Update app.py

app.py

@@ -1,73 +1,151 @@
-import gradio as gr
-
+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
-
-
-callbacks = [StreamingStdOutCallbackHandler()]
-print("creating ll 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 ll ended")
-
 from langchain.chains import RetrievalQA
 from langchain.memory import ConversationBufferMemory
 from langchain import PromptTemplate
 from langchain.retrievers import TFIDFRetriever
 
-
-
-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"),
+def get_model(m_name):
+    callbacks = [StreamingStdOutCallbackHandler()]
+    print("creating llm started")
+    llm = LlamaCpp(
+        model_path=m_name,
+        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")
+
+
+    # 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"),
+    #     }
+    # )
+    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",
     }
-)
-print("creating model created")
-
-
-def greet(question):
-    print(f"question is {question}")
-
-    out_gen = qa.run(question)  
-    print(f"out is {out_gen}")
-    return out_gen
-
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch(debug=True, share=True)
+    # 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 = get_model(llm_models.get('Base'))
+    # Model selection
+    st.sidebar.title("Model selection")
+    det_arch = st.sidebar.selectbox("LLM model", list(llm_models.keys()))
+
+    # For newline
+    st.sidebar.write("\n")
+
+
+    if st.sidebar.button("Select LLM"):
+        
+        with st.spinner("Loading model..."):
+            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()
+
+
+
+
+
+# def greet(question):
+#     print(f"question is {question}")
+
+#     out_gen = qa.run(question)  
+#     print(f"out is {out_gen}")
+#     return out_gen
+
+# demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+# demo.launch(debug=True, share=True)