Upload 4 files

chatbot.py

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+import gradio as gr
+from finetuned import get_search_query
+from scraper_script import scrape_flipkart_products
+from itertools import islice
+from trends import get_trending_brands
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot().style(height=750)
+    msg = gr.Textbox(label="Prompt")
+    clear = gr.ClearButton([msg, chatbot])
+
+    def respond(message, chat_history):
+        search_query = get_search_query(message).split('\n', 1)[0].strip()
+        print(search_query)
+        user_info = {
+            'name': 'John Doe',
+            'gender': 'Male',
+            'age': 34,
+            'location': 'Hyderabad'
+        }
+
+        popular_brands = get_trending_brands()[:5]
+        # popular_brands = ['Nike', 'Jockey']
+        scraped_results = scrape_flipkart_products(search_query, user_info, popular_brands)
+
+        if(len(scraped_results) == 0):
+            bot_message = "I'm sorry, I couldn't find anything."
+        else:
+            bot_message = "## Understood! Here are my recommendations:\n\n"
+            for entry in islice(scraped_results, 3):
+                bot_message += f"![Product Image]({entry['Image']})  \n"
+                if(entry['Brand']!='N/A'):
+                     bot_message += f"**Brand:** {entry['Brand']}  \n"
+                bot_message += f"**Description:** {entry['Description']}  \n"
+                bot_message += f"**Price:** {entry['Price']}  \n"
+                bot_message += f"**Deliver By:** {entry['Delivery_Date']}  \n"
+                bot_message += f"**Link:** <a href='{entry['Link']}' target='_blank'>Link</a> \n"
+
+
+        chat_history.append((message, bot_message))
+        return "", chat_history
+
+    msg.submit(respond, [msg, chatbot], [msg, chatbot])
+
+
+demo.launch()

finetuned.py

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+import openai
+
+openai.api_key = "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"
+def get_search_query(user_message):
+    reponse = openai.Completion.create(
+        model="ada:ft-personal-2023-08-20-13-35-30",
+        prompt=user_message+" \n\n###\n\n",
+        stop=[" \n"]
+    )
+
+    return reponse["choices"][0]["text"]

scraper_script.py

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+import requests
+from bs4 import BeautifulSoup
+import pandas as pd
+
+def scrape_flipkart_products(search_query, filters, brands):
+    gender = filters.get('gender', '').lower()
+    url = "https://www.flipkart.com/search?q="
+    if search_query:
+        url += f"{search_query.replace(' ', '+')}"
+    if brands:
+        for brand in brands:
+            brand=brand.split(" ")
+            brand="%2B".join(brand)
+            url += f"&p%5B%5D=facets.brand%255B%255D%3D{brand}"
+    if gender == 'Male':
+        url += "&p[]=facets.ideal_for%255B%255D%3DMen"
+    elif gender == 'Female':
+        url += "&p[]=facets.ideal_for%255B%255D%3DWomen"
+    print(url)
+    response = requests.get(url)
+    soup = BeautifulSoup(response.content, 'html.parser')
+
+    products = []
+
+    for product in soup.find_all('div', class_='_1xHGtK'):
+        brand_element = product.find('div', class_='_2WkVRV')
+        brand = brand_element.text if brand_element else "N/A"
+
+        link_element = product.find('a', class_='_2UzuFa')
+        link = "https://www.flipkart.com" + link_element['href'] if link_element else "N/A"
+
+        image_element = product.find('img')
+        image = image_element['src'] if image_element else "N/A"
+
+        description_element = product.find('a', class_='IRpwTa')
+        description = description_element.text if description_element else "N/A"
+
+        price_element = product.find('div', class_='_30jeq3')
+        price = price_element.text.replace('₹', '').replace(',', '') if price_element else "N/A"
+
+        delivery_date_element = product.find('span', class_="_1TPvTK")
+        delivery_date = delivery_date_element.text if delivery_date_element else "N/A"
+
+        products.append({
+            'Brand': brand,
+            'Link': link,
+            'Image': image,
+            'Description': description,
+            'Price': price,
+            'Delivery_Date': delivery_date
+        })
+
+    return products

trends.py

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+from pytrends.request import TrendReq
+import pandas as pd
+
+all_keywords = [
+    "Allen Solly",
+    "Van Heusen",
+    "Peter England",
+    "Adidas",
+    "Louis Philippe",
+    "Biba",
+    "Jockey",
+    "Levi's",
+    "Nike",
+    "Puma",
+    "Reebok",
+    "United Colors of Benetton",
+    "HRX",
+    "Forever 21",
+    "Arrow",
+    "Raymond",
+    "Park Avenue",
+    "Wrangler",
+    "Pepe Jeans",
+    "Jack & Jones",
+    "Indian Terrain",
+    "Spykar",
+    "Wildcraft",
+    "Calvin Klein Jeans",
+    "Tommy Hilfiger",
+]
+
+
+def get_trending_brands():
+    pytrends = TrendReq(hl='en-US', tz=360)
+
+    chunk_size = 5
+    keyword_chunks = [all_keywords[i:i+chunk_size] for i in range(0, len(all_keywords), chunk_size)]
+    popularity_scores = []
+
+    for chunk in keyword_chunks:
+        pytrends.build_payload(chunk, timeframe='now 7-d', geo='IN')
+        data = pytrends.interest_over_time()
+        chunk_popularity = data.mean().sort_values(ascending=False)
+        popularity_scores.append(chunk_popularity)
+
+    combined_scores = pd.concat(popularity_scores, axis=1)
+
+    overall_scores = combined_scores.mean(axis=1).sort_values(ascending=False)
+    ordered_keywords = overall_scores.index.tolist()
+    return ordered_keywords