Update app.py

app.py

@@ -1,53 +1,27 @@
 import gradio as gr
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-from sentence_transformers import SentenceTransformer, util
-import torch
-from datasets import load_dataset
 
-# Load the model and tokenizer
 model_name = "google/flan-t5-xl"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
 
-# Load the Gita dataset
-ds = load_dataset("knowrohit07/gita_dataset")
-chapters = ds['train']['Chapter']
-sentence_ranges = ds['train']['sentence_range']
-texts = ds['train']['Text']
-
-# Load a sentence transformer model for semantic search
-sentence_model = SentenceTransformer('all-MiniLM-L6-v2')
-
-# Encode all texts for faster similarity search
-text_embeddings = sentence_model.encode(texts, convert_to_tensor=True)
-
-def find_relevant_texts(query, top_k=3):
-    query_embedding = sentence_model.encode(query, convert_to_tensor=True)
-    cos_scores = util.cos_sim(query_embedding, text_embeddings)[0]
-    top_results = torch.topk(cos_scores, k=top_k)
-    
-    relevant_texts = []
-    for score, idx in zip(top_results[0], top_results[1]):
-        relevant_texts.append(f"Chapter {chapters[idx]}, Verses {sentence_ranges[idx]}: {texts[idx]}")
-    
-    return "\n\n".join(relevant_texts)
+gita_context = """
+The Bhagavad Gita is a 700-verse Hindu scripture that is part of the Indian epic Mahabharata. It is a dialogue between Prince Arjuna and Lord Krishna, who serves as his charioteer. The Gita's core message includes:
+1. The immortality of the soul (Atman)
+2. The nature of action (Karma) and duty (Dharma)
+3. The importance of devotion (Bhakti)
+4. The pursuit of knowledge (Jnana) and wisdom
+5. Different types of Yoga: Karma Yoga, Bhakti Yoga, Jnana Yoga, and Raja Yoga
+6. The concept of detachment from the fruits of one's actions
+7. The divine nature of Krishna as an avatar of Vishnu
+Key teachings include performing one's duty without attachment to results, the importance of self-realization, and the path to liberation (Moksha).
+"""
 
 def generate_response(question):
-    relevant_texts = find_relevant_texts(question)
-    
-    prompt = f"""Based on the following excerpts from the Bhagavad Gita, answer the question.
-
-Relevant excerpts:
-{relevant_texts}
-
-Question: {question}
-
-Answer:"""
-
+    prompt = f"Based on the following context about the Bhagavad Gita, answer the question.\n\nContext: {gita_context}\n\nQuestion: {question}\n\nAnswer:"
     input_ids = tokenizer(prompt, return_tensors="pt").input_ids
     outputs = model.generate(input_ids, max_new_tokens=200, do_sample=True, temperature=0.7, top_p=0.95)
     response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    
     return response
 
 iface = gr.Interface(