Update app.py

app.py

@@ -1,4 +1,101 @@
 import gradio as gr
+from llama_index.document_loaders import PDFMinerLoader
+from llama_index.text_splitter import CharacterTextSplitter
+from llama_index import LlamaIndex
+from transformers import T5ForConditionalGeneration, AutoTokenizer
+from sentence_transformers import SentenceTransformer
+import uuid
+import os
 
-iface=gr.Interface.load("models/sentence-transformers/sentence-t5-base")
-gr.launch(iface)
+model_name = 'google/flan-t5-base'
+model = T5ForConditionalGeneration.from_pretrained(model_name, device_map='auto', offload_folder="offload")
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+print('flan read')
+
+ST_name = 'sentence-transformers/sentence-t5-base'
+st_model = SentenceTransformer(ST_name)
+print('sentence read')
+
+index_path = "llama_index_test"
+llama_index = LlamaIndex(index_path)
+
+def get_context(query_text):
+    query_emb = st_model.encode(query_text)
+    query_response = llama_index.query(query_emb.tolist(), k=4)
+    context = query_response[0][0]
+    context = context.replace('\n', ' ').replace('  ', ' ')
+    return context
+
+def local_query(query, context):
+    t5query = """Using the available context, please answer the question. 
+    If you aren't sure please say i don't know.
+    Context: {}
+    Question: {}
+    """.format(context, query)
+
+    inputs = tokenizer(t5query, return_tensors="pt")
+
+    outputs = model.generate(**inputs, max_new_tokens=20)
+ 
+    return tokenizer.batch_decode(outputs, skip_special_tokens=True)
+
+def run_query(btn, history, query):
+    context = get_context(query)
+    
+    print('calling local query')
+    result = local_query(query, context)
+
+    print('printing result after call back')
+    print(result)
+
+    history.append((query, str(result[0])))
+        
+    print('printing history')
+    print(history)
+    return history, ""
+
+def upload_pdf(file):
+    try:
+        if file is not None: 
+            global llama_index
+            file_name = file.name 
+   
+            loader = PDFMinerLoader(file_name)
+            doc = loader.load()
+        
+            text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+            texts = text_splitter.split_documents(doc)
+        
+            texts = [i.page_content for i in texts]
+        
+            doc_emb = st_model.encode(texts)
+        
+            ids = [str(uuid.uuid1()) for _ in doc_emb]
+            
+            llama_index.add(doc_emb.tolist(), texts, ids)
+
+            return 'Successfully uploaded!'
+        else:
+            return "No file uploaded."
+
+    except Exception as e:
+        return f"An error occurred: {e}"
+
+with gr.Blocks() as demo:  
+    btn = gr.UploadButton("Upload a PDF", file_types=[".pdf"])
+    output = gr.Textbox(label="Output Box")
+    chatbot = gr.Chatbot(height=240)
+    
+    with gr.Row():
+        with gr.Column(scale=0.70):
+            txt = gr.Textbox(
+                show_label=False,
+                placeholder="Enter a question",
+            ) 
+
+    # Event handler for uploading a PDF
+    btn.upload(fn=upload_pdf, inputs=[btn], outputs=[output])
+    txt.submit(run_query, [btn, chatbot, txt], [chatbot, txt])
+
+gr.close_all()
+demo.queue().launch()