
+#     question_encoder_pool_output = question_encoder_output[0]
+
+#     result = rag_model.retriever(
+#         retriever_input_ids,
+#         question_encoder_pool_output.cpu().detach().to(torch.float32).numpy(),
+#         prefix = rag_model.rag.generator.config.prefix,
+#         n_docs = rag_model.config.n_docs,
+#         return_tensors = 'pt',
+#     )
+
+#     # Preparing query and retrieved docs for model
+#     all_docs = rag_model.retriever.index.get_doc_dicts(result.doc_ids)
+#     retrieved_context = []
+#     for docs in all_docs:
+#         titles = [strip_title(title) for title in docs['title']]
+#         texts = docs['text']
+#         for title, text in zip(titles, texts):
+#             retrieved_context.append(f'{title}: {text}')
+
+#     generation_model_input = input_format(query, retrieved_context)
+
+#     # Generating answer using gemma model
+#     tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta")
+#     input_ids = tokenizer(generation_model_input, return_tensors='pt').to(device)
+#     output = generating_model.generate(input_ids, max_new_tokens = 256)
+    
+#     return tokenizer.decode(output[0])
+
+
+
+
+
+
+# def respond(
+#     message,
+#     history: list[tuple[str, str]],
+#     system_message,
+#     max_tokens ,
+#     temperature,
+#     top_p,
+# ):
+#     if message:  # If there's a user query
+#         response = retrieved_info(message)  # Get the answer from your local FAISS and Q&A model
+#         return response
+
+#     # In case no message, return an empty string
+#     return ""
+
+
+
+# """
+# For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
+# """
+# # Custom title and description
+# title = "🧠 Welcome to Your AI Knowledge Assistant"
+# description = """
+# Hi!! I am your loyal assistant. My functionality is based on the RAG model. I retrieve relevant information and provide answers based on that. Ask me any questions, and let me assist you.
+# My capabilities are limited because I am still in the development phase. I will do my best to assist you. SOOO LET'S BEGGINNNN......
+# """
+
+# demo = gr.ChatInterface(
+#     respond,
+#     type = 'messages',
+#     additional_inputs=[
+#         gr.Textbox(value="You are a helpful and friendly assistant.", label="System message"),
+#         gr.Slider(minimum=1, maximum=2048, value=256, step=1, label="Max new tokens"),
+#         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+#         gr.Slider(
+#             minimum=0.1,
+#             maximum=1.0,
+#             value=0.95,
+#             step=0.05,
+#             label="Top-p (nucleus sampling)",
+#         ),
+#     ],
+#     title=title,
+#     description=description,
+#     textbox=gr.Textbox(placeholder=["'What is the future of AI?' or 'App Development'"]),
+#     examples=[["✨Future of AI"], ["📱App Development"]],
+#     example_icons=["🤖", "📱"],
+#     theme="compact",
+#     submit_btn = True,
+# )
+
+
+# if __name__ == "__main__":
+#     demo.launch(share = True )
+
 import torch
 import transformers
 from transformers import RagRetriever, RagSequenceForGeneration, AutoTokenizer, AutoModelForCausalLM
@@ -34,10 +175,17 @@ def strip_title(title):
 
 # getting the correct format to input in gemma model
 def input_format(query, context):
-    sys_instruction = f'Context:\n {context} \n Given the following information, generate answer to the question. Provide links in the answer from the information to increase credebility.'
-    message = f'Question: {query}'
+#     sys_instruction = f'Context:\n {context} \n Given the following information, generate answer to the question. Provide links in the answer from the information to increase credebility.'
+#     message = f'Question: {query}'
 
-    return f'<bos><start_of_turn>\n{sys_instruction}' + f' {message}<end_of_turn>\n'
+#     return f'<bos><start_of_turn>\n{sys_instruction}' + f' {message}<end_of_turn>\n'
+    return [
+        {
+            "role": "system", "content": f'Context:\n {context} \n Given the following information, generate answer to the question. Provide links in the answer from the information to increase credebility.' },
+        
+        {
+            "role": "user", "content": f"{query}"},
+    ]
 
 # retrieving and generating  answer in one call
 def retrieved_info(query, rag_model = rag_model, generating_model = model):
@@ -69,21 +217,18 @@ def retrieved_info(query, rag_model = rag_model, generating_model = model):
         texts = docs['text']
         for title, text in zip(titles, texts):
             retrieved_context.append(f'{title}: {text}')
+        print(retrieved_context)
 
-    generation_model_input = input_format(query, retrieved_context)
+    generation_model_input = input_format(query, retrieved_context[0])
 
     # Generating answer using gemma model
     tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta")
-    input_ids = tokenizer(generation_model_input, return_tensors='pt').to(device)
+    input_ids = tokenizer(generation_model_input, return_tensors='pt')['input_ids'].to(device)
     output = generating_model.generate(input_ids, max_new_tokens = 256)
     
     return tokenizer.decode(output[0])
 
 
-
-
-
-
 def respond(
     message,
     history: list[tuple[str, str]],
@@ -130,12 +275,12 @@ demo = gr.ChatInterface(
     description=description,
     textbox=gr.Textbox(placeholder=["'What is the future of AI?' or 'App Development'"]),
     examples=[["✨Future of AI"], ["📱App Development"]],
-    example_icons=["🤖", "📱"],
+    #example_icons=["🤖", "📱"],
     theme="compact",
     submit_btn = True,
 )
 
 
 if __name__ == "__main__":
-    demo.launch(share = True )
-
+    demo.launch(share = True,
+                show_error = True)