Delete app.py

app.py

@@ -1,142 +0,0 @@
-import os
-import streamlit as st
-from langchain.llms import HuggingFaceHub
-from langchain.chains import LLMChain
-from langchain.prompts import PromptTemplate
-
-class UserInterface():
-
-    def __init__(self, ):
-        st.warning("Warning: Some models may not work and some models may require GPU to run")
-        st.text("An Open Source Chat Application")
-        st.header("Open LLMs")
-
-        # self.API_KEY = st.sidebar.text_input(
-        #     'API Key',
-        #     type='password',
-        #     help="Type in your HuggingFace API key to use this app"
-        # )
-
-        models_name =("HuggingFaceH4/zephyr-7b-beta", )
-            
-      
-
-        self.temperature = st.sidebar.slider(
-            label='Temperature',
-            min_value=0.1,
-            max_value=1.0,
-            step=0.1,
-            value=0.5,
-            help="Set the temperature to get accurate or random result"
-        )
-
-        self.max_token_length = st.sidebar.slider(
-            label="Token Length",
-            min_value=32,
-            max_value=2048,
-            step=16,
-            value=64,
-            help="Set max tokens to generate maximum amount of text output"
-        )
-
-
-        self.model_kwargs = {
-            "temperature": self.temperature,
-            "max_new_tokens": self.max_token_length
-        }
-
-        os.environ['HUGGINGFACEHUB_API_TOKEN'] = os.getenv("HF_KEY")
-
-    
-    def form_data(self):
-
-        try:
-            # if not self.API_KEY.startswith('hf_'):
-            #     st.warning('Please enter your API key!', icon='⚠')
-            #     text_input_visibility = True
-            # else:
-            #     text_input_visibility = False
-            text_input_visibility = False
-            
-
-            if "messages" not in st.session_state:
-                    st.session_state.messages = []
-
-            st.write(f"You are using {self.models} model")
-
-            for message in st.session_state.messages:
-                with st.chat_message(message.get('role')):
-                    st.write(message.get("content"))
-            
-            context = st.sidebar.text_input(
-                 label="Context",
-                 help="Context lets you know on what the answer should be generated"
-                 )
-
-        
-            question = st.chat_input(
-                 key="question",
-                 disabled=text_input_visibility
-            )
-
-            template = f"<|system|>\nYou are a intelligent chatbot and expertise in {context}.</s>\n<|user|>\n{question}.\n<|assistant|>"
-
-            # template = """
-            # Answer the question based on the context, if you don't know then output "Out of Context"
-            # Context: {context}
-            # Question: {question}
-
-            # Answer: 
-            # """
-            prompt = PromptTemplate(
-                template=template,
-                input_variables=[
-                    'question',
-                    'context'
-                ]
-            )
-            llm = HuggingFaceHub(
-                repo_id = self.models,
-                model_kwargs = self.model_kwargs
-            )
-
-            if question:
-                llm_chain = LLMChain(
-                    prompt=prompt,
-                    llm=llm,
-                )
-
-                result = llm_chain.run({
-                    "question": question,
-                    "context": context
-                })
-
-                if "Out of Context" in result:
-                    result = "Out of Context"
-                st.session_state.messages.append(
-                    {
-                        "role":"user",
-                        "content": f"Context: {context}\n\nQuestion: {question}"
-                    }
-                )
-                with st.chat_message("user"):
-                    st.write(f"Context: {context}\n\nQuestion: {question}")
-                
-                if question.lower() == "clear":
-                    del st.session_state.messages
-                    return
-                
-                st.session_state.messages.append(
-                    {
-                        "role": "assistant",
-                        "content": result
-                    }
-                )
-                with st.chat_message('assistant'):
-                    st.markdown(result)
-
-        except Exception as e:
-            st.error(e, icon="🚨")
-
-model = UserInterface()
-model.form_data()