Spaces:
Sleeping
Sleeping
Randima-Silva
commited on
Commit
•
ca53fb7
1
Parent(s):
9caaa67
initial commit
Browse files- app.py +314 -0
- requirements.txt +41 -0
- static/styles/style.css +23 -0
- templates/city-not-found.html +20 -0
- templates/index.html +19 -0
- templates/weather.html +22 -0
app.py
ADDED
@@ -0,0 +1,314 @@
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1 |
+
# from flask import Flask, render_template, request
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2 |
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# from weather import get_current_weather
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3 |
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# from waitress import serve
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4 |
+
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# from transformers import PegasusForConditionalGeneration, PegasusTokenizer
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# app = Flask(__name__)
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# @app.route('/')
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# @app.route('/index')
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# def index():
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# return render_template('index.html')
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# @app.route('/test')
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# def test():
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# tokenizer = PegasusTokenizer.from_pretrained("google/pegasus-xsum")
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# # Load model
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# model = PegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum")
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# return "Hello World!..."
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# @app.route('/weather')
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# def get_weather():
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# city = request.args.get('city')
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# print("working...")
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# # Check for empty strings or string with only spaces
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# if not bool(city.strip()):
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# # You could render "City Not Found" instead like we do below
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# city = "Kansas City"
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# weather_data = get_current_weather(city)
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# # City is not found by API
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# if not weather_data['cod'] == 200:
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# return render_template('city-not-found.html')
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# return render_template(
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# "weather.html",
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# title=weather_data["name"],
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# status=weather_data["weather"][0]["description"].capitalize(),
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# temp=f"{weather_data['main']['temp']:.1f}",
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# feels_like=f"{weather_data['main']['feels_like']:.1f}"
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# )
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# if __name__ == "__main__":
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# serve(app, host="0.0.0.0", port=8000)
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# ---------------------------------------------------------------------------------
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# from flask import Flask, render_template, request, jsonify
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# from waitress import serve
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# from transformers import PegasusForConditionalGeneration, PegasusTokenizer
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# import time
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# app = Flask(__name__)
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# # Assuming the rest of your Flask app code remains unchanged
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# @app.route('/')
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# @app.route('/index')
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# def index():
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# return render_template('index.html')
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# @app.route('/test', methods=['POST'])
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# def test():
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# # Extract text from the request body
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# content = request.json.get('content', '')
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# if not content:
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# return jsonify({"error": "No content provided"}), 400
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# start_time = time.time()
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# # Specify the directory where you have saved the model
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# model_save_directory = "./my_project_folder/pegasus_model"
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# # Load the model and tokenizer from the directory
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# model = PegasusForConditionalGeneration.from_pretrained(model_save_directory)
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# tokenizer = PegasusTokenizer.from_pretrained(model_save_directory)
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# # Create tokens - number representation of our text
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# tokens = tokenizer(content, truncation=True, padding="longest", return_tensors="pt")
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# # Summarize
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# summary = model.generate(**tokens, min_length=60, max_length=100)
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# # Decode summary
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# summarized_text = tokenizer.decode(summary[0], skip_special_tokens=True)
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# end_time = time.time()
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# execution_time = end_time - start_time
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# # Return the summarized text and execution time
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# return jsonify({
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# "summarized_text": summarized_text,
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# "execution_time": f"{execution_time} seconds"
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# })
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# # Assuming you have the `if __name__ == "__main__"` block to run the app
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# if __name__ == "__main__":
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# serve(app, host="0.0.0.0", port=8000)
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# ======================================================================================
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# from flask import Flask, request, jsonify
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# from waitress import serve
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from pymongo import MongoClient
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# from transformers import PegasusForConditionalGeneration, PegasusTokenizer
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from flask import Flask, render_template, request, jsonify
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from flask_cors import CORS
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from waitress import serve
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from transformers import PegasusForConditionalGeneration, PegasusTokenizer
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from transformers import BartForConditionalGeneration, BartTokenizer
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import torch
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import time
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import time
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from datetime import datetime, timedelta
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app = Flask(__name__)
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CORS(app)
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# Use your MongoDB Atlas connection string
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mongo_conn_str = 'mongodb+srv://final_year_project:Ngd2jIj9PpvQfb5i@cluster0.3mhko.mongodb.net/news_scraping_site?retryWrites=true&w=majority&appName=Cluster0'
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client = MongoClient(mongo_conn_str)
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# Adjust these to match your specific database and collection names
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db = client['news_scraping_site']
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summaries_collection = db.articles
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scraped_collection = db.scrapedarticles
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@app.route('/')
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def hello():
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return {"hello":"its fucking working..."}
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@app.route('/index')
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def index():
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return render_template('index.html')
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@app.route('/test', methods=['POST'])
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def test():
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content = request.json.get('content', '')
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if not content:
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return jsonify({"error": "No content provided"}), 400
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start_time = time.time()
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# model_save_directory = "./my_project_folder/pegasus_model"
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model_save_directory = "./models/pegasus_model"
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model = PegasusForConditionalGeneration.from_pretrained(model_save_directory)
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tokenizer = PegasusTokenizer.from_pretrained(model_save_directory)
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tokens = tokenizer(content, truncation=True, padding="longest", return_tensors="pt")
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summary = model.generate(**tokens, min_length=60, max_length=100)
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summarized_text = tokenizer.decode(summary[0], skip_special_tokens=True)
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171 |
+
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172 |
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# Save the summary to MongoDB Atlas
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173 |
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summary_document = {
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174 |
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"original_text": content,
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"summarized_text": summarized_text,
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176 |
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"timestamp": time.time()
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177 |
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}
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178 |
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result = summaries_collection.insert_one(summary_document)
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+
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180 |
+
end_time = time.time()
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181 |
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execution_time = end_time - start_time
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182 |
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183 |
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return jsonify({
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184 |
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"summarized_text": summarized_text,
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"execution_time": f"{execution_time} seconds",
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186 |
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"mongodb_object_id": str(result.inserted_id) # Return the MongoDB Object ID of the inserted document
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+
})
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188 |
+
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189 |
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190 |
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@app.route('/bart', methods=['POST'])
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191 |
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def bart():
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print("bart route called")
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# Get the content from the request
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content = request.json.get('content', '')
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print(content)
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# Check if content is provided
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+
if not content:
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return jsonify({"error": "No content provided"}), 400
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start_time = time.time()
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203 |
+
# Path to your BART model, adjust as necessary
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model_save_directory = "./models/bart-large-cnn"
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# Load the tokenizer and model
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tokenizer = BartTokenizer.from_pretrained(model_save_directory)
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model = BartForConditionalGeneration.from_pretrained(model_save_directory)
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# Process the content for summarization
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211 |
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inputs_no_trunc = tokenizer(content, max_length=None, return_tensors='pt', truncation=False)
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212 |
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chunk_start = 0
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chunk_end = tokenizer.model_max_length # 1024 for BART
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214 |
+
inputs_batch_lst = []
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215 |
+
while chunk_start <= len(inputs_no_trunc['input_ids'][0]):
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216 |
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inputs_batch = inputs_no_trunc['input_ids'][0][chunk_start:chunk_end]
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217 |
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inputs_batch = torch.unsqueeze(inputs_batch, 0)
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218 |
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inputs_batch_lst.append(inputs_batch)
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chunk_start += tokenizer.model_max_length
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chunk_end += tokenizer.model_max_length
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# Generate summaries for each batch of tokens
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summary_ids_lst = [model.generate(inputs, num_beams=4, max_length=100, early_stopping=True) for inputs in inputs_batch_lst]
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224 |
+
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# Combine the batched summaries
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summary_batch_lst = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for summary_id in summary_ids_lst for g in summary_id]
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summary_all = '\n'.join(summary_batch_lst)
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+
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# Calculate the execution time
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execution_time = time.time() - start_time
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231 |
+
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232 |
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summary_document = {
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"original_text": content,
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"summarized_text": summary_all,
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"timestamp": time.time()
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}
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result = summaries_collection.insert_one(summary_document)
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# Return the summarized text and execution time
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241 |
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return jsonify({
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"summarized_text": summary_all,
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243 |
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"execution_time": f"{execution_time} seconds",
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"mongodb_article_id":f"{result.inserted_id}"
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})
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+
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@app.route('/one', methods=['POST'])
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def one():
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print("bart route called")
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# Get the limit from the request
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limit = request.json.get('limit', 5)
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+
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# Calculate the time threshold (1 hour ago)
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time_threshold = datetime.now() - timedelta(hours=1)
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+
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# Query for articles
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articles = scraped_collection.find({
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"summarized": "false"
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260 |
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# "fetched_time": {"$gte": time_threshold}
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261 |
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}).limit(limit)
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262 |
+
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263 |
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# print(len(articles))
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264 |
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articles_list = list(articles)
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265 |
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print(articles_list)
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266 |
+
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267 |
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# Path to your BART model
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model_save_directory = "./models/bart-large-cnn"
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+
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270 |
+
# Load the tokenizer and model
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271 |
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tokenizer = BartTokenizer.from_pretrained(model_save_directory)
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model = BartForConditionalGeneration.from_pretrained(model_save_directory)
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+
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for article in articles:
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content = article['content']
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start_time = time.time()
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# Summarize the content
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inputs = tokenizer(content, return_tensors='pt', max_length=1024, truncation=True)
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summary_ids = model.generate(inputs['input_ids'], num_beams=4, max_length=100, early_stopping=True)
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+
summary_text = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
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execution_time = time.time() - start_time
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summary_document = {
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288 |
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# "original_text": content,
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"summary": summary_text,
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290 |
+
"summarized":"true"
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291 |
+
# "timestamp": time.time()
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292 |
+
}
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293 |
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result = summaries_collection.insert_one(summary_document)
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+
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# Save the summarized text back to the database
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297 |
+
result_scraped = scraped_collection.update_one(
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{"_id": article['_id']},
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{"$set": {"summarized":"true"}}
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300 |
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)
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301 |
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print(f"Summarized and updated article ID {article['_id']}, Execution time: {execution_time} seconds")
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+
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return jsonify({"message": "Summarization completed for requested articles"})
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+
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+
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+
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if __name__ == "__main__":
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+
app.run(host="0.0.0.0", port=7860)
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+
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+
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# if __name__ == "__main__":
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# # serve(app, host="0.0.0.0", port=9000)
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+
# app.run(host="0.0.0.0", port=9000, debug=True)
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requirements.txt
ADDED
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blinker==1.6.2
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certifi==2023.5.7
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charset-normalizer==3.2.0
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click==8.1.5
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colorama==0.4.6
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dnspython==2.6.1
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filelock==3.13.1
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Flask==2.3.2
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Flask-Cors==4.0.0
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fsspec==2024.3.0
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huggingface-hub==0.21.4
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idna==3.4
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itsdangerous==2.1.2
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Jinja2==3.1.2
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MarkupSafe==2.1.3
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mpmath==1.3.0
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networkx==3.2.1
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numpy==1.26.4
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packaging==24.0
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pillow==10.2.0
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pip==24.0
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pymongo==4.6.2
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python-dotenv==1.0.0
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PyYAML==6.0.1
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regex==2023.12.25
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requests==2.31.0
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safetensors==0.4.2
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sentencepiece==0.2.0
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sympy==1.12
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tokenizers==0.15.2
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torch==2.2.1
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torchaudio==2.2.1
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torchvision==0.17.1
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tqdm==4.66.2
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transformers==4.38.2
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typing_extensions==4.10.0
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urllib3==2.0.3
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waitress==2.1.2
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Werkzeug==2.3.6
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gunicorn
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static/styles/style.css
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* {
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margin: 0;
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padding: 0;
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box-sizing: border-box;
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}
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body {
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padding: 2rem;
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background-color: #333;
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color: whitesmoke;
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min-height: 100vh;
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display: flex;
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flex-direction: column;
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align-items: center;
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gap: 2rem;
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font-size: 2rem;
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}
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input, button {
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font-size: 2rem;
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padding: 1rem;
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border-radius: 10px;
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}
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templates/city-not-found.html
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>City Not Found</title>
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<link href="{{ url_for('static', filename='styles/style.css') }}" rel="stylesheet" />
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</head>
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<body>
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<h1>City Not Found</h1>
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<h2>Try Again?</h2>
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<form action="/weather">
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<input type="text" name="city" id="city" placeholder="Enter a City" />
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<button type="submit">Submit</button>
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</form>
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</body>
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</html>
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templates/index.html
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Get Weather Conditions</title>
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<link href="{{ url_for('static', filename='styles/style.css') }}" rel="stylesheet" />
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</head>
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<body>
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<h1>Get Weather Conditions</h1>
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<form action="/weather">
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<input type="text" name="city" id="city" placeholder="Enter a City" />
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<button type="submit">Submit</button>
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</form>
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</body>
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</html>
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templates/weather.html
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>{{ title }} Weather</title>
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<link href="{{ url_for('static', filename='styles/style.css') }}" rel="stylesheet" />
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</head>
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<body>
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<h1>{{ title }} Weather</h1>
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<p>{{ status }} and {{ temp }} °</p>
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<p>Feels like {{ feels_like }} °</p>
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<form action="/weather">
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<input type="text" name="city" id="city" placeholder="Enter a City" />
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<button type="submit">Submit</button>
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</form>
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</body>
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</html>
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