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import streamlit as st
import requests
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load the Llama 3.2 model and tokenizer
model_name = "your-llama-3-2-model" # Replace with the correct model path in Hugging Face Hub
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
def search_api_call(query: str):
api_key = '614e992d87c496fb15a81a2039e00e6a42530f5c' # Replace with your actual API key
url = f"https://api.serper.dev/search?api_key={api_key}&q={query}"
try:
response = requests.get(url)
data = response.json()
return data['organic_results'] # Adjust according to the API response structure
except Exception as e:
st.error("Error fetching from the API: " + str(e))
return None
def is_llm_insufficient(llm_response: str) -> bool:
return "I don't know" in llm_response or llm_response.strip() == ''
def summarize_search_results(results):
summary = ""
for result in results:
summary += f"- {result['title']} ({result['link']})\n"
return summary
def generate_llm_response(user_query: str) -> str:
inputs = tokenizer(user_query, return_tensors="pt")
outputs = model.generate(**inputs, max_length=150)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
# Streamlit user interface
st.title("Real-Time Factual Information Fetcher")
user_query = st.text_input("Enter your query:")
if st.button("Submit"):
if user_query:
llm_response = generate_llm_response(user_query)
if is_llm_insufficient(llm_response) or 'recent' in user_query.lower():
search_results = search_api_call(user_query)
if search_results:
search_summary = summarize_search_results(search_results)
combined_response = f"{llm_response}\n\nHere are some recent findings:\n{search_summary.strip()}"
else:
combined_response = llm_response # Fallback to the original LLM response
else:
combined_response = llm_response
st.markdown("### Response:")
st.markdown(combined_response)
else:
st.warning("Please enter a query.")
if __name__ == "__main__":
import streamlit as st |