Update title and Top k related query

app.py

@@ -1,80 +1,79 @@
-import PyPDF2
-import numpy as np
-import faiss
-from transformers import BertTokenizer, BertForMaskedLM, BertForQuestionAnswering
-import torch
-
-tokenizer = BertTokenizer.from_pretrained('bert-large-uncased')
-qa_model = BertForQuestionAnswering.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad')
-from transformers import AutoTokenizer, AutoModelForCausalLM
-gen_model_id = "distilgpt2"
-gen_tokenizer = AutoTokenizer.from_pretrained(gen_model_id)
-gen_model = AutoModelForCausalLM.from_pretrained(gen_model_id).to("cuda" if torch.cuda.is_available() else "cpu")
-
-def read_pdf(file):
-    reader = PyPDF2.PdfReader(file)
-    text = ""
-    for page in reader.pages:
-        text += page.extract_text()
-    return text
-
-def split_text(text, chunk_size=500):
-    return [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]
-
-def encode_text(text):
-    inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True)
-    with torch.no_grad():
-        outputs = qa_model.bert(**inputs)
-    return outputs.last_hidden_state.mean(dim=1).numpy().astype(np.float32)
-
-def create_faiss_index(chunks):
-    embeddings = np.vstack([encode_text(chunk) for chunk in chunks])
-    index = faiss.IndexFlatL2(embeddings.shape[1])
-    index.add(embeddings)
-    return index, embeddings
-
-
-def search_faq(query, index, k=3):
-    query_emb = encode_text(query)
-    D, I = index.search(query_emb, k)
-    return I
-
-def generate_distilgpt2_answer(context, question):
-    prompt = f"Context: {context}\nQuestion: {question}\nAnswer:"
-    inputs = gen_tokenizer(prompt, return_tensors="pt").to(gen_model.device)
-    with torch.no_grad():
-        outputs = gen_model.generate(**inputs, max_new_tokens=64,
-                                     pad_token_id=gen_tokenizer.eos_token_id,
-                                     eos_token_id=gen_tokenizer.eos_token_id,
-                                     repetition_penalty=1.3)
-    generated = gen_tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return generated.split("Answer:")[-1].strip()
-
-def process_pdf_and_answer(pdf_file, query, top_k=1):
-    text = read_pdf(pdf_file)
-    chunks = split_text(text)
-    faiss_index, _ = create_faiss_index(chunks)
-    indices = search_faq(query, faiss_index, k=top_k)
-
-    answers = []
-    for idx in indices[0]:
-        context = chunks[idx]
-        answer = generate_distilgpt2_answer(context, query)
-        answers.append(answer)
-
-    return "\n\n---\n\n".join(answers)
-
-import gradio as gr
-interface = gr.Interface(
-    fn=process_pdf_and_answer,
-    inputs=[
-        gr.File(label="Upload PDF"),
-        gr.Textbox(label="Your Question"),
-        gr.Slider(1, 5, step=1, label="Top K Results", value=1)
-    ],
-    outputs=gr.Textbox(label="Generated Answer(s)"),
-    title="📄 PDF Quwstion Answering",
-    description="Upload a PDF and ask a question about its content. The model will try to answer based on the most relevant chunks.",
-)
-
+import PyPDF2
+import numpy as np
+import faiss
+from transformers import BertTokenizer, BertForMaskedLM, BertForQuestionAnswering
+import torch
+
+tokenizer = BertTokenizer.from_pretrained('bert-large-uncased')
+qa_model = BertForQuestionAnswering.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad')
+from transformers import AutoTokenizer, AutoModelForCausalLM
+gen_model_id = "distilgpt2"
+gen_tokenizer = AutoTokenizer.from_pretrained(gen_model_id)
+gen_model = AutoModelForCausalLM.from_pretrained(gen_model_id).to("cuda" if torch.cuda.is_available() else "cpu")
+
+def read_pdf(file):
+    reader = PyPDF2.PdfReader(file)
+    text = ""
+    for page in reader.pages:
+        text += page.extract_text()
+    return text
+
+def split_text(text, chunk_size=500):
+    return [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]
+
+def encode_text(text):
+    inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True)
+    with torch.no_grad():
+        outputs = qa_model.bert(**inputs)
+    return outputs.last_hidden_state.mean(dim=1).numpy().astype(np.float32)
+
+def create_faiss_index(chunks):
+    embeddings = np.vstack([encode_text(chunk) for chunk in chunks])
+    index = faiss.IndexFlatL2(embeddings.shape[1])
+    index.add(embeddings)
+    return index, embeddings
+
+
+def search_faq(query, index, k=3):
+    query_emb = encode_text(query)
+    D, I = index.search(query_emb, k)
+    return I
+
+def generate_distilgpt2_answer(context, question):
+    prompt = f"Context: {context}\nQuestion: {question}\nAnswer:"
+    inputs = gen_tokenizer(prompt, return_tensors="pt").to(gen_model.device)
+    with torch.no_grad():
+        outputs = gen_model.generate(**inputs, max_new_tokens=64,
+                                     pad_token_id=gen_tokenizer.eos_token_id,
+                                     eos_token_id=gen_tokenizer.eos_token_id,
+                                     repetition_penalty=1.3)
+    generated = gen_tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return generated.split("Answer:")[-1].strip()
+
+def process_pdf_and_answer(pdf_file, query, top_k=1):
+    text = read_pdf(pdf_file)
+    chunks = split_text(text)
+    faiss_index, _ = create_faiss_index(chunks)
+    indices = search_faq(query, faiss_index, k=top_k)
+
+    answers = []
+    for idx in indices[0]:
+        context = chunks[idx]
+        answer = generate_distilgpt2_answer(context, query)
+        answers.append(answer)
+
+    return "\n\n---\n\n".join(answers)
+
+import gradio as gr
+interface = gr.Interface(
+    fn=process_pdf_and_answer,
+    inputs=[
+        gr.File(label="Upload PDF"),
+        gr.Textbox(label="Your Question"),
+    ],
+    outputs=gr.Textbox(label="Generated Answer(s)"),
+    title="📄 PDF Question Answering",
+    description="Upload a PDF and ask a question about its content. The model will try to answer based on the most relevant chunks.",
+)
+
 interface.launch()