Update app.py

app.py

@@ -1,94 +1,3 @@
-# import gradio as gr
-
-# from langchain_community.llms import LlamaCpp
-# from langchain.prompts import PromptTemplate
-# from langchain.chains import LLMChain
-# from langchain_core.callbacks import StreamingStdOutCallbackHandler
-# from langchain.retrievers import TFIDFRetriever
-# from langchain.chains import RetrievalQA
-# from langchain.memory import ConversationBufferMemory
-# from langchain_community.chat_models import ChatLlamaCpp
-
-
-# callbacks = [StreamingStdOutCallbackHandler()]
-# print("creating ll started")
-# llm = ChatLlamaCpp(
-#     model_path="finbro-v0.1.0-llama-3-8B-instruct-1m.gguf",
-#     n_batch=8,
-#     temperature=0.85,
-#     max_tokens=256,
-#     top_p=0.95,
-#     top_k = 10,
-#     callback_manager=callbacks,
-#     n_ctx=2048,
-#     verbose=True,  # Verbose is required to pass to the callback manager
-# )
-# print("creating llm ended")
-
-
-
-
-
-
-# def greet(question, model_type):
-#     print(f"question is {question}")
-#     if model_type == "With memory":
-#         retriever = TFIDFRetriever.from_texts(
-#             ["Finatial AI"])
-        
-        
-#         template = """You are the Finiantial expert:
-#         {history}
-#         {context}
-#         ### Instruction:
-#         {question}
-        
-#         ### Input:
-        
-        
-#         ### Response:
-#         """
-        
-#         prompt1 = PromptTemplate(
-#             input_variables=["history", "context", "question"],
-#             template=template,
-#         )
-        
-#         llm_chain_model = RetrievalQA.from_chain_type(
-#             llm=llm,
-#             chain_type='stuff',
-#             retriever=retriever,
-#             verbose=False,
-#             chain_type_kwargs={
-#                 "verbose": False,
-#                 "prompt": prompt1,
-#                 "memory": ConversationBufferMemory(
-#                     memory_key="history",
-#                     input_key="question"),
-#             }
-#         )
-#         print("creating model created")
-#     else:
-#         template = """You are the Finiantial expert:
-#         ### Instruction:
-#         {question}
-#         ### Input:
-#         ### Response:
-#         """
-        
-#         prompt = PromptTemplate(template=template, input_variables=["question"])
-        
-#         llm_chain_model = LLMChain(prompt=prompt, llm=llm)
-#     out_gen = llm_chain_model.run(question)  
-#     print(f"out is: {out_gen}")
-#     return out_gen
-
-# demo = gr.Interface(fn=greet, inputs=["text", gr.Dropdown(
-#             ["With memory", "Without memory"], label="Memory status", info="With using memory, the output will be slow but strong"
-#         ),], outputs="text")
-# demo.launch(debug=True, share=True)
-
-
 import gradio as gr
 
 from langchain_community.llms import LlamaCpp
@@ -100,11 +9,11 @@ from langchain.chains import RetrievalQA
 from langchain.memory import ConversationBufferMemory
 from langchain_community.chat_models import ChatLlamaCpp
 
+
 callbacks = [StreamingStdOutCallbackHandler()]
 print("creating ll started")
-M_NAME = "finbro-v0.1.0-llama-3-8B-instruct-1m.gguf"
 llm = ChatLlamaCpp(
-    model_path=M_NAME,
+    model_path="finbro-v0.1.0-llama-3-8B-instruct-1m.gguf",
     n_batch=8,
     temperature=0.85,
     max_tokens=256,
@@ -114,7 +23,7 @@ llm = ChatLlamaCpp(
     n_ctx=2048,
     verbose=True,  # Verbose is required to pass to the callback manager
 )
-# print("creating ll ended")
+print("creating llm ended")
 
 
 
@@ -122,29 +31,120 @@ llm = ChatLlamaCpp(
 
 
 def greet(question, model_type):
-    print("prompt started ")
     print(f"question is {question}")
-    template = """You are the Finiantial expert:
-    
-    ### Instruction:
-    {question}
-    
-    ### Input:
-    
-    
-    ### Response:
-    """
-    print("test1")
-    prompt = PromptTemplate(template=template, input_variables=["question"])
-    print("test2")
-    llm_chain_model = LLMChain(prompt=prompt, llm=llm)
-    print("test3")
+    if model_type == "With memory":
+        retriever = TFIDFRetriever.from_texts(
+            ["Finatial AI"])
+        
+        
+        template = """You are the Finiantial expert:
+        {history}
+        {context}
+        ### Instruction:
+        {question}
+        
+        ### Input:
+        
+        
+        ### Response:
+        """
+        
+        prompt1 = PromptTemplate(
+            input_variables=["history", "context", "question"],
+            template=template,
+        )
+        
+        llm_chain_model = RetrievalQA.from_chain_type(
+            llm=llm,
+            chain_type='stuff',
+            retriever=retriever,
+            verbose=False,
+            chain_type_kwargs={
+                "verbose": False,
+                "prompt": prompt1,
+                "memory": ConversationBufferMemory(
+                    memory_key="history",
+                    input_key="question"),
+            }
+        )
+        print("creating model created")
+    else:
+        template = """You are the Finiantial expert:
+        ### Instruction:
+        {question}
+        ### Input:
+        ### Response:
+        """
+        
+        prompt = PromptTemplate(template=template, input_variables=["question"])
+        
+        llm_chain_model = LLMChain(prompt=prompt, llm=llm)
     out_gen = llm_chain_model.run(question)  
-    print("test4")
     print(f"out is: {out_gen}")
     return out_gen
 
 demo = gr.Interface(fn=greet, inputs=["text", gr.Dropdown(
-            ["Without memory", "With memory"], label="Memory status", info="With using memory, the output will be slow but strong"
+            ["With memory", "Without memory"], label="Memory status", info="With using memory, the output will be slow but strong"
         ),], outputs="text")
-demo.launch(debug=True, share=True)
+demo.launch(debug=True, share=True)
+
+
+# import gradio as gr
+
+# from langchain_community.llms import LlamaCpp
+# from langchain.prompts import PromptTemplate
+# from langchain.chains import LLMChain
+# from langchain_core.callbacks import StreamingStdOutCallbackHandler
+# from langchain.retrievers import TFIDFRetriever
+# from langchain.chains import RetrievalQA
+# from langchain.memory import ConversationBufferMemory
+# from langchain_community.chat_models import ChatLlamaCpp
+
+# callbacks = [StreamingStdOutCallbackHandler()]
+# print("creating ll started")
+# M_NAME = "finbro-v0.1.0-llama-3-8B-instruct-1m.gguf"
+# llm = ChatLlamaCpp(
+#     model_path=M_NAME,
+#     n_batch=8,
+#     temperature=0.85,
+#     max_tokens=256,
+#     top_p=0.95,
+#     top_k = 10,
+#     callback_manager=callbacks,
+#     n_ctx=2048,
+#     verbose=True,  # Verbose is required to pass to the callback manager
+# )
+# # print("creating ll ended")
+
+
+
+
+
+
+# def greet(question, model_type):
+#     print("prompt started ")
+#     print(f"question is {question}")
+#     template = """You are the Finiantial expert:
+    
+#     ### Instruction:
+#     {question}
+    
+#     ### Input:
+    
+    
+#     ### Response:
+#     """
+#     print("test1")
+#     prompt = PromptTemplate(template=template, input_variables=["question"])
+#     print("test2")
+#     llm_chain_model = LLMChain(prompt=prompt, llm=llm)
+#     print("test3")
+#     out_gen = llm_chain_model.run(question)  
+#     print("test4")
+#     print(f"out is: {out_gen}")
+#     return out_gen
+
+# demo = gr.Interface(fn=greet, inputs=["text", gr.Dropdown(
+#             ["Without memory", "With memory"], label="Memory status", info="With using memory, the output will be slow but strong"
+#         ),], outputs="text")
+# demo.launch(debug=True, share=True)