added fastAPI at chatbot.py

chatbot.py

@@ -1,55 +1,66 @@
-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
+from fastapi import FastAPI
+from pydantic import BaseModel
+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)
+
+# Initialize FastAPI app
+app = FastAPI()
+
+# Initialize the models
+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()
+
+class QueryInput(BaseModel):
+    input: str
+
+@app.post("/predict")
+def predict(query: QueryInput):
+    query_text = query.input
+    # Query Pinecone
+    xq = retriever.encode([query_text]).tolist()
+    xc = index.query(vector=xq, top_k=1, include_metadata=True)
+
+    # 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_text} 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_text} context: {context_str}"
+
+    # Generate answer using T5 model
+    output_text = context_str
+    if len(output_text.splitlines()) > 5:
+        return {"response": output_text}
+
+    if output_text.lower() == "none":
+        return {"response": "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 {"response": answer}
+
+# To run the server (use uvicorn when deploying):
+# uvicorn chatbot:app --reload