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| import streamlit as st | |
| import requests | |
| import os | |
| import sqlite3 | |
| import time | |
| import uuid | |
| import datetime | |
| import hashlib | |
| import json | |
| import pandas as pd | |
| # FastAPI base URL | |
| #BASE_URL = "http://localhost:8000" | |
| import os | |
| API_URL=os.getenv("API_URL") | |
| API_TOKEN=os.getenv("API_TOKEN") | |
| BASE_URL=API_URL | |
| #API_URL = "https://api-inference.huggingface.co/models/your-username/your-private-model" | |
| headers = {"Authorization":f"Bearer {API_TOKEN}"} | |
| def query(payload): | |
| response = requests.post(API_URL, headers=headers, json=payload) | |
| return response.json() | |
| #data = query({"inputs": "Hello, how are you?"}) | |
| #print(data) | |
| st.title("Generative AI Demos") | |
| def generate_unique_hash(filename: str, uuid: str) -> str: | |
| # Generate a UUID for the session or device | |
| device_uuid = uuid | |
| # Get the current date and time | |
| current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") | |
| # Combine filename, current time, and UUID into a single string | |
| combined_string = f"{filename}-{current_time}-{device_uuid}" | |
| # Generate a hash using SHA256 | |
| unique_hash = hashlib.sha256(combined_string.encode()).hexdigest() | |
| return unique_hash | |
| # Function to generate or retrieve a UUID from local storage | |
| uuid_script = """ | |
| <script> | |
| if (!localStorage.getItem('uuid')) { | |
| localStorage.setItem('uuid', '""" + str(uuid.uuid4()) + """'); | |
| } | |
| const uuid = localStorage.getItem('uuid'); | |
| const streamlitUUIDInput = window.parent.document.querySelector('input[data-testid="stTextInput"][aria-label="UUID"]'); | |
| if (streamlitUUIDInput) { | |
| streamlitUUIDInput.value = uuid; | |
| } | |
| </script> | |
| """ | |
| ga_script = """ | |
| <!-- Google tag (gtag.js) --> | |
| <script async src="https://www.googletagmanager.com/gtag/js?id=G-PWP4PRW5G5"></script> | |
| <script> | |
| window.dataLayer = window.dataLayer || []; | |
| function gtag(){dataLayer.push(arguments);} | |
| gtag('js', new Date()); | |
| gtag('config', 'G-PWP4PRW5G5'); | |
| </script> | |
| """ | |
| # Add Google Analytics to the Streamlit app | |
| st.components.v1.html(ga_script, height=0, width=0) | |
| # Execute the JavaScript in the Streamlit app | |
| st.components.v1.html(uuid_script, height=0, width=0) | |
| # Store and display UUID in a non-editable text field using session state | |
| if 'uuid' not in st.session_state: | |
| st.session_state['uuid'] = str(uuid.uuid4()) | |
| uuid_from_js = st.session_state['uuid'] | |
| # Retrieve UUID from DOM | |
| if uuid_from_js is None: | |
| st.error("Unable to retrieve UUID from the browser.") | |
| else: | |
| # Display UUID in a non-editable text field | |
| st.text_input("Your UUID", value=uuid_from_js, disabled=True) | |
| # Define tabs | |
| tab1, tab2,tab3 = st.tabs(["Review Analyzer", "Presentation Creator","Semantic Search"]) | |
| with tab1: | |
| st.header("Review Analyzer") | |
| uploaded_file = st.file_uploader("Upload your reviews CSV file", type=["csv"],key=2) | |
| if uploaded_file is not None: | |
| en1 = generate_unique_hash(uploaded_file.name, uuid_from_js) | |
| files = {"file": (en1, uploaded_file.getvalue(), "text/csv")} | |
| st.info("Calling model inference. Please wait...") | |
| response = requests.post(f"{BASE_URL}/upload/", files=files, headers=headers) | |
| if response.status_code == 200: | |
| st.info("Processing started. Please wait...") | |
| # Poll for completion | |
| while True: | |
| status_response = requests.get(f"{BASE_URL}/status/{en1}", headers=headers) | |
| if status_response.status_code == 200 and (status_response.json()["status"] == "complete" or status_response.json()["status"] == "error"): | |
| if status_response.json()["status"] == "complete": | |
| st.success("File processed successfully.") | |
| download_response = requests.get(f"{BASE_URL}/download/{en1}", headers=headers) | |
| if download_response.status_code == 200: | |
| st.download_button( | |
| label="Download Processed File", | |
| data=download_response.content, | |
| file_name=f"processed_{en1}", | |
| mime="text/csv" | |
| ) | |
| break | |
| time.sleep(10) | |
| else: | |
| st.error("Failed to upload file for processing.") | |
| with tab2: | |
| st.header("Presentation Creator") | |
| # Input URL for presentation creation | |
| presentation_url = st.text_input("Enter the URL for the presentation content") | |
| if presentation_url: | |
| #unique_id = generate_unique_hash(presentation_url, str(uuid.uuid4())) | |
| st.info("Creating presentation. Please wait...") | |
| # Mock payload for processing the URL | |
| payload = {"url": presentation_url, "id": uuid_from_js} | |
| response = requests.post(f"{BASE_URL}/presentation_creator", json=payload, headers=headers) | |
| print("response",response) | |
| if response.status_code == 200: | |
| st.info("Processing started. Please wait...") | |
| unique_id=response.json()["filename"] | |
| # Poll for completion | |
| print("unique id is ",unique_id,BASE_URL) | |
| while True: | |
| status_response = requests.get(f"{BASE_URL}/status/{unique_id}", headers=headers) | |
| print("status_response",status_response) | |
| if status_response.status_code == 200 and (status_response.json()["status"] == "complete" or status_response.json()["status"] == "error"): | |
| if status_response.json()["status"] == "complete": | |
| st.success("Presentation created successfully.") | |
| download_response = requests.get(f"{BASE_URL}/download/{unique_id}", headers=headers) | |
| if download_response.status_code == 200: | |
| st.download_button( | |
| label="Download Presentation File", | |
| data=download_response.content, | |
| file_name=f"presentation_{unique_id}.pptx", | |
| mime="application/pdf" | |
| ) | |
| else: | |
| st.error("error in downloading the presentation file ") | |
| else: | |
| st.error("error in creating presentation") | |
| break | |
| time.sleep(10) | |
| else: | |
| st.error("Failed to create presentation.") | |
| with tab3: | |
| st.header("Semantic Search") | |
| # Create a form for the inputs and submit button | |
| with st.form(key='semantic_search_form'): | |
| # Input URL for presentation creation | |
| presentation_url = st.text_input("Enter the URL for the semantic search") | |
| search_query = st.text_input("Enter your query") | |
| # Submit button inside the form | |
| submit_button = st.form_submit_button(label="Submit") | |
| if submit_button: | |
| if presentation_url and search_query: | |
| #unique_id = generate_unique_hash(presentation_url, str(uuid.uuid4())) | |
| st.info("Performing semantic search. Please wait...") | |
| # Mock payload for processing the URL | |
| payload = {"url": presentation_url, "id": uuid_from_js,"search_query":search_query} | |
| response = requests.post(f"{BASE_URL}/semantic_search", json=payload, headers=headers) | |
| print("response",response.json()) | |
| if response.status_code == 200: | |
| st.info("Processing started. Please wait...") | |
| unique_id=response.json()["filename"] | |
| # Poll for completion | |
| print("unique id is ",unique_id,BASE_URL) | |
| while True: | |
| status_response = requests.get(f"{BASE_URL}/status/{unique_id}", headers=headers) | |
| print("status_response",status_response.json()) | |
| if status_response.status_code == 200 and (status_response.json()["status"] == "complete" or status_response.json()["status"] == "error"): | |
| if status_response.json()["status"] == "complete": | |
| st.success("Presentation created successfully.") | |
| download_response = requests.get(f"{BASE_URL}/download/{unique_id}", headers=headers) | |
| if download_response.status_code == 200: | |
| #print("download_response",download_response.content) | |
| # Load JSON data into a Python list of dictionaries | |
| data = json.loads(download_response.content) | |
| # Convert the list of dictionaries to a DataFrame | |
| df = pd.DataFrame(data) | |
| df = df["page_content"] | |
| # Display the DataFrame in Streamlit as an interactive dataframe | |
| #st.dataframe(df) | |
| df = df.str.split(' ##### ', 1).str[1].str.strip() | |
| # Alternatively, display it as a static table | |
| st.table(df) | |
| else: | |
| st.error("error in downloading the presentation file ") | |
| else: | |
| st.error("error in creating presentation") | |
| break | |
| time.sleep(2) | |
| else: | |
| st.error("Failed to create presentation.") | |
| else: | |
| st.error("Please enter both a URL and a query.") | |
| # uploaded_file = st.file_uploader("Upload your reviews CSV file", type=["csv"],key=1) | |
| # if uploaded_file is not None: | |
| # # Save uploaded file to FastAPI | |
| # en1 = generate_unique_hash(uploaded_file.name, uuid_from_js) | |
| # files = {"file": (en1, uploaded_file.getvalue(), "text/csv")} | |
| # st.info("Calling model inference. Please wait...") | |
| # response = requests.post(f"{BASE_URL}/upload/", files=files,headers=headers) | |
| # print("response to file upload is ",response) | |
| # if response.status_code == 200: | |
| # st.info("Processing started. Please wait...") | |
| # # Poll for completion | |
| # while True: | |
| # status_response = requests.get(f"{BASE_URL}/status/{en1}",headers=headers) | |
| # if status_response.status_code == 200 and (status_response.json()["status"] == "complete" or status_response.json()["status"]=="error"): | |
| # if status_response.json()["status"] == "complete": | |
| # st.success("File processed successfully.") | |
| # download_response = requests.get(f"{BASE_URL}/download/{en1}",headers=headers) | |
| # if download_response.status_code == 200: | |
| # st.download_button( | |
| # label="Download Processed File", | |
| # data=download_response.content, | |
| # file_name=f"processed_{en1}", | |
| # mime="text/csv" | |
| # ) | |
| # break | |
| # time.sleep(10) | |
| # else: | |
| # st.error("Failed to upload file for processing.") |