Update app.py

app.py

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+# Rewriting the app into a Gradio-compatible format
+import zipfile
+import os
+
+# Directory for Gradio version
+gradio_dir = "/mnt/data/exam-ai-gradio"
+os.makedirs(gradio_dir, exist_ok=True)
+
+# Gradio-based app.py content
+gradio_app_code = '''import gradio as gr
+from transformers import pipeline
+from googlesearch import search
+import requests
+from bs4 import BeautifulSoup
+from sentence_transformers import SentenceTransformer
+import faiss
+import numpy as np
+
+# Initialize models
+summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
+embed_model = SentenceTransformer("all-MiniLM-L6-v2")
+
+def summarize_text(text, max_len=512):
+    if not text.strip():
+        return "No content to summarize."
+    try:
+        summary = summarizer(text, max_length=max_len, min_length=100, do_sample=False)
+        return summary[0]["summary_text"]
+    except Exception as e:
+        return f"Summarization error: {e}"
+
+def search_links(query, max_results=5):
+    try:
+        return list(search(query, num_results=max_results))
+    except Exception as e:
+        return []
+
+def fetch_page_content(url):
+    try:
+        res = requests.get(url, timeout=10)
+        soup = BeautifulSoup(res.text, "html.parser")
+        return soup.get_text(separator=" ", strip=True)
+    except:
+        return ""
+
+def embed_chunks(chunks):
+    return embed_model.encode(chunks)
+
+def create_faiss_index(chunks):
+    embeddings = embed_chunks(chunks)
+    index = faiss.IndexFlatL2(embeddings.shape[1])
+    index.add(np.array(embeddings))
+    return index, embeddings
+
+def generate_notes(query):
+    query_phrases = [
+        f"{query} exam syllabus 2025",
+        f"{query} exam dates",
+        f"{query} preparation resources",
+        f"{query} important topics"
+    ]
+
+    chunks = []
+    docs = []
+
+    for phrase in query_phrases:
+        urls = search_links(phrase, max_results=3)
+        for url in urls:
+            content = fetch_page_content(url)
+            if len(content.strip()) > 200:
+                docs.append(content)
+
+    for doc in docs:
+        chunks.extend([doc[i:i+300] for i in range(0, len(doc), 300)])
+
+    if not chunks:
+        return "⚠️ No content could be retrieved. Please try again with a different query."
+
+    index, _ = create_faiss_index(chunks)
+    prompt = f"important topics and notes for {query} exam"
+    query_vec = embed_chunks([prompt])[0].reshape(1, -1)
+    D, I = index.search(query_vec, k=15)
+    selected = [chunks[i] for i in I[0]]
+    unique_chunks = list(set([c.strip() for c in selected if len(c.strip()) > 200]))
+    combined = "\\n\\n".join(unique_chunks[:10])
+    notes = summarize_text(combined)
+
+    return notes
+
+iface = gr.Interface(
+    fn=generate_notes,
+    inputs=gr.Textbox(lines=1, placeholder="Enter exam name (e.g., AAI ATC)", label="Exam Name"),
+    outputs=gr.Textbox(lines=15, label="AI-Generated Important Topic Notes"),
+    title="📘 AI Exam Assistant",
+    description="Enter your exam name and get summarized notes with syllabus, dates, topics and resources."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+'''
+
+# Gradio-specific requirements
+gradio_requirements = '''gradio
+transformers
+torch
+sentence-transformers
+faiss-cpu
+googlesearch-python
+beautifulsoup4
+requests
+'''
+
+# Save files
+with open(os.path.join(gradio_dir, "app.py"), "w") as f:
+    f.write(gradio_app_code)
+
+with open(os.path.join(gradio_dir, "requirements.txt"), "w") as f:
+    f.write(gradio_requirements)
+
+# Create zip
+zip_path = "/mnt/data/exam-ai-gradio.zip"
+with zipfile.ZipFile(zip_path, "w") as zipf:
+    for root, _, files in os.walk(gradio_dir):
+        for file in files:
+            full_path = os.path.join(root, file)
+            arcname = os.path.relpath(full_path, gradio_dir)
+            zipf.write(full_path, arcname=arcname)
+
+zip_path