add chain

Dockerfile

@@ -4,7 +4,6 @@ WORKDIR /code
 
 COPY ./requirements.txt /code/requirements.txt
 
-RUN pip3 install torch torchvision torchaudio
 RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
 
 COPY . .

app.py

@@ -3,29 +3,23 @@ import torch
 import streamlit as st
 from streamlit import session_state as state
 import streamlit_ace
-import model
 import pipline
 
 if "app" not in state:
     state.app = "model"
-    # state.input_text = "This is the input text."
-    # state.word = "input"
     state.out = ""
-
 st.title("Streamlit using Huggingface Transformers and langchain")
+in_area = st.container()
+out_area = st.container()
 
-def __run_pipline():
-    st.text(f"input_text: {state.input_text}\nword: {state.word}")
-    st.markdown(":green[Running pipline]")
-    st.text(pipline.pipeline(state.input_text, state.word))
 
-def __run_model():
-    st.text(f"input_text: {state.input_text}")
-    st.markdown(":green[Running model]")
-    st.text(model.run(state.input_text))
+
+def __run_pipline():
+    out_area.text(f"input_text: {state.input_text}\nword: {state.word}")
+    out_area.markdown(":green[Running pipline]")
+    out_area.text(pipline.chain(state.input_text))
 
 
-st.text_area("input_text", key="input_text")
-st.text_input("word", key="word")
-st.button("pip line", on_click=__run_pipline)
-st.button("model", on_click=__run_model)
+in_area.text_area("input_text", key="input_text")
+in_area.text_input("word", key="word")
+in_area.button("pip line", on_click=__run_pipline)

pipline.py

@@ -1,36 +1,24 @@
 import langchain as lc
-from langchain import PromptTemplate
+from langchain import PromptTemplate, OpenAI, LLMChain
 from langchain.prompts import load_prompt
 import wikipedia
-import model
-
+import os
+llm = OpenAI()
 # save templates to a file
+template = """Question:
+ The user wrote me the following text, what is he trying to imply to me?
+ {user_input}
 
-
+Answer: Let's think step by step."""
 # An example prompt with multiple input variables
-multiple_input_prompt = PromptTemplate(
-    input_variables=["adjective", "content"],
-    template="Tell me a {adjective} joke about {content}.",
+input_prompt = PromptTemplate(
+    input_variables=["user_input"],
+    template=template,
 )
-multiple_input_prompt.save("awesome_prompt.json")  # Save to JSON file
-# multiple_input_prompt.format(adjective="funny", content="chickens")
-# -> "Tell me a funny joke about chickens."
+input_prompt.save("awesome_prompt.json")  # Save to JSON file
 
 
 prompt = load_prompt("awesome_prompt.json")
 
-
-def pipeline(text, word):
-    model_output = ""
-    input_text = prompt.format(adjective="funny", content=text)
-    while word not in model_output:
-        model_output = model.run(input_text)
-        wikipedia_entry = wikipedia.search(word)[1]
-        wiki = wikipedia.summary(wikipedia_entry, auto_suggest=False, redirect=True)
-        input_text += model_output + wiki
-    return model_output
-
-
-if __name__ == "__main__":
-    print("pipline test")
-    pipeline("This is the input text.", "input")
+prompt = PromptTemplate(template=template, input_variables=["user_input"])
+chain = LLMChain(prompt=prompt, llm=llm)