Upload main.py

main.py

@@ -0,0 +1,154 @@
+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",
+            "Sharathhebbar24/chat_gpt2",
+            "Sharathhebbar24/convo_bot_gpt_v1",
+            "Open-Orca/Mistral-7B-OpenOrca",
+            "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
+            "Sharathhebbar24/llama_7b_chat",
+            "CultriX/MistralTrix-v1",
+            "ahxt/LiteLlama-460M-1T",
+        )
+        self.models = st.sidebar.selectbox(
+            label="Choose your models",
+            options=models_name,
+            help="Choose your model",
+        )
+
+        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()