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app.py
CHANGED
@@ -1,28 +1,239 @@
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import json
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import numpy as np
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import faiss
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import
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from sentence_transformers import SentenceTransformer
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# Load FAISS index
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FAISS_INDEX_PATH = "faiss_medical.index"
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index = faiss.read_index(FAISS_INDEX_PATH)
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# Load embedding model
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embed_model = SentenceTransformer("all-MiniLM-L6-v2")
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# Load FAISS ID β Text Mapping
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with open("id_to_text.json", "r") as f:
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id_to_text = json.load(f)
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# Convert JSON keys to integers (FAISS returns int IDs)
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id_to_text = {int(k): v for k, v in id_to_text.items()}
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# Initialize Groq client
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client = Groq(api_key=os.getenv("GROQ_API_KEY"))
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def retrieve_medical_summary(query, k=3):
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"""
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Retrieve the most relevant medical literature from FAISS.
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@@ -43,12 +254,30 @@ def retrieve_medical_summary(query, k=3):
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# Retrieve the closest matching text using FAISS index IDs
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retrieved_docs = [id_to_text.get(int(idx), "No relevant data found.") for idx in I[0]]
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# Ensure all retrieved texts are strings (Flatten lists if needed)
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retrieved_docs = [doc if isinstance(doc, str) else " ".join(doc) for doc in retrieved_docs]
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# Join multiple retrieved documents into one response
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return "\n\n---\n\n".join(retrieved_docs) if retrieved_docs else "No relevant data found."
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def generate_medical_answer_groq(query, model="llama-3.3-70b-versatile", max_tokens=500, temperature=0.3):
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"""
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Generates a medical response using Groq's API with LLaMA 3.3-70B, after retrieving relevant literature from FAISS.
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@@ -63,7 +292,7 @@ def generate_medical_answer_groq(query, model="llama-3.3-70b-versatile", max_tok
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str: The AI-generated medical advice.
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"""
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# Retrieve relevant medical literature from FAISS
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retrieved_summary = retrieve_medical_summary(query)
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print("\nπ Retrieved Medical Text for Query:", query)
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print(retrieved_summary, "\n")
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@@ -72,7 +301,7 @@ def generate_medical_answer_groq(query, model="llama-3.3-70b-versatile", max_tok
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return "No relevant medical data found. Please consult a healthcare professional."
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try:
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# Send request to Groq API
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response = client.chat.completions.create(
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model=model,
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messages=[
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@@ -88,6 +317,10 @@ def generate_medical_answer_groq(query, model="llama-3.3-70b-versatile", max_tok
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except Exception as e:
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return f"Error generating response: {str(e)}"
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# Gradio Interface
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def ask_medical_question(question):
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return generate_medical_answer_groq(question)
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from datasets import load_dataset
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# Load dataset from Hugging Face
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dataset = load_dataset("MedRAG/textbooks")
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# Preview dataset
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print(dataset)
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import pandas as pd
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# Convert to Pandas DataFrame
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df = pd.DataFrame(dataset["train"])
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# Display first rows
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print(df.head())
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# Check file format
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print(df.dtypes)
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import nltk
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import shutil
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# Supprimer les ressources existantes
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nltk.data.path.append('/root/nltk_data') # Ajouter le chemin de nltk_data
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nltk.data.clear_cache() # Effacer le cache des donnΓ©es
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# RΓ©installer le package 'punkt'
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nltk.download('all')
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import re
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import nltk
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from nltk.corpus import stopwords
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from nltk.tokenize import word_tokenize, sent_tokenize
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from nltk.stem import WordNetLemmatizer
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# Download necessary NLTK components
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nltk.download("stopwords")
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nltk.download("punkt")
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nltk.download("wordnet")
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nltk.download("omw-1.4")
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# Load stopwords and lemmatizer
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stop_words = set(stopwords.words("english"))
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lemmatizer = WordNetLemmatizer()
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# Step 1: Preprocessing Function
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def preprocess_text(text):
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text = text.lower() # Convert to lowercase
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text = re.sub(r"[^\w\s]", "", text) # Remove special characters
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words = word_tokenize(text) # Tokenization
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words = [lemmatizer.lemmatize(w) for w in words if w not in stop_words] # Lemmatization & stopword removal
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return " ".join(words)
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# Apply preprocessing before chunking
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dataset = dataset.map(lambda row: {"cleaned_content": preprocess_text(row["content"])})
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# Step 2: Chunking Function
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def chunk_text(text, chunk_size=3):
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sentences = sent_tokenize(text) # Split text into sentences
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return [" ".join(sentences[i:i+chunk_size]) for i in range(0, len(sentences), chunk_size)]
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# Apply chunking on the cleaned text
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dataset = dataset.map(lambda row: {"chunks": chunk_text(row["cleaned_content"])})
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from sentence_transformers import SentenceTransformer
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# Load BioBERT or MiniLM for fast embedding
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embed_model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
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def generate_embedding(row):
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embedding = embed_model.encode(row["chunks"], convert_to_tensor=False).tolist()
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# Fix: Ensure embedding is a flat list, not nested
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row["embedding"] = embedding[0] if isinstance(embedding, list) and len(embedding) == 1 else embedding
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return row
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dataset = dataset.map(generate_embedding)
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import numpy as np
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# Flatten embeddings (convert [[...]] β [...])
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valid_embeddings = [
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np.array(row["embedding"]).flatten().tolist() # Ensure each embedding is 1D
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for row in dataset["train"]
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if isinstance(row["embedding"], list) and len(row["embedding"]) == 384
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]
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# Convert to NumPy array
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embeddings_np = np.array(valid_embeddings, dtype=np.float32)
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# Check shape
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print("β
Fixed Embeddings Shape:", embeddings_np.shape) # Expected: (num_samples, 384)
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import numpy as np
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# Flatten embeddings (convert [[...]] β [...])
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valid_embeddings = [
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np.array(row["embedding"]).flatten().tolist() # Ensure each embedding is 1D
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for row in dataset["train"]
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if isinstance(row["embedding"], list) and len(row["embedding"]) == 384
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]
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# Convert to NumPy array
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embeddings_np = np.array(valid_embeddings, dtype=np.float32)
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# Check shape
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print("β
Fixed Embeddings Shape:", embeddings_np.shape) # Expected: (num_samples, 384)
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import faiss
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# Check if embeddings are 2D
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if len(embeddings_np.shape) == 1:
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embeddings_np = embeddings_np.reshape(1, -1) # Ensure it's (num_samples, embedding_dim)
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# Check final shape
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print("Fixed Embeddings Shape:", embeddings_np.shape)
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# Create FAISS index
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index = faiss.IndexFlatL2(embeddings_np.shape[1])
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index.add(embeddings_np) # Add all embeddings
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print("β
Embeddings successfully stored in FAISS!")
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print("Total embeddings in FAISS:", index.ntotal)
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FAISS_INDEX_PATH = "/content/faiss_medical.index" # Save in Colab's file system
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# Save the FAISS index
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faiss.write_index(index, FAISS_INDEX_PATH)
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print(f"β
FAISS index successfully saved at: {FAISS_INDEX_PATH}")
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# Load FAISS index from file
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index = faiss.read_index(FAISS_INDEX_PATH)
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print(f"β
FAISS index loaded from: {FAISS_INDEX_PATH}")
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print(f"Total embeddings stored: {index.ntotal}")
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print("π Available columns:", dataset.column_names) # Should include "chunks"
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medical_texts = dataset["train"]["chunks"] # β
Correct way to access chunks
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# Use the same text that will be encoded
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print("π Dataset structure:", dataset)
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print("π Available columns in train:", dataset["train"].column_names)
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print("β
First 3 chunked texts:", dataset["train"]["chunks"][:3])
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import json
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id_to_text = {idx: text for idx, text in enumerate(medical_texts)}
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with open("id_to_text.json", "w") as f:
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json.dump(id_to_text, f)
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import os
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# β
Check if file exists
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if os.path.exists("id_to_text.json"):
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print("β
`id_to_text.json` exists!")
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# β
Load the JSON file
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with open("id_to_text.json", "r") as f:
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id_to_text = json.load(f)
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# β
Compare number of records
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print(f"π Records in `id_to_text.json`: {len(id_to_text)}")
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print(f"π Records in `medical_texts`: {len(medical_texts)}")
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if len(id_to_text) == len(medical_texts):
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print("β
JSON file contains the correct number of records!")
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else:
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print("β Mismatch! FAISS ID mapping and dataset size are different.")
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else:
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print("β `id_to_text.json` was not found! Make sure it was saved correctly.")
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import random
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# β
Pick 3 random FAISS IDs
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sample_ids = random.sample(list(id_to_text.keys()), 3)
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# β
Print their corresponding texts
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for faiss_id in sample_ids:
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print(f"FAISS ID {faiss_id} β Text: {id_to_text[faiss_id][:100]}...") # Show only first 100 chars
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import faiss
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import numpy as np
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from sentence_transformers import SentenceTransformer
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# β
Load FAISS
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FAISS_INDEX_PATH = "/content/faiss_medical.index"
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index = faiss.read_index(FAISS_INDEX_PATH)
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# β
Load Sentence Transformer model
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embed_model = SentenceTransformer("all-MiniLM-L6-v2")
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# β
Test a retrieval query
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query = "What are the symptoms of pneumonia?"
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query_embedding = embed_model.encode([query])
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# β
Perform FAISS search
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D, I = index.search(np.array(query_embedding).astype("float32"), 3) # Retrieve top 3 matches
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# β
Print the FAISS results & compare with JSON mapping
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print("π FAISS Search Results:", I[0])
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print("π FAISS Distances:", D[0])
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# β
Load `id_to_text.json`
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with open("id_to_text.json", "r") as f:
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id_to_text = json.load(f)
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id_to_text = {int(k): v for k, v in id_to_text.items()} # Ensure keys are integers
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# β
Print the matching texts
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for faiss_id in I[0]:
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print(f"FAISS ID {faiss_id} β Text: {id_to_text[faiss_id][:100]}...") # Show first 100 characters
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import faiss
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import numpy as np
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from sentence_transformers import SentenceTransformer
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import json
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# β
Load FAISS index
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FAISS_INDEX_PATH = "/content/faiss_medical.index"
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index = faiss.read_index(FAISS_INDEX_PATH)
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# β
Load embedding model
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embed_model = SentenceTransformer("all-MiniLM-L6-v2")
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# β
Load FAISS ID β Text Mapping
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with open("id_to_text.json", "r") as f:
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id_to_text = json.load(f)
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# β
Convert JSON keys to integers (FAISS returns int IDs)
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id_to_text = {int(k): v for k, v in id_to_text.items()}
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def retrieve_medical_summary(query, k=3):
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"""
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Retrieve the most relevant medical literature from FAISS.
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# Retrieve the closest matching text using FAISS index IDs
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retrieved_docs = [id_to_text.get(int(idx), "No relevant data found.") for idx in I[0]]
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# β
Ensure all retrieved texts are strings (Flatten lists if needed)
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retrieved_docs = [doc if isinstance(doc, str) else " ".join(doc) for doc in retrieved_docs]
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# β
Join multiple retrieved documents into one response
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return "\n\n---\n\n".join(retrieved_docs) if retrieved_docs else "No relevant data found."
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# β
Example Test
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query = "What are the symptoms of pneumonia?"
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retrieved_summary = retrieve_medical_summary(query, k=3)
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print("π Retrieved Medical Summary:\n", retrieved_summary)
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import os
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from groq import Groq
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# β
Store API Key in Environment Variable
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os.environ["GROQ_API_KEY"] = "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" # Replace with your actual key
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# β
Initialize Groq client correctly (Retrieve API key properly)
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client = Groq(api_key=os.getenv("GROQ_API_KEY"))
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+
|
281 |
def generate_medical_answer_groq(query, model="llama-3.3-70b-versatile", max_tokens=500, temperature=0.3):
|
282 |
"""
|
283 |
Generates a medical response using Groq's API with LLaMA 3.3-70B, after retrieving relevant literature from FAISS.
|
|
|
292 |
str: The AI-generated medical advice.
|
293 |
"""
|
294 |
|
295 |
+
# β
Retrieve relevant medical literature from FAISS
|
296 |
retrieved_summary = retrieve_medical_summary(query)
|
297 |
print("\nπ Retrieved Medical Text for Query:", query)
|
298 |
print(retrieved_summary, "\n")
|
|
|
301 |
return "No relevant medical data found. Please consult a healthcare professional."
|
302 |
|
303 |
try:
|
304 |
+
# β
Send request to Groq API
|
305 |
response = client.chat.completions.create(
|
306 |
model=model,
|
307 |
messages=[
|
|
|
317 |
except Exception as e:
|
318 |
return f"Error generating response: {str(e)}"
|
319 |
|
320 |
+
# β
Example Usage
|
321 |
+
query = "What are the symptoms of pneumonia?"
|
322 |
+
print("π©Ί AI-Generated Response:", generate_medical_answer_groq(query))
|
323 |
+
|
324 |
# Gradio Interface
|
325 |
def ask_medical_question(question):
|
326 |
return generate_medical_answer_groq(question)
|