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import gradio as gr
import faiss
import numpy as np
import pandas as pd
from sentence_transformers import SentenceTransformer
import google.generativeai as genai
import re
import os
# Load data and FAISS index
def load_data_and_index():
docs_df = pd.read_pickle("docs_with_embeddings (1).pkl") # Adjust path for HF Spaces
embeddings = np.array(docs_df['embeddings'].tolist(), dtype=np.float32)
dimension = embeddings.shape[1]
index = faiss.IndexFlatL2(dimension)
index.add(embeddings)
return docs_df, index
docs_df, index = load_data_and_index()
# Load SentenceTransformer
minilm = SentenceTransformer('all-MiniLM-L6-v2')
# Configure Gemini API using Hugging Face Secrets
GEMINI_API_KEY = os.environ.get("GEMINI_API_KEY")
if not GEMINI_API_KEY:
raise ValueError("Gemini API key not found. Please set it in Hugging Face Spaces secrets.")
genai.configure(api_key=GEMINI_API_KEY)
model = genai.GenerativeModel('gemini-2.0-flash')
# Preprocess text function
def preprocess_text(text):
text = text.lower()
text = text.replace('\n', ' ').replace('\t', ' ')
text = re.sub(r'[^\w\s.,;:>-]', ' ', text)
text = ' '.join(text.split()).strip()
return text
# Retrieve documents
def retrieve_docs(query, k=5):
query_embedding = minilm.encode([query], show_progress_bar=False)[0].astype(np.float32)
distances, indices = index.search(np.array([query_embedding]), k)
retrieved_docs = docs_df.iloc[indices[0]][['label', 'text', 'source']]
retrieved_docs['distance'] = distances[0]
return retrieved_docs
# RAG pipeline integrated into respond function
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p, # Keeping top_p as an input, though Gemini doesn’t use it directly
):
# Preprocess the user message
preprocessed_query = preprocess_text(message)
# Retrieve relevant documents
retrieved_docs = retrieve_docs(preprocessed_query, k=5)
context = "\n".join(retrieved_docs['text'].tolist())
# Construct the prompt with system message, history, and RAG context
prompt = f"{system_message}\n\n"
for user_msg, assistant_msg in history:
if user_msg:
prompt += f"User: {user_msg}\n"
if assistant_msg:
prompt += f"Assistant: {assistant_msg}\n"
prompt += (
f"Query: {message}\n"
f"Relevant Context: {context}\n"
f"Generate a short, concise, and to-the-point response to the query based only on the provided context."
)
# Generate response with Gemini
response = model.generate_content(
prompt,
generation_config=genai.types.GenerationConfig(
max_output_tokens=max_tokens,
temperature=temperature
)
)
answer = response.text.strip()
if not answer.endswith('.'):
last_period = answer.rfind('.')
if last_period != -1:
answer = answer[:last_period + 1]
else:
answer += "."
# Yield the full response (no streaming, as Gemini API doesn’t support it here)
yield answer
# Gradio Chat Interface
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(
value="You are a medical AI assistant diagnosing patients based on their query, using relevant context from past records of other patients.",
label="System message"
),
gr.Slider(minimum=1, maximum=2048, value=150, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.75, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p (nucleus sampling)", # Included but not used by Gemini
),
],
title="🏥 Medical Chat Assistant",
description="A chat-based medical assistant that diagnoses patient queries using AI and past records."
)
if __name__ == "__main__":
demo.launch() |