Made working with llama.cpp

streamlit_app_talking_ep.py

@@ -8,9 +8,7 @@ import matplotlib.pyplot as plt
 import pandas as pd
 import streamlit as st
 
-
 from arango_things import arango_db
-from things import normalize_party_names
 from streamlit_info import (
     party_colors,
     explainer,
@@ -23,7 +21,7 @@ from streamlit_info import (
 )
 
 from langchain.schema import Document
-
+from llama_server import LLM
 
 class Params:
     """Class containing parameters for URL.
@@ -171,7 +169,8 @@ def summarize(df_party, user_input):
 
     {texts}
 
-    Please make a very short summary of the standpoints of the {party} party in the EU parliament, in relation to the user search. Make note of the speaker and the dates for the speeches.
+    You are a journalist and are going to write a short summary of the standpoints of the {party} party in the EU parliament, in relation to the user search. 
+    Make note of the speaker and the dates for the speeches.
     
     Example 1:
     In 2020, Parliamentarian NN, ({party}) wanted to decrease the budget for the EU parliament. Later, 2022, she wanted to incrase the budget.
@@ -185,11 +184,48 @@ def summarize(df_party, user_input):
 
     # Generate a summary of the party's standpoints.
     # Return the summaries dictionary.
-    system_prompt = "You are a journalist and have been asked to write a short summary of the standpoints of the party in the EU parliament."
+    
+    #TODO Have to understand sysmtem_prompt in llama_server
+    #system_prompt = "You are a journalist and have been asked to write a short summary of the standpoints of the party in the EU parliament." 
 
-    return ollama(
-        prompt=prompt, system_prompt=system_prompt, temperature=0.5, print_tokens=False
-    )
+    return llama.generate(prompt=prompt)
+
+
+def normalize_party_names(name):
+        """
+        Normalizes party names to the format used in the database.
+
+        Parameters:
+        name (str): The party name to be normalized.
+
+        Returns:
+        str: The normalized party name.
+        """
+
+        parties = {
+            "EPP": "EPP",
+            "PPE": "EPP",
+            "RE": "Renew",
+            "S-D": "S&D",
+            "S&D": "S&D",
+            "ID": "ID",
+            "ECR": "ECR",
+            "GUE/NGL": "GUE/NGL",
+            "The Left": "GUE/NGL",
+            "Greens/EFA": "Greens/EFA",
+            "G/EFA": "Greens/EFA",
+            "Verts/ALE": "Greens/EFA",
+            "NA": "NA",
+            "NULL": "NA",
+            None: "NA",
+            "-": "NA",
+            "Vacant": "NA",
+            "NI": "NA",
+            "Renew": "Renew"
+
+        }
+
+        return parties[name]
 
 
 def make_snippet(text, input_tokens, token_text_list, token_input_list):
@@ -214,7 +250,7 @@ def make_snippet(text, input_tokens, token_text_list, token_input_list):
         snippet = []
         text_lower = text.lower()
         # Calculate snippet length in words.
-        snippet_length = 40 * int(8 / len(input_tokens))  # * Change to another value?
+        snippet_length = 40 * int(10 / len(input_tokens)+1)  # * Change to another value?
 
         # Loop through each input token.
         for token in input_tokens:
@@ -635,6 +671,9 @@ partycodes = list(party_colors.keys())  # List of partycodes
 # Max hits returned by db.
 return_limit = 10000
 
+# Initialize LLM model.
+llama = LLM(temperature=0.5)
+
 # Ask for word to search for.
 user_input = st.text_input(
     " ",
@@ -644,18 +683,17 @@ user_input = st.text_input(
     help='You can use asterix (*), minus (-), quotationmarks ("") and OR.',
 )
 
-
 if len(user_input) > 3:
     params.q = user_input
 
     # print(user_input.upper())
-    # print(ollama(prompt=f'''A user wants to search in a database containing debates in the European Parliament and have made the input below. Take that input and write three questions would generate a good result if used for quering a vector database. Answer with a python style list containing the three questions.
+    # print(llama.generate(prompt=f'''A user wants to search in a database containing debates in the European Parliament and have made the input below. Take that input and write three questions would generate a good result if used for quering a vector database. Answer with a python style list containing the three questions.
 
     # User input: {user_input}
 
     # Questions: '''))
 
-    try:  #! When in procution, uncomment this.
+    try:  #! When in prodution, uncomment this.
         user_input = user_input.replace("'", '"')
         input_tokens = re.findall(r'(?:"[^"]*"|\S)+', user_input)
 
@@ -811,10 +849,15 @@ if len(user_input) > 3:
             st.session_state["hits"] = df.shape[0]
         else:
             if st.session_state["hits"] != df.shape[0]:
-                del st.session_state["df_excerpts"]
-                del st.session_state["excerpt_page"]
-                del st.session_state["text_next_page_button"]
+                if "df_excerpts" in st.session_state:
+                    del st.session_state["df_excerpts"]
+                if "excerpt_page" in st.session_state:
+                    del st.session_state["excerpt_page"]
+                if 'text_next_page_button' in st.session_state:
+                    del st.session_state["text_next_page_button"]
                 del st.session_state["disable_next_page_button"]
+                if 'disable_next_page_button' in st.session_state:
+                    del st.session_state["disable_next_page_button"]
                 st.session_state["hits"] = df.shape[0]
 
         ##! Show snippets.
@@ -871,9 +914,8 @@ if len(user_input) > 3:
             party_talks = pd.DataFrame(df_["Party"].value_counts())
             party_labels = party_talks.index.to_list()
             fig, ax1 = plt.subplots()
-
-            total = party_talks["Party"].sum()
-            mentions = party_talks["Party"]  #!
+            total = party_talks["count"].sum()
+            mentions = party_talks["count"]  #!
             ax1.pie(
                 mentions,
                 labels=party_labels,
@@ -904,7 +946,7 @@ if len(user_input) > 3:
             .mark_bar()
             .encode(
                 x="Year",
-                y="Mentions",
+                y="count",
                 color=alt.Color("color", scale=None),
                 tooltip=["Party", "Mentions"],
             )