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app.py

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+import streamlit as st
+import pandas as pd
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+from sentence_transformers import SentenceTransformer, util
+import torch
+
+# Load medical data
+excel_file_path = r"C:\Users\HEHEBOI\Desktop\GPT2Final\medical_data.csv"
+try:
+    medical_df = pd.read_csv(excel_file_path, encoding='utf-8')
+except UnicodeDecodeError:
+    medical_df = pd.read_csv(excel_file_path, encoding='latin1')
+
+# TF-IDF Vectorization
+vectorizer = TfidfVectorizer(stop_words='english')
+X_tfidf = vectorizer.fit_transform(medical_df['Questions'])
+
+# Load pre-trained GPT-2 model and tokenizer
+model_name = "sshleifer/tiny-gpt2"
+tokenizer = GPT2Tokenizer.from_pretrained(model_name)
+model = GPT2LMHeadModel.from_pretrained(model_name)
+
+# Load pre-trained Sentence Transformer model
+sbert_model_name = "paraphrase-MiniLM-L6-v2"
+sbert_model = SentenceTransformer(sbert_model_name)
+
+# Function to answer medical questions using a combination of TF-IDF, LLM, and semantic similarity
+def get_medical_response(question, vectorizer, X_tfidf, model, tokenizer, sbert_model, medical_df):
+    # TF-IDF Cosine Similarity
+    question_vector = vectorizer.transform([question])
+    tfidf_similarities = cosine_similarity(question_vector, X_tfidf).flatten()
+
+    # Find the most similar question using semantic similarity
+    question_embedding = sbert_model.encode(question, convert_to_tensor=True)
+    similarities = util.pytorch_cos_sim(question_embedding, sbert_model.encode(medical_df['Questions'].tolist(), convert_to_tensor=True)).flatten()
+    max_sim_index = similarities.argmax().item()
+
+    # LLM response generation
+    input_text = "Medical Bot: " + medical_df.iloc[max_sim_index]['Questions']
+    input_ids = tokenizer.encode(input_text, return_tensors="pt")
+    attention_mask = torch.ones(input_ids.shape, dtype=torch.long)
+    pad_token_id = tokenizer.eos_token_id
+    lm_output = model.generate(input_ids, max_length=150, num_return_sequences=1, no_repeat_ngram_size=2, attention_mask=attention_mask, pad_token_id=pad_token_id)
+    lm_generated_response = tokenizer.decode(lm_output[0], skip_special_tokens=True)
+
+    # Compare similarities and choose the best response
+    if tfidf_similarities.max() > 0.5:
+        tfidf_index = tfidf_similarities.argmax()
+        return medical_df.iloc[tfidf_index]['Answers']
+    else:
+        return lm_generated_response
+
+# Streamlit app
+st.title("Medical Bot")
+
+user_input = st.text_input("You:")
+if user_input.lower() == "exit":
+    st.stop()
+response = get_medical_response(user_input, vectorizer, X_tfidf, model, tokenizer, sbert_model, medical_df)
+st.text_area("Bot's Response:", response)