Update app.py

app.py

@@ -0,0 +1,142 @@
+import streamlit as st
+from langchain_community.llms import LlamaCpp
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+from langchain_core.callbacks import StreamingStdOutCallbackHandler
+from langchain.chains import RetrievalQA
+from langchain.memory import ConversationBufferMemory
+from langchain import PromptTemplate
+from langchain.retrievers import TFIDFRetriever
+
+callbacks = [StreamingStdOutCallbackHandler()]
+print("creating llm started")
+llm = LlamaCpp(
+    model_path='unsloth.Q5_K_M.gguf',
+    temperature=0.75,
+    max_tokens=30,
+    top_p=4,
+    callback_manager=callbacks,
+    verbose=True,  # Verbose is required to pass to the callback manager
+)
+print("creating llm ended")
+
+def with_memory(llm):
+    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,
+    )
+    
+    qa = 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"),
+        }
+    )
+    return qa
+
+def without_memory(llm):
+    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)
+    print("creating model created")
+    return llm_chain_model
+
+
+def main():
+
+    """Build a streamlit layout"""
+    # Wide mode
+    st.set_page_config(layout="wide")
+    
+    llm_models = {
+    
+        "Base": "unsloth.Q5_K_M.gguf",
+        "Cerebras": "cerebras_Llama3-DocChat-1.0-8B_Base_adapt_basic_model_16bit.gguf",
+        "Bavest": "bavest_fin_llama_33b_adapt_basic_model_16bit.gguf",
+        "Aliyasir": "aliyasir_Llama-3-8B-Instruct-Finance-RAG_adapt_basic_model_16bit.gguf",
+        "Basic Adapt": "adapt-unsloth.Q5_K_M.gguf",
+        "adapt llm": "AdaptLLM_finance-LLM-13B_adapt_basic_model_16bit.gguf",
+        "Fibro" : "finbro-v0.1.0-llama-3-8B-instruct-1m.gguf",
+    }
+    # Designing the interface
+    st.title("Financial LLM test")
+    # For newline
+    st.write("\n")
+    # Instructions
+    st.markdown("*Hint: you can select the LLM model and write your prompt")
+    # Set the columns
+
+    col1, col2 = st.columns(2)
+    col1.subheader("Prompt Section")
+    col2.subheader("Model Output")
+
+    llm_qa = without_memory(llm)
+    # Model selection
+    st.sidebar.title("Model selection")
+    det_arch = st.sidebar.selectbox("LLM model", ['With Memory', 'Without Memory'])
+
+    # For newline
+    st.sidebar.write("\n")
+
+
+    if st.sidebar.button("Select LLM"):
+        with st.spinner("Loading model..."):
+            if det_arch == 'Without Memory':
+                llm_qa = without_memory(llm)
+            else:
+                llm_qa = with_memory(llm)
+            # llm_qa = get_model(llm_models.get(det_arch))
+            # load the model TODO
+
+    text_input = ''
+    with col1:
+        text_input_temp = st.text_input(
+            "Please, type your question and submit.",
+            "Write Your Prompt",
+            key="placeholder",
+        )
+        
+        if st.button("Submit"):
+            text_input = text_input_temp
+            
+    with col2:
+        if text_input != '':
+            with st.spinner("Analyzing..."):
+                out_gen = llm_qa.run(question)  
+                st.write("LLM Response: ", out_gen)
+                text_input = ''
+            
+    
+
+if __name__ == "__main__":
+    main()