Create app.py

app.py

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+import gradio as gr
+import PyPDF2
+from transformers import AutoTokenizer, AutoModel
+import torch
+import weaviate
+import cohere
+
+auth_config = weaviate.AuthApiKey(api_key="16LRz5YwOtnq8ov51Lhg1UuAollpsMgspulV")
+client = weaviate.Client(
+    url="https://wkoll9rds3orbu9fhzfr2a.c0.asia-southeast1.gcp.weaviate.cloud",
+    auth_client_secret=auth_config
+)
+cohere_client = cohere.Client("LEvCVeZkqZMW1aLYjxDqlstCzWi4Cvlt9PiysqT8")
+
+def load_pdf(file):
+    reader = PyPDF2.PdfReader(file)
+    text = ''
+    for page in range(len(reader.pages)):
+        text += reader.pages[page].extract_text()
+    return text
+
+tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
+model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
+
+def get_embeddings(text):
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+    with torch.no_grad():
+        embeddings = model(**inputs).last_hidden_state.mean(dim=1).squeeze().cpu().numpy()
+    return embeddings
+
+def upload_document_chunks(chunks):
+    for idx, chunk in enumerate(chunks):
+        embedding = get_embeddings(chunk)
+        client.data_object.create(
+            {"content": chunk},
+            "Document",
+            vector=embedding.tolist()
+        )
+
+def query_answer(query):
+    query_embedding = get_embeddings(query)
+    result = client.query.get("Document", ["content"])\
+                .with_near_vector({"vector": query_embedding.tolist()})\
+                .with_limit(3)\
+                .do()
+    return result
+
+def generate_response(context, query):
+    response = cohere_client.generate(
+        model='command',
+        prompt=f"Context: {context}\n\nQuestion: {query}?\nAnswer:",
+        max_tokens=100
+    )
+    return response.generations[0].text.strip()
+
+def qa_pipeline(pdf_file, query):
+    document_text = load_pdf(pdf_file)
+    document_chunks = [document_text[i:i+500] for i in range(0, len(document_text), 500)]
+
+    upload_document_chunks(document_chunks)
+
+    response = query_answer(query)
+    context = ' '.join([doc['content'] for doc in response['data']['Get']['Document']])
+
+    answer = generate_response(context, query)
+
+    return context, answer
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Interactive QA Bot")
+
+    pdf_input = gr.File(label="Upload a PDF file", file_types=[".pdf"])
+    query_input = gr.Textbox(label="Ask a question")
+
+    doc_segments_output = gr.Textbox(label="Retrieved Document Segments")
+    answer_output = gr.Textbox(label="Answer")
+
+    gr.Button("Submit").click(
+        qa_pipeline,
+        inputs=[pdf_input, query_input],
+        outputs=[doc_segments_output, answer_output]
+    )
+
+demo.launch()