File size: 11,726 Bytes
79b08ef 4899ef1 79b08ef |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 |
import streamlit as st
import json
import os
import numpy as np
import faiss
from sentence_transformers import SentenceTransformer
from PyPDF2 import PdfReader
from openai import OpenAI
import time
from PIL import Image
class IntegratedChatSystem:
def __init__(self, api_key: str):
self.api_key = api_key
self.client = OpenAI(api_key=api_key)
self.embedding_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
self.embedding_dim = 384
self.index = faiss.IndexFlatIP(self.embedding_dim)
self.metadata = []
self.fine_tuned_model = None
def add_image(self, image, context_text: str):
"""Add an image and its context to the retrieval system"""
try:
# Generate embedding for the context text
embedding = self.embedding_model.encode(context_text)
embedding = np.expand_dims(embedding, axis=0)
# Save image and add to index
if not os.path.exists('uploaded_images'):
os.makedirs('uploaded_images')
# Generate unique filename
filename = f"image_{len(self.metadata)}.jpg"
image_path = os.path.join('uploaded_images', filename)
# Save image
image.save(image_path)
# Add to FAISS index
self.index.add(embedding)
self.metadata.append({
"filepath": image_path,
"context": context_text
})
return True
except Exception as e:
st.error(f"Error adding image: {str(e)}")
return False
def search_relevant_images(self, query: str, similarity_threshold: float = 0.7, top_k: int = 3):
"""Search for relevant images based on query"""
try:
if self.index.ntotal == 0:
return []
# Generate embedding for the query
query_embedding = self.embedding_model.encode(query)
query_embedding = np.expand_dims(query_embedding, axis=0)
# Search in the index
distances, indices = self.index.search(query_embedding, min(top_k, self.index.ntotal))
# Filter results based on similarity threshold
relevant_images = [
self.metadata[i] for i, distance in zip(indices[0], distances[0])
if i != -1 and distance >= similarity_threshold
]
return relevant_images
except Exception as e:
st.error(f"Error searching images: {str(e)}")
return []
def generate_qna_pairs(self, text: str):
"""Generate question-answer pairs from text using OpenAI API"""
try:
completion = self.client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": "Generate 11 relevant question-answer pairs from the given text. Format each pair as a complete, informative question with its corresponding detailed answer."},
{"role": "user", "content": f"Text: {text}"}
],
temperature=0.7
)
response_text = completion.choices[0].message.content
qa_pairs = []
pairs = response_text.split('\n\n')
for pair in pairs:
if 'Q:' in pair and 'A:' in pair:
question = pair.split('A:')[0].replace('Q:', '').strip()
answer = pair.split('A:')[1].strip()
qa_pairs.append({
"messages": [
{"role": "system", "content": "You are an assistant chatbot. You should help the user by answering their question."},
{"role": "user", "content": question},
{"role": "assistant", "content": answer}
]
})
return qa_pairs
except Exception as e:
st.error(f"Error generating QA pairs: {str(e)}")
return []
def create_fine_tuning_job(self, training_file_id):
try:
response = self.client.fine_tuning.jobs.create(
training_file=training_file_id,
model="gpt-3.5-turbo-0125"
)
return response.id
except Exception as e:
st.error(f"Error creating fine-tuning job: {str(e)}")
return None
def monitor_fine_tuning_job(self, job_id):
try:
progress_bar = st.progress(0)
status_text = st.empty()
details_text = st.empty()
stages = {
"validating_files": "Validating training files...",
"queued": "Job queued - waiting to start...",
"running": "Training in progress...",
"succeeded": "Training completed successfully!",
"failed": "Training failed.",
"cancelled": "Training was cancelled."
}
# Approximate progress percentages for each stage
progress_mapping = {
"validating_files": 0.1,
"queued": 0.2,
"running": 0.6,
"succeeded": 1.0,
"failed": 1.0,
"cancelled": 1.0
}
last_status = None
start_time = time.time()
while True:
job_status = self.client.fine_tuning.jobs.retrieve(job_id)
current_status = job_status.status
# Update progress bar
progress_bar.progress(progress_mapping.get(current_status, 0))
# Update status message
status_message = stages.get(current_status, "Processing...")
status_text.markdown(f"**Status:** {status_message}")
# Show elapsed time and other details
elapsed_time = int(time.time() - start_time)
details_text.markdown(f"""
**Details:**
- Time elapsed: {elapsed_time // 60}m {elapsed_time % 60}s
- Job ID: {job_id}
- Current stage: {current_status}
""")
# Status changed notification
if current_status != last_status:
if current_status == "running":
st.info("🚀 Model training has begun!")
elif current_status == "succeeded":
st.success("✅ Fine-tuning completed successfully!")
self.fine_tuned_model = job_status.fine_tuned_model
st.balloons() # Celebration effect
# Display model details
st.markdown(f"""
**Training Completed!**
- Model ID: `{self.fine_tuned_model}`
- Total training time: {elapsed_time // 60}m {elapsed_time % 60}s
- Status: Ready to use
You can now use the chat interface to interact with your fine-tuned model!
""")
return True
elif current_status in ["failed", "cancelled"]:
st.error(f"❌ Training {current_status}. Please check the OpenAI dashboard for details.")
return False
last_status = current_status
time.sleep(10)
except Exception as e:
st.error(f"Error monitoring fine-tuning job: {str(e)}")
return False
# Initialize Streamlit interface
st.title("PDF Fine-tuning and Chat System with Image Retrieval")
# Initialize session state
if 'chat_system' not in st.session_state:
api_key = ""
st.session_state.chat_system = IntegratedChatSystem(api_key)
# Sidebar for image upload
with st.sidebar:
st.header("Image Upload")
uploaded_image = st.file_uploader("Upload Image", type=["jpg", "jpeg", "png"])
image_context = st.text_area("Image Context Description")
if uploaded_image and image_context and st.button("Add Image"):
image = Image.open(uploaded_image)
if st.session_state.chat_system.add_image(image, image_context):
st.success("Image added successfully!")
# Main area tabs
tab1, tab2 = st.tabs(["Fine-tuning", "Chat"])
with tab1:
st.header("Upload and Fine-tune")
uploaded_file = st.file_uploader("Upload a PDF for Fine-Tuning", type=["pdf"])
if uploaded_file is not None:
if st.button("Process and Fine-tune"):
with st.spinner("Processing PDF..."):
# Extract text from PDF
reader = PdfReader(uploaded_file)
text = "\n".join([page.extract_text() for page in reader.pages])
# Show processing steps
progress_placeholder = st.empty()
# Step 1: Generate QA pairs
progress_placeholder.text("Step 1/3: Generating QA pairs...")
qa_pairs = st.session_state.chat_system.generate_qna_pairs(text)
if qa_pairs:
# Step 2: Save and upload training file
progress_placeholder.text("Step 2/3: Preparing training file...")
jsonl_file = "questions_and_answers.jsonl"
with open(jsonl_file, 'w') as f:
for pair in qa_pairs:
json.dump(pair, f)
f.write("\n")
with open(jsonl_file, "rb") as f:
response = st.session_state.chat_system.client.files.create(
file=f,
purpose="fine-tune"
)
training_file_id = response.id
# Step 3: Start fine-tuning
progress_placeholder.text("Step 3/3: Starting fine-tuning process...")
job_id = st.session_state.chat_system.create_fine_tuning_job(training_file_id)
if job_id:
progress_placeholder.empty() # Clear the step indicator
st.info(f"🎯 Fine-tuning job initiated!")
st.session_state.chat_system.monitor_fine_tuning_job(job_id)
with tab2:
st.header("Chat Interface")
if st.session_state.chat_system.fine_tuned_model:
st.success(f"Using fine-tuned model: {st.session_state.chat_system.fine_tuned_model}")
else:
st.info("Using default model (fine-tuned model not available)")
user_message = st.text_input("Enter your message:")
if st.button("Send") and user_message:
result = st.session_state.chat_system.chat(user_message)
st.write("Response:", result["response"])
if result["relevant_images"]:
st.subheader("Relevant Images:")
for img_data in result["relevant_images"]:
if os.path.exists(img_data["filepath"]):
image = Image.open(img_data["filepath"])
st.image(image, caption=img_data["context"]) |