build: create a new streamlit app file that will be built in Langchain. TO DO: build out Langchain and then remove - rename back to when migration is successful

app_langchain.py

@@ -0,0 +1,110 @@
+import streamlit as st
+import yaml
+import requests
+import re
+import os
+from src.pdfParser import get_pdf_text
+
+# Get HuggingFace API key
+api_key_name = "HUGGINGFACE_HUB_TOKEN"
+api_key = os.getenv(api_key_name)
+if api_key is None:
+    st.error(f"Failed to read `{api_key_name}`. Ensure the token is correctly located")
+
+
+with open("config/model_config.yml", "r") as file:
+    model_config = yaml.safe_load(file)
+
+system_message = model_config["system_message"]
+model_id = model_config["model_id"]
+
+
+def query(payload, model_id):
+    headers = {"Authorization": f"Bearer {api_key}"}
+    API_URL = f"https://api-inference.huggingface.co/models/{model_id}"
+    response = requests.post(API_URL, headers=headers, json=payload)
+    return response.json()
+
+
+def prompt_generator(system_message, user_message):
+    return f"""
+        <s>[INST] <<SYS>>
+        {system_message}
+        <</SYS>>
+        {user_message} [/INST]
+        """
+
+
+# Pattern to clean up text response from API
+pattern = r".*\[/INST\]([\s\S]*)$"
+
+# Initialize chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Include PDF upload ability
+pdf_upload = st.file_uploader(
+    "Upload a .PDF here",
+    type=".pdf",
+)
+
+if pdf_upload is not None:
+    pdf_text = get_pdf_text(pdf_upload)
+
+
+if "key_inputs" not in st.session_state:
+    st.session_state.key_inputs = {}
+
+col1, col2, col3 = st.columns([3, 3, 2])
+
+with col1:
+    key_name = st.text_input("Key/Column Name (e.g. patient_name)", key="key_name")
+
+with col2:
+    key_description = st.text_area(
+        "*(Optional) Description of key/column", key="key_description"
+    )
+
+with col3:
+    if st.button("Extract this column"):
+        if key_description:
+            st.session_state.key_inputs[key_name] = key_description
+        else:
+            st.session_state.key_inputs[key_name] = "No further description provided"
+
+if st.session_state.key_inputs:
+    keys_title = st.write("\nKeys/Columns for extraction:")
+    keys_values = st.write(st.session_state.key_inputs)
+
+    with st.spinner("Extracting requested data"):
+        if st.button("Extract data!"):
+            user_message = f"""
+                Use the text provided and denoted by 3 backticks ```{pdf_text}```. 
+                Extract the following columns and return a table that could be uploaded to an SQL database.
+                {'; '.join([key + ': ' + st.session_state.key_inputs[key] for key in st.session_state.key_inputs])}
+            """
+            the_prompt = prompt_generator(
+                system_message=system_message, user_message=user_message
+            )
+            response = query(
+                {
+                    "inputs": the_prompt,
+                    "parameters": {"max_new_tokens": 500, "temperature": 0.1},
+                },
+                model_id,
+            )
+            try:
+                match = re.search(
+                    pattern, response[0]["generated_text"], re.MULTILINE | re.DOTALL
+                )
+                if match:
+                    response = match.group(1).strip()
+
+                response = eval(response)
+
+                st.success("Data Extracted Successfully!")
+                st.write(response)
+            except:
+                st.error("Unable to connect to model. Please try again later.")
+
+            # st.success(f"Data Extracted!")