Create app.py

app.py

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+import gradio as gr
+import google.generativeai as genai
+from transformers import pipeline
+import json
+from tempfile import NamedTemporaryFile
+from ppt_parser import transfer_to_structure  # <- FIXED import
+
+# ✅ Use your Gemini API key
+GOOGLE_API_KEY = "AIzaSyA8fWpwJE21zxpuN8Fi8Qx9-iwx3d_AZiw"
+genai.configure(api_key=GOOGLE_API_KEY)
+
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+gemini_model = genai.GenerativeModel("models/gemini-1.5-flash")
+
+extracted_text = ""
+
+def extract_text_from_pptx_json(parsed_json: dict) -> str:
+    extracted_text = ""
+    for slide_key, slide in parsed_json.items():
+        for shape_key, shape in slide.items():
+            if shape.get('type') == 'group':
+                group = shape.get('group_content', {})
+                for _, group_shape in group.items():
+                    if group_shape.get('type') == 'text':
+                        for para_key, para in group_shape.items():
+                            if para_key.startswith("paragraph_"):
+                                extracted_text += para.get("text", "") + "\n"
+            elif shape.get('type') == 'text':
+                for para_key, para in shape.items():
+                    if para_key.startswith("paragraph_"):
+                        extracted_text += para.get("text", "") + "\n"
+    return extracted_text.strip()
+
+def handle_pptx_upload(pptx_file):
+    global extracted_text
+    with NamedTemporaryFile(delete=False, suffix=".pptx") as tmp:
+        tmp.write(pptx_file.read())
+        tmp_path = tmp.name
+
+    parsed_json_str, _, _ = transfer_to_structure(tmp_path, "images")
+    parsed_json = json.loads(parsed_json_str)
+    extracted_text = extract_text_from_pptx_json(parsed_json)
+    return extracted_text or "No readable text found in slides."
+
+def summarize_text():
+    global extracted_text
+    if not extracted_text:
+        return "Please upload and extract text from a PPTX file first."
+    summary = summarizer(extracted_text, max_length=200, min_length=50, do_sample=False)[0]['summary_text']
+    return summary
+
+def clarify_concept(question):
+    global extracted_text
+    if not extracted_text:
+        return "Please upload and extract text from a PPTX file first."
+    prompt = f"Context:\n{extracted_text}\n\nQuestion: {question}"
+    response = gemini_model.generate_content(prompt)
+    return response.text if response else "No response from Gemini."
+
+# ✅ Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("## 🧠 AI-Powered Study Assistant for PowerPoint Lectures")
+
+    pptx_input = gr.File(label="📂 Upload PPTX File")
+    extract_btn = gr.Button("📜 Extract & Summarize")
+
+    extracted_output = gr.Textbox(label="📄 Extracted Text", lines=10, interactive=False)
+    summary_output = gr.Textbox(label="📝 Summary", interactive=False)
+
+    extract_btn.click(handle_pptx_upload, inputs=[pptx_input], outputs=[extracted_output])
+    extract_btn.click(summarize_text, outputs=[summary_output])
+
+    question = gr.Textbox(label="❓ Ask a Question")
+    ask_btn = gr.Button("💬 Ask Gemini")
+    ai_answer = gr.Textbox(label="🤖 Gemini Answer", lines=4)
+
+    ask_btn.click(clarify_concept, inputs=[question], outputs=[ai_answer])
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860, share=True)