Create app.py

app.py

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+import streamlit as st
+import requests
+from PyPDF2 import PdfReader
+from transformers import pipeline
+from huggingface_hub import HfApi
+import io
+import os
+from datetime import datetime
+
+# --- Constants ---
+COMMUNITY_BETA_MESSAGE = "This Streamlit app is part of a community in Beta. Please open discussions in the Community tab of the Community card."
+DEFAULT_BADGE_IMAGE_URL = "https://img.shields.io/badge/Hugging%20Face-Space-blue"
+COPYRIGHT_TEXT = f"© {datetime.now().year} Your Name/Organization. All rights reserved."
+
+# --- CSS ---
+st.markdown(
+    """
+    <style>
+    .reportview-container {
+        margin-top: -2em;
+    }
+    #MainMenu {visibility: hidden;}
+    footer {visibility: hidden;}
+    header {visibility: hidden;}
+    .st-emotion-cache-z53if6 {
+        padding-top: 10px;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True,
+)
+
+# --- Sidebar Settings ---
+st.sidebar.header("Settings")
+arxiv_link = st.sidebar.text_input("arXiv Paper Link or ID", placeholder="e.g., https://arxiv.org/abs/2301.00001 or 2301.00001")
+custom_space_name = st.sidebar.text_input("Custom Hugging Face Space Name (optional)")
+badge_image_url = st.sidebar.text_input("Badge Image URL", DEFAULT_BADGE_IMAGE_URL)
+copyright_text = st.sidebar.text_input("Copyright Text", COPYRIGHT_TEXT)
+
+hf_token = st.sidebar.text_input("Hugging Face Token", type="password")
+
+# --- Main App ---
+st.title("arXiv Paper to Hugging Face Space")
+st.info(COMMUNITY_BETA_MESSAGE)
+
+if hf_token:
+    try:
+        api = HfApi(token=hf_token)
+        user_info = api.whoami()
+        hf_username = user_info['fullname'] if 'fullname' in user_info else user_info['name']
+        st.sidebar.success(f"Logged in as: {hf_username}")
+    except Exception as e:
+        st.sidebar.error(f"Error with Hugging Face Token: {e}")
+
+if arxiv_link:
+    arxiv_id = None
+    if arxiv_link.startswith("https://arxiv.org/abs/"):
+        arxiv_id = arxiv_link.split("/")[-1]
+    elif arxiv_link.isdigit():
+        arxiv_id = arxiv_link
+    elif arxiv_link.startswith("arxiv:"):
+        arxiv_id = arxiv_link.split(":")[-1]
+
+    if arxiv_id:
+        pdf_url = f"https://arxiv.org/pdf/{arxiv_id}.pdf"
+        try:
+            response = requests.get(pdf_url)
+            response.raise_for_status()
+            pdf_content = response.content
+            pdf_file = io.BytesIO(pdf_content)
+            reader = PdfReader(pdf_file)
+            text = ""
+            for page in reader.pages:
+                text += page.extract_text()
+
+            st.subheader("Paper Content Preview:")
+            st.markdown(f'<iframe src="{pdf_url}" width="700" height="600" type="application/pdf"></iframe>', unsafe_allow_html=True)
+
+            if st.button("Convert to Hugging Face Space"):
+                if not hf_token:
+                    st.warning("Please enter your Hugging Face Token in the sidebar to create a Space.")
+                else:
+                    space_name_suffix = custom_space_name if custom_space_name else arxiv_id
+                    space_name = f"arxiv-{space_name_suffix}"
+
+                    try:
+                        api = HfApi(token=hf_token)
+                        repo_id = f"{hf_username}/{space_name}"
+                        api.create_repo(repo_id=repo_id, space_sdk="static")
+
+                        # Save the PDF to a temporary file
+                        with open("paper.pdf", "wb") as f:
+                            f.write(pdf_content)
+                        api.upload_file(
+                            path_or_fileobj="paper.pdf",
+                            path_in_repo="paper.pdf",
+                            repo_id=repo_id,
+                            repo_type="space",
+                        )
+                        os.remove("paper.pdf")
+
+                        # PDF Analysis
+                        try:
+                            st.info("Analyzing PDF content...")
+                            pipe = pipeline("text2text-generation", model="deepseek-ai/DeepSeek-R1-Distill-Qwen-32B")
+                            analysis_result = pipe(text[:4096], max_length=512)[0]['generated_text'] # Limit input for faster processing
+
+                            analysis_pdf_content = f"""
+                            # Analysis of arXiv Paper: {arxiv_id}
+
+                            **Generated on:** {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}
+
+                            **Analysis:**
+                            {analysis_result}
+
+                            ---
+                            {copyright_text}
+                            """
+
+                            # Save analysis to a temporary text file
+                            with open("analysis.txt", "w") as f:
+                                f.write(analysis_pdf_content)
+
+                            api.upload_file(
+                                path_or_fileobj="analysis.txt",
+                                path_in_repo="analysis.txt",
+                                repo_id=repo_id,
+                                repo_type="space",
+                            )
+                            os.remove("analysis.txt")
+
+                            st.success(f"Analysis saved to the Space as `analysis.txt`.")
+
+                        except Exception as e_analysis:
+                            st.error(f"Error during PDF analysis: {e_analysis}")
+
+                        badge_html = f"""
+                        <a href="https://huggingface.co/spaces/{repo_id}" target="_blank">
+                            <img src="{badge_image_url}" alt="Hugging Face Space">
+                        </a>
+                        """
+                        st.subheader("Hugging Face Space Created!")
+                        st.markdown(f"Space URL: https://huggingface.co/spaces/{repo_id}")
+                        st.markdown("Embed this badge in your README or website:")
+                        st.code(badge_html, language="html")
+
+                    except Exception as e_hf:
+                        st.error(f"Error creating or updating Hugging Face Space: {e_hf}")
+
+        except requests.exceptions.RequestException as e_http:
+            st.error(f"Error fetching PDF from arXiv: {e_http}")
+        except Exception as e_pdf:
+            st.error(f"Error processing PDF: {e_pdf}")
+    else:
+        st.warning("Invalid arXiv link or ID format.")
+
+st.markdown("---")
+st.markdown(copyright_text)