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Runtime error
| import streamlit as st | |
| import requests | |
| # Set the page configuration | |
| st.set_page_config(page_title="RadiantScriptor") | |
| # Function to call the Hugging Face model API | |
| def query_huggingface_model(prompt): | |
| API_TOKEN = "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" | |
| API_URL = "https://poxj7ux0l7kszkjs.us-east-1.aws.endpoints.huggingface.cloud" | |
| headers = {"Authorization": f"Bearer {API_TOKEN}"} | |
| response = requests.post(API_URL, headers=headers, json={"inputs": prompt}) | |
| if response.status_code == 200: | |
| return response.json() | |
| else: | |
| return {"error": response.text} | |
| st.title("RadiantScriptor") | |
| # User input for uploading a text file | |
| uploaded_file = st.file_uploader("Upload a text file", type=["txt"]) | |
| user_prompt = "" | |
| if uploaded_file is not None: | |
| # Read the contents of the uploaded text file | |
| user_prompt = uploaded_file.read().decode("utf-8") | |
| # Display the content to the user (optional) | |
| st.text_area("Uploaded Text:", value=user_prompt, height=150) | |
| if st.button("Generate Report-Findings"): | |
| with st.spinner('Generating report...'): | |
| # Query the Hugging Face model API | |
| response = query_huggingface_model(user_prompt) | |
| if "error" in response: | |
| st.error(f"Error: {response['error']}") | |
| else: | |
| # Assuming the response is a JSON object containing the generated text | |
| report = response[0]['generated_text'] # Adjust based on the actual response structure | |
| # Display the report | |
| st.text_area("Generated findings:", value=report, height=300) | |