Update app.py

app.py

@@ -23,21 +23,6 @@ import certifi
 from bs4 import BeautifulSoup
 import requests
 
-
-# List of available models
-AVAILABLE_MODELS = [
-    "mistralai/Mistral-7B-Instruct-v0.1",
-    "meta-llama/Llama-2-70b-chat-hf",
-    "tiiuae/falcon-40b-instruct",
-    "bigscience/bloomz",
-    "google/flan-t5-xxl",
-    "EleutherAI/gpt-neox-20b",
-    "mistralai/Mistral-Nemo-Instruct-2407",  # Current default model
-]
-
-# Load environment variables from a .env file
-load_dotenv()
-
 # Load environment variables from a .env file
 load_dotenv()
 
@@ -51,10 +36,10 @@ SEARXNG_KEY = 'f9f07f93b37b8483aadb5ba717f556f3a4ac507b281b4ca01e6c6288aa3e3ae5'
 
 # Use the environment variable
 HF_TOKEN = os.getenv('HF_TOKEN')
-
-# Modify the client initialization to use the selected model
-
-client = InferenceClient(model_name, token=HF_TOKEN)
+client = InferenceClient(
+    "meta-llama/Llama-3.1-8B-Instruct",
+    token=HF_TOKEN,
+)
 
 # Initialize the similarity model
 similarity_model = SentenceTransformer('all-MiniLM-L6-v2')
@@ -326,7 +311,7 @@ Your response should be detailed, informative, accurate, and directly relevant t
     try:
         response = llm_client.chat_completion(
             messages=messages,
-            max_tokens=10000,
+            max_tokens=7000,
             temperature=temperature
         )
         return response.choices[0].message.content.strip()
@@ -550,13 +535,9 @@ def search_and_scrape(query, chat_history, num_results=5, scraper="trafilatura",
         return f"An unexpected error occurred during the search and scrape process: {e}"
 
 
-# Update the chat_function to include the model selection
-def chat_function(message, history, num_results, scraper, max_chars, time_range, language, category, engines, safesearch, method, llm_temperature, model_name):
+def chat_function(message, history, num_results, scraper, max_chars, time_range, language, category, engines, safesearch, method, llm_temperature):
     chat_history = "\n".join([f"{role}: {msg}" for role, msg in history])
     
-    # Get the client for the selected model
-    client = client(model_name)
-    
     response = search_and_scrape(
         query=message,
         chat_history=chat_history,
@@ -569,13 +550,11 @@ def chat_function(message, history, num_results, scraper, max_chars, time_range,
         engines=engines,
         safesearch=safesearch,
         method=method,
-        llm_temperature=llm_temperature,
-        client=client  # Pass the client to the search_and_scrape function
+        llm_temperature=llm_temperature
     )
     
     yield response
 
-# Update the Gradio interface to include the model selection dropdown
 iface = gr.ChatInterface(
     chat_function,
     title="SearXNG Scraper for Financial News",
@@ -596,7 +575,6 @@ iface = gr.ChatInterface(
         gr.Slider(0, 2, value=2, step=1, label="Safe Search Level"),
         gr.Radio(["GET", "POST"], value="POST", label="HTTP Method"),
         gr.Slider(0, 1, value=0.2, step=0.1, label="LLM Temperature"),
-        gr.Dropdown(AVAILABLE_MODELS, value="mistralai/Mistral-Nemo-Instruct-2407", label="Model Selection"),
     ],
     additional_inputs_accordion=gr.Accordion("⚙️ Advanced Parameters", open=True),
     retry_btn="Retry",
@@ -611,5 +589,5 @@ iface = gr.ChatInterface(
 )
 
 if __name__ == "__main__":
-    logger.info("Starting the SearXNG Scraper for Financial News using ChatInterface with Advanced Parameters and Model Selection")
+    logger.info("Starting the SearXNG Scraper for Financial News using ChatInterface with Advanced Parameters")
     iface.launch(share=True)