Update app.py
Browse files
app.py
CHANGED
@@ -5,6 +5,7 @@ from threading import Thread
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import PyPDF2
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import pandas as pd
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import torch
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# Set page configuration
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st.set_page_config(
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@@ -13,7 +14,6 @@ st.set_page_config(
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layout="centered"
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)
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# Correct model name
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MODEL_NAME = "amiguel/optimizedModelListing6.1"
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# Title with rocket emojis
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@@ -36,55 +36,18 @@ with st.sidebar:
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if "messages" not in st.session_state:
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st.session_state.messages = []
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# Process uploaded files
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@st.cache_data
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def process_file(uploaded_file):
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try:
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if uploaded_file.type == "application/pdf":
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pdf_reader = PyPDF2.PdfReader(uploaded_file)
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return "\n".join([page.extract_text() for page in pdf_reader.pages])
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elif uploaded_file.type == "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet":
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df = pd.read_excel(uploaded_file)
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return df.to_markdown()
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except Exception as e:
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st.error(f"π Error processing file: {str(e)}")
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return ""
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# Load model and tokenizer with authentication
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@st.cache_resource
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def load_model(hf_token):
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login(token=hf_token)
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else:
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st.error("π Authentication required!")
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return None, None
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tokenizer = AutoTokenizer.from_pretrained(
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MODEL_NAME,
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token=hf_token
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)
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model = AutoModelForCausalLM.from_pretrained(
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MODEL_NAME,
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device_map="auto",
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torch_dtype=torch.float16,
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token=hf_token
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)
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return model, tokenizer
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except Exception as e:
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st.error(f"π€ Model loading failed: {str(e)}")
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return None, None
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full_prompt = f"""Analyze this context:
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{file_context}
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Question: {prompt}
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Answer:"""
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streamer = TextIteratorStreamer(
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tokenizer,
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@@ -92,36 +55,27 @@ def generate_response(prompt, file_context):
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skip_special_tokens=True
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)
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inputs = tokenizer(
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full_prompt,
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return_tensors="pt",
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max_length=4096,
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truncation=True
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).to(model.device)
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max_new_tokens
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temperature
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top_p
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repetition_penalty
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do_sample
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use_cache
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Thread(target=model.generate, kwargs=generation_kwargs).start()
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return streamer
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# Display chat messages
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for message in st.session_state.messages:
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with st.chat_message(message["role"], avatar=avatar):
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st.markdown(message["content"])
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except:
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with st.chat_message(message["role"]):
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st.markdown(message["content"])
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# Chat input handling
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if prompt := st.chat_input("Ask your inspection question..."):
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@@ -129,7 +83,7 @@ if prompt := st.chat_input("Ask your inspection question..."):
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st.error("π Authentication required!")
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st.stop()
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# Load model
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if "model" not in st.session_state:
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st.session_state.model, st.session_state.tokenizer = load_model(hf_token)
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model = st.session_state.model
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@@ -143,23 +97,25 @@ if prompt := st.chat_input("Ask your inspection question..."):
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# Process file
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file_context = process_file(uploaded_file)
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# Generate response
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if model and tokenizer:
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try:
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with st.chat_message("assistant", avatar="π€"):
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response_container = st.empty()
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full_response = ""
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for chunk in streamer:
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# Remove <think> tags and clean text
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cleaned_chunk = chunk.replace("<think>", "").replace("</think>", "").strip()
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full_response += cleaned_chunk + " "
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# Update display with typing cursor
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response_container.markdown(full_response + "β", unsafe_allow_html=True)
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# Display
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response_container.markdown(full_response)
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st.session_state.messages.append({"role": "assistant", "content": full_response})
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import PyPDF2
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import pandas as pd
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import torch
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import time
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# Set page configuration
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st.set_page_config(
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layout="centered"
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)
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MODEL_NAME = "amiguel/optimizedModelListing6.1"
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# Title with rocket emojis
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if "messages" not in st.session_state:
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st.session_state.messages = []
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@st.cache_data
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def process_file(uploaded_file):
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# Existing file processing logic
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pass
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@st.cache_resource
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def load_model(hf_token):
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# Existing model loading logic
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pass
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def generate_with_kv_cache(prompt, file_context, use_cache=True):
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full_prompt = f"Analyze this context:\n{file_context}\n\nQuestion: {prompt}\nAnswer:"
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streamer = TextIteratorStreamer(
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tokenizer,
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skip_special_tokens=True
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)
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inputs = tokenizer(full_prompt, return_tensors="pt").to(model.device)
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# KV Caching parameters
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generation_kwargs = {
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**inputs,
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"max_new_tokens": 1024,
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"temperature": 0.7,
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"top_p": 0.9,
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"repetition_penalty": 1.1,
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"do_sample": True,
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"use_cache": use_cache, # KV Cache control
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"streamer": streamer
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}
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Thread(target=model.generate, kwargs=generation_kwargs).start()
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return streamer
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# Display chat messages
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for message in st.session_state.messages:
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# Existing message display logic
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pass
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# Chat input handling
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if prompt := st.chat_input("Ask your inspection question..."):
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st.error("π Authentication required!")
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st.stop()
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# Load model
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if "model" not in st.session_state:
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st.session_state.model, st.session_state.tokenizer = load_model(hf_token)
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model = st.session_state.model
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# Process file
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file_context = process_file(uploaded_file)
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# Generate response with KV caching
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if model and tokenizer:
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try:
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with st.chat_message("assistant", avatar="π€"):
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start_time = time.time()
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streamer = generate_with_kv_cache(prompt, file_context, use_cache=True)
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response_container = st.empty()
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full_response = ""
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for chunk in streamer:
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cleaned_chunk = chunk.replace("<think>", "").replace("</think>", "").strip()
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full_response += cleaned_chunk + " "
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response_container.markdown(full_response + "β", unsafe_allow_html=True)
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# Display metrics
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end_time = time.time()
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st.caption(f"Generated in {end_time - start_time:.2f}s using KV caching")
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response_container.markdown(full_response)
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st.session_state.messages.append({"role": "assistant", "content": full_response})
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