Create app.py

app.py

@@ -0,0 +1,51 @@
+import google.generativeai as genai
+import pdfplumber
+import gradio as gr
+import os
+from dotenv import python_dotenv
+
+python_dotenv()
+
+# Replace with your API key
+GOOGLE_API_KEY = os.getenv("GEMINI_API_KEY")
+genai.configure(api_key=GOOGLE_API_KEY)
+
+# Load Gemini Pro model
+model = genai.GenerativeModel('gemini-pro')
+
+def extract_text_from_pdf(pdf_file):
+    text = ""
+    with pdfplumber.open(pdf_file) as pdf:
+        for page in pdf.pages:
+            page_text = page.extract_text()
+            if page_text:
+                text += page_text
+    return text
+
+def summarize_pdf(pdf_file):
+    text = extract_text_from_pdf(pdf_file)
+    if not text.strip():
+        return "No extractable text found in the PDF."
+
+    # Limit text size if needed (Gemini handles long input, but it's safer)
+    text = text[:15000]
+
+    prompt = f"Summarize the following PDF content:\n\n{text}"
+
+    try:
+        response = model.generate_content(prompt)
+        return response.text.strip()
+    except Exception as e:
+        return f"Error during summarization: {e}"
+
+# Gradio interface
+iface = gr.Interface(
+    fn=summarize_pdf,
+    inputs=gr.File(label="Upload PDF", file_types=[".pdf"]),
+    outputs="text",
+    title="PDF Summarizer with Gemini",
+    description="Upload a PDF and get a summary using Google's Gemini Pro model."
+)
+
+if __name__ == "__main__":
+    iface.launch()