Uploaded chatbot.py

chatbot.py

@@ -0,0 +1,55 @@
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+from sentence_transformers import SentenceTransformer
+from pinecone import Pinecone
+
+device = 'cpu' 
+
+# Initialize Pinecone instance
+pc = Pinecone(api_key='89eeb534-da10-4068-92f7-12eddeabe1e5')
+
+# Check if the index exists; if not, create it
+index_name = 'abstractive-question-answering'
+index = pc.Index(index_name)
+
+def load_models():
+    print("Loading models...")
+    
+    retriever = SentenceTransformer("flax-sentence-embeddings/all_datasets_v3_mpnet-base")
+    tokenizer = T5Tokenizer.from_pretrained('t5-base')
+    generator = T5ForConditionalGeneration.from_pretrained('t5-base').to(device)
+
+    return retriever, generator, tokenizer
+
+retriever, generator, tokenizer = load_models()
+
+def process_query(query):    
+    # Query Pinecone
+    xq = retriever.encode([query]).tolist()
+    xc = index.query(vector=xq, top_k=1, include_metadata=True)
+    
+    # Print the response to check the structure
+    print("Pinecone response:", xc)
+
+    # Check if 'matches' exists and is a list
+    if 'matches' in xc and isinstance(xc['matches'], list):
+        context = [m['metadata']['Output'] for m in xc['matches']]
+        context_str = " ".join(context)
+        formatted_query = f"answer the question: {query} context: {context_str}"
+    else:
+        # Handle the case where 'matches' isn't found or isn't in the expected format
+        context_str = ""
+        formatted_query = f"answer the question: {query} context: {context_str}"
+
+    # Generate answer using T5 model
+    output_text = context_str
+    if len(output_text.splitlines()) > 5:
+        return output_text
+
+    if output_text.lower() == "none":
+        return "The topic is not covered in the student manual."
+
+    inputs = tokenizer.encode(formatted_query, return_tensors="pt", max_length=512, truncation=True).to(device)
+    ids = generator.generate(inputs, num_beams=4, min_length=10, max_length=60, repetition_penalty=1.2)
+    answer = tokenizer.decode(ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
+
+    return answer