added app.py

app.py

@@ -0,0 +1,65 @@
+import streamlit as st
+from langchain.document_loaders import PyPDFLoader
+from langchain.indexes import VectorstoreIndexCreator
+from langchain.chains import RetrievalQA, RetrievalQAWithSourcesChain
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+
+    
+def respond_to_question(question, model, tokenizer):
+
+    prompt = [{'role': 'user', 'content': question}]
+    inputs = tokenizer.apply_chat_template(
+        prompt,
+        add_generation_prompt=True,
+        return_tensors='pt'
+    )
+
+    tokens = model.generate(
+        inputs.to(model.device),
+        max_new_tokens=1024,
+        temperature=0.8,
+        do_sample=True
+    )
+
+    print(tokenizer.decode(tokens[0], skip_special_tokens=False))
+    return tokenizer.decode(tokens[0], skip_special_tokens=False)
+    # prompt = "write me a python function that prints the fibonacci sequence"
+    # messages = [
+    #     {
+    #         "role": "system",
+    #         "content": "You are a friendly chatbot who can code",
+    #     },
+    #     {"role": "user", "content": prompt},
+    # ]
+    # prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    # outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
+    # print(outputs[0]["generated_text"].split("<|assistant|>")[1])
+    # return outputs[0]["generated_text"].split("<|assistant|>")[1]
+
+def main():
+    st.title("LangChain Demo")
+
+    tokenizer = AutoTokenizer.from_pretrained('stabilityai/stablelm-zephyr-3b')
+    model = AutoModelForCausalLM.from_pretrained(
+            'stabilityai/stablelm-zephyr-3b',
+            trust_remote_code=True,
+            device_map="auto"
+        )
+
+    if 'messages' not in st.session_state:
+        st.session_state.messages = []
+        
+    prompt = st.text_input("Enter your question here:")
+
+    for message in st.session_state.messages:
+        st.chat_message(message['role']).markdown(message['text'])
+
+    if prompt:
+        st.session_state.messages.append({'role': 'user', 'text': prompt})
+        st.chat_message("user").markdown(prompt)
+        model_response = respond_to_question(prompt, model, tokenizer)
+        st.session_state.messages.append({'role': 'Assistant', 'text': model_response})
+        st.chat_message("system").markdown(model_response)

requirements.txt

@@ -0,0 +1,5 @@
+streamlit
+langchain
+transformers
+git+https://github.com/huggingface/transformers.git
+accelerate