changed the title

app.py

@@ -1,58 +1,58 @@
-import gradio as gr
-import os, requests, asyncio
-from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig
-from crawl4ai.content_filter_strategy import PruningContentFilter
-from crawl4ai.markdown_generation_strategy import DefaultMarkdownGenerator
-from openai import AzureOpenAI
-from dotenv import load_dotenv
-load_dotenv()
-client = AzureOpenAI(
-    api_key=os.getenv("AZURE_OPENAI_KEY").strip(),
-    api_version="2025-01-01-preview",
-    azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT").strip()
-)
-
-DEPLOYMENT_NAME = os.getenv("AZURE_OPENAI_DEPLOYMENT").strip()
-SERPER_API_KEY = os.getenv("SERPER_API_KEY").strip()
-
-def search_company_interviews(company):
-    headers = {"X-API-KEY": SERPER_API_KEY, "Content-Type": "application/json"}
-    query = f"{company} interview experience site:glassdoor.com OR site:geeksforgeeks.org OR site:prepinsta.com"
-    r = requests.post("https://google.serper.dev/search", headers=headers, json={"q": query})
-    return [res["link"] for res in r.json().get("organic", [])[:3]]
-
-async def crawl_url(url):
-    browser_conf = BrowserConfig(headless=True)
-    filter_strategy = PruningContentFilter()
-    md_gen = DefaultMarkdownGenerator(content_filter=filter_strategy)
-    run_conf = CrawlerRunConfig(markdown_generator=md_gen)
-
-    async with AsyncWebCrawler(config=browser_conf) as crawler:
-        result = await crawler.arun(url=url, config=run_conf)
-        return result.markdown.fit_markdown or result.markdown.raw_markdown
-
-async def fetch_and_summarize(company):
-    urls = search_company_interviews(company)
-    contents = []
-    for url in urls:
-        contents.append(await crawl_url(url))
-    context = "\n".join(contents)[:4000]
-    messages = [
-        {"role": "system", "content": "You summarize interview experiences for job seekers."},
-        {"role": "user", "content": f"Summarize interview process for {company} based on:\n{context}"}
-    ]
-    response = client.chat.completions.create(model=DEPLOYMENT_NAME, messages=messages, max_tokens=800)
-    return response.choices[0].message.content
-
-def get_interview_experience(company):
-    return asyncio.run(fetch_and_summarize(company))
-
-with gr.Blocks() as demo:
-    gr.Markdown("## 💼 Interview Experience Finder")
-    company = gr.Textbox(label="Company Name")
-    output = gr.Textbox(label="Interview Insights", lines=15)
-    btn = gr.Button("Fetch")
-    btn.click(get_interview_experience, inputs=[company], outputs=[output])
-
-if __name__ == "__main__":
-    demo.launch(share=False, server_name="0.0.0.0", server_port=7860, pwa=True)
+import gradio as gr
+import os, requests, asyncio
+from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig
+from crawl4ai.content_filter_strategy import PruningContentFilter
+from crawl4ai.markdown_generation_strategy import DefaultMarkdownGenerator
+from openai import AzureOpenAI
+from dotenv import load_dotenv
+load_dotenv()
+client = AzureOpenAI(
+    api_key=os.getenv("AZURE_OPENAI_KEY").strip(),
+    api_version="2025-01-01-preview",
+    azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT").strip()
+)
+
+DEPLOYMENT_NAME = os.getenv("AZURE_OPENAI_DEPLOYMENT").strip()
+SERPER_API_KEY = os.getenv("SERPER_API_KEY").strip()
+
+def search_company_interviews(company):
+    headers = {"X-API-KEY": SERPER_API_KEY, "Content-Type": "application/json"}
+    query = f"{company} interview experience site:glassdoor.com OR site:geeksforgeeks.org OR site:prepinsta.com"
+    r = requests.post("https://google.serper.dev/search", headers=headers, json={"q": query})
+    return [res["link"] for res in r.json().get("organic", [])[:3]]
+
+async def crawl_url(url):
+    browser_conf = BrowserConfig(headless=True)
+    filter_strategy = PruningContentFilter()
+    md_gen = DefaultMarkdownGenerator(content_filter=filter_strategy)
+    run_conf = CrawlerRunConfig(markdown_generator=md_gen)
+
+    async with AsyncWebCrawler(config=browser_conf) as crawler:
+        result = await crawler.arun(url=url, config=run_conf)
+        return result.markdown.fit_markdown or result.markdown.raw_markdown
+
+async def fetch_and_summarize(company):
+    urls = search_company_interviews(company)
+    contents = []
+    for url in urls:
+        contents.append(await crawl_url(url))
+    context = "\n".join(contents)[:4000]
+    messages = [
+        {"role": "system", "content": "You summarize interview experiences for job seekers."},
+        {"role": "user", "content": f"Summarize interview process for {company} based on:\n{context}"}
+    ]
+    response = client.chat.completions.create(model=DEPLOYMENT_NAME, messages=messages, max_tokens=800)
+    return response.choices[0].message.content
+
+def get_interview_experience(company):
+    return asyncio.run(fetch_and_summarize(company))
+
+with gr.Blocks() as demo:
+    gr.Markdown("## 💼 Interview Process and Expericence Finder")
+    company = gr.Textbox(label="Company Name")
+    output = gr.Textbox(label="Interview Insights", lines=15)
+    btn = gr.Button("Fetch")
+    btn.click(get_interview_experience, inputs=[company], outputs=[output])
+
+if __name__ == "__main__":
+    demo.launch(share=False, server_name="0.0.0.0", server_port=7860, pwa=True)