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| import gradio as gr | |
| import openai | |
| import os | |
| from dotenv import load_dotenv | |
| import requests | |
| from transformers import BertTokenizer, BertForSequenceClassification | |
| import torch | |
| import faiss | |
| import numpy as np | |
| # Load .env | |
| load_dotenv() | |
| # API Keys and Org ID | |
| openai.api_key = os.getenv("OPENAI_API_KEY") | |
| openai.organization = os.getenv("OPENAI_ORG_ID") | |
| serper_api_key = os.getenv("SERPER_API_KEY") | |
| # Load PubMedBERT tokenizer and model | |
| tokenizer = BertTokenizer.from_pretrained("microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract") | |
| model = BertForSequenceClassification.from_pretrained("microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract", num_labels=2) | |
| # FAISS setup for vector search | |
| dimension = 768 | |
| index = faiss.IndexFlatL2(dimension) | |
| # Function to embed text (PubMedBERT) | |
| def embed_text(text): | |
| inputs = tokenizer(text, return_tensors="pt", truncation=True, padding="max_length", max_length=512) | |
| outputs = model(**inputs, output_hidden_states=True) | |
| hidden_state = outputs.hidden_states[-1] | |
| return hidden_state.mean(dim=1).detach().numpy() | |
| # Function to retrieve info from PubMedBERT | |
| def handle_fda_query(query): | |
| inputs = tokenizer(query, return_tensors="pt", padding="max_length", truncation=True, max_length=512) | |
| outputs = model(**inputs) | |
| logits = outputs.logits | |
| prediction = torch.argmax(logits, dim=1).item() | |
| # Simulate a meaningful FDA-related response | |
| if prediction == 1: | |
| return f"FDA Query Processed: '{query}' contains important regulatory information." | |
| else: | |
| return f"FDA Query Processed: '{query}' seems to be general and not regulatory-heavy." | |
| # Function to enhance info via GPT-4o-mini | |
| def enhance_with_gpt4o(fda_response): | |
| try: | |
| response = openai.ChatCompletion.create( | |
| model="gpt-4o-mini", # Correct model | |
| messages=[{"role": "system", "content": "You are an expert FDA assistant."}, {"role": "user", "content": f"Enhance this FDA info: {fda_response}"}], | |
| max_tokens=150 | |
| ) | |
| return response['choices'][0]['message']['content'] | |
| except Exception as e: | |
| return f"Error: {str(e)}" | |
| # Main function that gets PubMedBERT output and enhances it using GPT-4o-mini | |
| def respond(message, system_message, max_tokens, temperature, top_p): | |
| try: | |
| # First retrieve info via PubMedBERT | |
| fda_response = handle_fda_query(message) | |
| # Then enhance this info via GPT-4o-mini | |
| enhanced_response = enhance_with_gpt4o(fda_response) | |
| # Return both the PubMedBERT result and the enhanced version | |
| return f"Original Info from PubMedBERT: {fda_response}\n\nEnhanced Info via GPT-4o-mini: {enhanced_response}" | |
| except Exception as e: | |
| return f"Error: {str(e)}" | |
| # Gradio Interface | |
| demo = gr.Interface( | |
| fn=respond, | |
| inputs=[ | |
| gr.Textbox(label="Enter your FDA query", placeholder="Ask Ferris2.0 anything FDA-related."), | |
| gr.Textbox(value="You are Ferris2.0, the most advanced FDA Regulatory Assistant.", label="System message"), | |
| gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), | |
| gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), | |
| gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)") | |
| ], | |
| outputs="text", | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() |