Update app.py

app.py

@@ -1,8 +1,6 @@
 import gradio as gr
 import os
-import random
 import time
-import numpy as np
 import json
 
 qs = {}
@@ -31,37 +29,31 @@ def app_inits():
     for filename in filenames:
         with open(filename, 'r') as file:
             data = json.load(file)
-            qs.update(data)
+            qs[filename] = data
         
     print("Loaded Q & A")
-    print("Keys:", list(qs.keys()))
-    print("CALL URL: http://127.0.0.1:7860/?dw=0.02&dl=0.0001")
+    print("JSON Files:", filenames)
         
     return    
 
-with gr.Blocks(theme = theme) as demo:
-    chatbot = gr.Chatbot(elem_id="chatbot", layout = "panel", avatar_images=("images/user.jpg", "images/bot.jpg"),)
+with gr.Blocks(theme=theme) as demo:
+    chatbot = gr.Chatbot(elem_id="chatbot", layout="panel", avatar_images=("images/user.jpg", "images/bot.jpg"),)
     
     def get_params(request: gr.Request):        
-        headers = request.headers
-        host = request.client.host
-        user_agent = request.headers["user-agent"]
         params = request.query_params
-        
         return str(params)
 
     with gr.Row(equal_height=True):
-        msg = gr.Textbox(show_label=False, placeholder="Message ChatGPT...", max_lines = 5,container = False,)
-        btn = gr.Button(value="", min_width=80, size = "lg", icon="images/button.jpg", scale= 0)
+        msg = gr.Textbox(show_label=False, placeholder="Message ChatGPT...", max_lines=5, container=False,)
+        btn = gr.Button(value="", min_width=80, size="lg", icon="images/button.jpg", scale=0)
         url_params = gr.State()
-     
 
     demo.load(get_params, None, url_params, queue=False)
 
     def user(user_message, history):
         return "", history + [[user_message, None]]
 
-    def bot( history, url_params):
+    def bot(history, url_params):
         global qs
         DelayBetweenWords = 0.1
         DelayBetweenLetters = 0.0001
@@ -71,28 +63,14 @@ with gr.Blocks(theme = theme) as demo:
             DelayBetweenWords = float(params.get("dw", DelayBetweenWords))
             DelayBetweenLetters = float(params.get("dl", DelayBetweenLetters))
         
-        keywords = list(qs.keys())
-
         text = history[-1][0]
-
-        file_location = 'images/log_data.txt'
-        full_path = os.path.abspath(file_location)
-
-        if text != "":
-           search_keyword = find_best_keyword_match(keywords, text)     
-        else:   
-           search_keyword = "None"
-
-        log_message("-ENTRY- [query]: " + text + " [params] " + url_params + " [keyword] " + search_keyword)
         
-        if search_keyword == "None":
-           bot_message = "Sorry, don't know any information about this."
-        else:
-           output_text = qs.get(search_keyword, "Keyword identified. No information found.")
-           bot_message = output_text
+        bot_message = "Sorry, I couldn't find an answer to your question."
 
-        if text == "debug":
-           bot_message = "[DEBUG (log file: " + full_path + " params: " + url_params + "\nWords Delay=" + str(DelayBetweenWords) +  " sec. Letters Delay=" + str(DelayBetweenLetters) + " sec.) \nKeys:\n" + "\n".join(keywords) + "]"
+        for filename, data in qs.items():
+            if text in data:
+                bot_message = data[text]
+                break
 
         history[-1][1] = ""
         
@@ -106,8 +84,6 @@ with gr.Blocks(theme = theme) as demo:
 
             yield history
     
-        log_message("-EXIT- [query]: " + text + " [params] " + url_params + " [keyword] " + search_keyword)
-
     btn.click(user, [msg, chatbot], [msg, chatbot], queue=False, show_progress=False).then(
         bot, [chatbot, url_params], chatbot, concurrency_limit=50
     )
@@ -116,57 +92,6 @@ with gr.Blocks(theme = theme) as demo:
         bot, [chatbot, url_params], chatbot, concurrency_limit=50
     )
 
-def normalize(s):
-    """Lowercase and strip the string to normalize it."""
-    return s.lower().strip()
-
-def levenshtein_distance(s1, s2):
-    """Calculate the Levenshtein distance between two strings."""
-    if len(s1) < len(s2):
-        return levenshtein_distance(s2, s1)
-
-    if len(s2) == 0:
-        return len(s1)
-
-    previous_row = range(len(s2) + 1)
-    for i, c1 in enumerate(s1):
-        current_row = [i + 1]
-        for j, c2 in enumerate(s2):
-            insertions = previous_row[j + 1] + 1
-            deletions = current_row[j] + 1
-            substitutions = previous_row[j] + (c1 != c2)
-            current_row.append(min(insertions, deletions, substitutions))
-        previous_row = current_row
-    
-    return previous_row[-1]
-
-def find_best_keyword_match(keywords, text, max_distance=3):
-    """Find the best keyword match in the text, allowing for some misspelling."""
-    text_normalized = normalize(text)
-    best_match = None
-    lowest_distance = float('inf')
-    
-    for keyword in keywords:
-        keyword_normalized = normalize(keyword)
-        if ' ' in keyword_normalized:
-            slice_length = len(keyword_normalized)
-            for i in range(len(text_normalized) - slice_length + 1):
-                text_slice = text_normalized[i:i+slice_length]
-                distance = levenshtein_distance(text_slice, keyword_normalized)
-                if distance < lowest_distance:
-                    best_match = keyword
-                    lowest_distance = distance
-        else:
-            for word in text_normalized.split():
-                distance = levenshtein_distance(word, keyword_normalized)
-                if distance < lowest_distance:
-                    best_match = keyword
-                    lowest_distance = distance
-
-    if lowest_distance <= max_distance:
-        return best_match
-    return "None"
-
 from datetime import datetime
 
 filename = 'images/log_data.txt'