Update file_utils.py

file_utils.py

@@ -1,24 +1,107 @@
-import gradio as gr
-from file_study_guide_panel import file_study_guide_dashboard
+from sqlmodel import SQLModel, Field, create_engine, Session, select
+from datetime import datetime
+from typing import Optional
+import os
+import json
+from pptx import Presentation
+import fitz  # PyMuPDF
+from openai import OpenAI
 
-def file_based_learning_router():
-    with gr.Column(scale=4, elem_id="main-column") as router:
-        view = gr.State("study")  # default view
+# === Setup ===
+db_path = "/tmp/chatter_sessions.db"
+engine = create_engine(f"sqlite:///{db_path}")
+SQLModel.metadata.create_all(engine)
 
-        file_view_panel = gr.Column(visible=False)
-        file_study_panel = gr.Column(visible=True)
+openai_api_key = os.getenv("OPENAI_API_KEY")
+client = OpenAI(api_key=openai_api_key)
 
-        with file_study_panel:
-            file_study_guide_dashboard()
+# === DB Table for Study Guides ===
+class StudyGuideEntry(SQLModel, table=True):
+    id: Optional[int] = Field(default=None, primary_key=True)
+    user: str
+    filename: str
+    guide: str
+    timestamp: str
 
-        with file_view_panel:
-            file_viewer_dashboard()
+# === DB Utilities ===
+def save_study_guide(user, filename, guide):
+    session = Session(engine)
+    entry = StudyGuideEntry(
+        user=user,
+        filename=filename,
+        guide=guide,
+        timestamp=datetime.now().strftime("%Y-%m-%d %H:%M")
+    )
+    session.add(entry)
+    session.commit()
+    session.close()
 
-        def show_view(mode):
-            return (
-                gr.update(visible=(mode == "study")),
-                gr.update(visible=(mode == "file")),
-                mode
-            )
+def fetch_study_guides(user):
+    session = Session(engine)
+    statement = select(StudyGuideEntry).where(StudyGuideEntry.user == user)
+    results = session.exec(statement).all()
+    session.close()
+    return results
 
-        return router, show_view, file_study_panel, file_view_panel
+# === GPT Utility ===
+def call_llm(prompt, system_message="You are a helpful AI tutor."):
+    approx_tokens = len(prompt) // 4
+    if approx_tokens > 7000:
+        prompt = prompt[:28000] + "\n\n[Truncated for token limit]"
+
+    response = client.chat.completions.create(
+        model="gpt-4",
+        messages=[
+            {"role": "system", "content": system_message},
+            {"role": "user", "content": prompt}
+        ],
+        temperature=0.7
+    )
+    return response.choices[0].message.content
+
+# === File Parsing ===
+def extract_text_from_file(file):
+    ext = os.path.splitext(file.name)[1].lower()
+
+    if ext == ".pdf":
+        with fitz.open(file.name) as doc:
+            return "\n".join([page.get_text() for page in doc])
+
+    elif ext in [".txt", ".md"]:
+        return file.read().decode("utf-8")
+
+    elif ext == ".pptx":
+        prs = Presentation(file.name)
+        return "\n".join([
+            shape.text for slide in prs.slides for shape in slide.shapes if hasattr(shape, "text")
+        ])
+
+    else:
+        return "Unsupported file type."
+
+# === GPT-Based Generators ===
+
+def generate_summary(text):
+    prompt = f"Summarize the following document in 5–7 bullet points:\n\n{text}"
+    return call_llm(prompt)
+
+def generate_flashcards(text):
+    prompt = f"Generate 5 flashcards based on this document. Each flashcard should follow this format:\nQ: ...\nA: ...\n\n{text}"
+    return call_llm(prompt)
+
+def generate_quiz(text):
+    prompt = (
+        "Generate 5 multiple choice questions based on the document below. "
+        "Return the result as a JSON array where each item has:\n"
+        "- question (string)\n- options (list of strings)\n- answer (correct option string)\n\n"
+        f"{text}"
+    )
+    response = call_llm(prompt)
+    try:
+        return json.loads(response)
+    except:
+        return []
+
+def answer_question(text, question):
+    prompt = f"Using only the document below, answer this question:\n\nDocument:\n{text}\n\nQuestion:\n{question}"
+    return call_llm(prompt)