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app.py
CHANGED
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import gradio as gr
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from huggingface_hub import InferenceClient
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from transformers import BertTokenizer, BertForSequenceClassification
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import openai
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import os
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import faiss
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import requests
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from datasets import load_dataset
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# Load
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openai.api_key = os.getenv("OPENAI_API_KEY") # Ensure the OpenAI API key is pulled correctly
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serper_api_key = os.getenv("SERPER_API_KEY") # Ensure the Serper API key is pulled correctly
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# Load PubMedBERT tokenizer and model for FDA-related processing
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tokenizer = BertTokenizer.from_pretrained("microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract")
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model = BertForSequenceClassification.from_pretrained("microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract", num_labels=2)
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dimension = 768 # PubMedBERT embedding size
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index = faiss.IndexFlatL2(dimension)
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def embed_text(text):
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inputs = tokenizer(text, return_tensors="pt", truncation=True, padding="max_length", max_length=512)
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outputs = model(**inputs, output_hidden_states=True)
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hidden_state = outputs.hidden_states[-1]
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return hidden_state.mean(dim=1).detach().numpy()
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#
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past_conversation = "FDA approval for companion diagnostics requires careful documentation."
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past_embedding = embed_text(past_conversation)
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index.add(past_embedding)
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#
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def search_memory(query):
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query_embedding = embed_text(query)
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D, I = index.search(query_embedding, k=1)
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return I
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#
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def handle_fda_query(query):
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inputs = tokenizer(query, return_tensors="pt", padding="max_length", truncation=True)
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outputs = model(**inputs)
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response = "Processed FDA-related query via PubMedBERT"
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return response
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#
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def handle_openai_query(prompt):
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response = openai.
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max_tokens=100
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)
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return response.choices[0].
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# Web search with Serper API
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def web_search(query):
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url = f"https://google.serper.dev/search"
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headers = {
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"X-API-KEY":
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}
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params = {
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"q": query
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response = requests.get(url, headers=headers, params=params)
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return response.json()
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#
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def respond(
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message,
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history: list[tuple[str, str]],
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temperature,
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top_p,
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):
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# Prepare
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messages = [{"role": "system", "content": system_message}]
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for val in history:
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messages.append({"role": "user", "content": message})
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# Check if
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openai_response = handle_openai_query(f"Is this query FDA-related: {message}")
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if "FDA" in openai_response or "regulatory" in openai_response:
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# Search past conversations/memory using FAISS
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memory_index = search_memory(message)
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if memory_index:
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return f"Found relevant past memory: {past_conversation}"
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# If no memory match, proceed with PubMedBERT
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return handle_fda_query(message)
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# If query asks for
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if "search the web" in message.lower():
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return web_search(message)
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#
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temperature=temperature,
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top_p=top_p,
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):
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token = message.choices[0].delta.content
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# Create Gradio ChatInterface for interaction
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],
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)
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if __name__ == "__main__":
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demo.launch(
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import gradio as gr
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from huggingface_hub import InferenceClient
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from transformers import BertTokenizer, BertForSequenceClassification
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import openai
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import os
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import faiss
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import requests
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from datasets import load_dataset
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# Load OpenAI API key and organization ID from environment variables
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openai.api_key = os.getenv("OPENAI_API_KEY")
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openai.Organization = os.getenv("OPENAI_ORG_ID")
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# Load PubMedBERT tokenizer and model
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tokenizer = BertTokenizer.from_pretrained("microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract")
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model = BertForSequenceClassification.from_pretrained("microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract", num_labels=2)
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dimension = 768 # PubMedBERT embedding size
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index = faiss.IndexFlatL2(dimension)
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# Embed text using PubMedBERT
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def embed_text(text):
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inputs = tokenizer(text, return_tensors="pt", truncation=True, padding="max_length", max_length=512)
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outputs = model(**inputs, output_hidden_states=True)
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hidden_state = outputs.hidden_states[-1]
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return hidden_state.mean(dim=1).detach().numpy()
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# Add past conversation embedding to FAISS index
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past_conversation = "FDA approval for companion diagnostics requires careful documentation."
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past_embedding = embed_text(past_conversation)
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past_embedding = np.array(past_embedding) # Convert to numpy array
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# Reshape if necessary (e.g., (1, 768) for PubMedBERT)
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past_embedding = past_embedding.reshape(1, -1)
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index.add(past_embedding)
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# Search past conversations/memory using FAISS
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def search_memory(query):
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query_embedding = embed_text(query)
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D, I = index.search(query_embedding, k=1)
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return I
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# Handle FDA-specific queries with PubMedBERT
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def handle_fda_query(query):
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inputs = tokenizer(query, return_tensors="pt", padding="max_length", truncation=True)
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outputs = model(**inputs)
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response = "Processed FDA-related query via PubMedBERT"
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return response
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# Handle general queries using GPT-4O
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def handle_openai_query(prompt):
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response = openai.Chat.create(
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model="gpt-4-0314-16k-512",
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messages=[
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{"role": "user", "content": prompt}
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],
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temperature=0.7,
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max_tokens=100
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)
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return response.choices[0].message.content
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# Web search with Serper API
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def web_search(query):
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url = f"https://google.serper.dev/search"
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headers = {
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"X-API-KEY": os.getenv("SERPER_API_KEY")
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}
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params = {
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"q": query
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response = requests.get(url, headers=headers, params=params)
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return response.json()
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# Contextual Short-Term Memory (CSTM)
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cstm = []
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# Long-Term Memory (LTM)
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ltm = [] # Load knowledge base articles or FAQs
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# Semantic search function
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def semantic_search(query, cstm, ltm):
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# Generate embeddings for query and CSTM/LTM
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query_embedding = embed_text(query)
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cstm_embeddings = [embed_text(text) for text in cstm]
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ltm_embeddings = [embed_text(text) for text in ltm]
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# Calculate similarity scores
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cstm_scores = calculate_similarity(query_embedding, cstm_embeddings)
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ltm_scores = calculate_similarity(query_embedding, ltm_embeddings)
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# Retrieve top relevant results from CSTM and LTM
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top_cstm = np.argmax(cstm_scores)
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top_ltm = np.argmax(ltm_scores)
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return top_cstm, top_ltm
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# Calculate similarity between embeddings
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def calculate_similarity(query_embedding, embeddings):
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similarity_scores = []
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for embedding in embeddings:
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score = cosine_similarity(query_embedding, embedding)
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similarity_scores.append(score)
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return similarity_scores
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# Cosine similarity function
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def cosine_similarity(a, b):
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dot_product = np.dot(a, b)
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magnitude_a = np.linalg.norm(a)
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magnitude_b = np.linalg.norm(b)
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return dot_product / (magnitude_a * magnitude_b)
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# Main assistant function
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def respond(
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message,
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history: list[tuple[str, str]],
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temperature,
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top_p,
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):
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# Prepare context for OpenAI and PubMedBERT
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messages = [{"role": "system", "content": system_message}]
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for val in history:
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messages.append({"role": "user", "content": message})
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# Check if query is FDA-related
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openai_response = handle_openai_query(f"Is this query FDA-related: {message}")
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if "FDA" in openai_response or "regulatory" in openai_response:
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# Search past conversations/memory using FAISS
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memory_index = search_memory(message)
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if memory_index:
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return f"Found relevant past memory: {past_conversation}"
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# If no memory match, proceed with PubMedBERT
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return handle_fda_query(message)
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# If query asks for web search, perform web search
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if "search the web" in message.lower():
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return web_search(message)
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# Perform semantic search on CSTM and LTM
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top_cstm, top_ltm = semantic_search(message, cstm, ltm)
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if top_cstm:
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return f"Found relevant context: {cstm[top_cstm]}"
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elif top_ltm:
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return f"Found relevant knowledge: {ltm[top_ltm]}"
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# General conversational handling with GPT-4O
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response = handle_openai_query(message)
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return response
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# Create Gradio ChatInterface for interaction
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],
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)
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if __name__ == "__main__":
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demo.launch()
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