Create Narrator.py

Author: ANGELOANTU7

Backend/Narrator.py

@@ -0,0 +1,89 @@
+from fastapi import APIRouter
+import openai
+import requests
+from bs4 import BeautifulSoup
+import os,shutil
+from urllib.parse import urlparse, urljoin
+
+openai.api_key = "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"
+# Create an instance of APIRouter
+router = APIRouter()
+
+
+openai.api_key = "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"
+
+# Process each paragraph and search for related images
+def process_paragraphs(paragraphs):
+    # Create the "images" directory if it doesn't exist
+    if not os.path.exists("images"):
+        os.makedirs("images")
+    for query in paragraphs:
+        image_url = search_image(query)
+        if image_url:
+            # Download the image and save it locally
+            response = requests.get(image_url)
+            if response.status_code == 200:
+                with open(f"images/image_{query}.jpg", 'wb') as f:
+                    f.write(response.content)
+                    print(f"Image_{query}.jpg saved.")
+
+def search_image(query):
+    url = f"https://www.google.com/search?q={query}&tbm=isch&tbs=isz:l"
+    headers = {
+        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3"
+    }
+    response = requests.get(url, headers=headers)
+    soup = BeautifulSoup(response.text, 'html.parser')
+    image_elements = soup.find_all('img')
+    image_url = None
+    if len(image_elements) > 1:
+        image_url = image_elements[1].get('src')
+        if image_url:
+            parsed_url = urlparse(image_url)
+            if not parsed_url.scheme:
+                base_url = response.url
+                image_url = urljoin(base_url, image_url)
+    return image_url
+
+
+def extract_text_from_file(file_path):
+    try:
+        with open(file_path, 'r', encoding='utf-8') as file:
+            text = file.read()
+        return text
+    except FileNotFoundError:
+        print(f"File '{file_path}' not found.")
+        return None
+
+def extract_important_topics(questions):
+    text = questions
+    response = openai.ChatCompletion.create(
+        model="gpt-3.5-turbo",
+        messages=[
+            {
+                "role": "user",
+                "content": f"You are a teacher, who will take a Topic to a student. I will provide you with a basic summary of the student's lecture note, which may be a bit incomplete, but you have to use it and interact with the student and make sure they understand it fully. Basically, you have to narrate (everything should be text):\n\n{text}\n\n"
+            }
+        ]
+    )
+
+    important_topics = response.choices[0].message.content
+    print(important_topics)
+    return important_topics
+
+
+def extract_image_name(questions):
+    text = questions
+    response = openai.ChatCompletion.create(
+        model="gpt-3.5-turbo",
+        messages=[
+            {
+                "role": "user",
+                "content": f"create a word for each paragraph as a list\n\n{text}\n\n"
+            }
+        ]
+    )
+
+    important_topics = response.choices[0].message.content
+    print(important_topics)
+    return important_topics