Update app.py

app.py

@@ -1,18 +1,9 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
 import json
-import uuid
 from PIL import Image
 from bs4 import BeautifulSoup
 import requests
-import random
-from transformers import LlavaProcessor, LlavaForConditionalGeneration, TextIteratorStreamer
-from threading import Thread
-import re
-import time 
-import torch
-import cv2
-from gradio_client import Client, file
 
 def extract_text_from_webpage(html_content):
     soup = BeautifulSoup(html_content, 'html.parser')
@@ -22,16 +13,14 @@ def extract_text_from_webpage(html_content):
 
 def search(query):
     term = query
-    start = 0
     all_results = []
     max_chars_per_page = 8000
     with requests.Session() as session:
         resp = session.get(
             url="https://www.google.com/search",
             headers={"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:109.0) Gecko/20100101 Firefox/111.0"},
-            params={"q": term, "num": 3, "udm": 14},
-            timeout=5,
-            verify=None,
+            params={"q": term, "num": 3},
+            timeout=5
         )
         resp.raise_for_status()
         soup = BeautifulSoup(resp.text, "html.parser")
@@ -40,7 +29,7 @@ def search(query):
             link = result.find("a", href=True)
             link = link["href"]
             try:
-                webpage = session.get(link, headers={"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:109.0) Gecko/20100101 Firefox/111.0"}, timeout=5, verify=False)
+                webpage = session.get(link, headers={"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:109.0) Gecko/20100101 Firefox/111.0"}, timeout=5)
                 webpage.raise_for_status()
                 visible_text = extract_text_from_webpage(webpage.text)
                 if len(visible_text) > max_chars_per_page:
@@ -55,9 +44,6 @@ client_gemma = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3")
 client_mixtral = InferenceClient("NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO")
 client_llama = InferenceClient("meta-llama/Meta-Llama-3-8B-Instruct")
 
-
-func_caller = []
-
 # Define the main chat function
 def respond(message, history):
     func_caller = []
@@ -71,7 +57,7 @@ def respond(message, history):
         func_caller.append({"role": "user", "content": f"{str(msg[0])}"})
         func_caller.append({"role": "assistant", "content": f"{str(msg[1])}"})
 
-    message_text = message["text"]
+    message_text = message
     func_caller.append({"role": "user", "content": f'[SYSTEM]You are a helpful assistant. You have access to the following functions: \n {str(functions_metadata)}\n\nTo use these functions respond with:\n<functioncall> {{ "name": "function_name", "arguments": {{ "arg_1": "value_1", "arg_1": "value_1", ... }} }}  </functioncall>  [USER] {message_text}'})
     
     response = client_gemma.chat_completion(func_caller, max_tokens=200)
@@ -90,9 +76,7 @@ def respond(message, history):
         json_data = json.loads(str(response))
         if json_data["name"] == "web_search":
             query = json_data["arguments"]["query"]
-            gr.Info("Searching Web")
             web_results = search(query)
-            gr.Info("Extracting relevant Info")
             web2 = ' '.join([f"Link: {res['link']}\nText: {res['text']}\n\n" for res in web_results])
             messages = f"system\nYou are OpenCHAT mini a helpful assistant made by KingNish. You are provided with WEB results from which you can find informations to answer users query in Structured and More better way. You do not say Unnecesarry things Only say thing which is important and relevant. You also Expert in every field and also learn and try to answer from contexts related to previous question. Try your best to give best response possible to user. You also try to show emotions using Emojis and reply like human, use short forms, friendly tone and emotions."
             for msg in history:
@@ -129,13 +113,16 @@ def respond(message, history):
             if not response.token.text == "":
                 output += response.token.text
                 yield output
-                    
+
+# Define the Gradio demo
 demo = gr.ChatInterface(
     fn=respond,
     chatbot=gr.Chatbot(show_copy_button=True, likeable=True, layout="panel"),
-    description =" ",
+    description="Ask anything and get responses based on web searches and AI models.",
     textbox=gr.MultimodalTextbox(),
     multimodal=True,
     concurrency_limit=200,
 )
+
+# Launch the Gradio demo
 demo.launch()