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Update app.py
Browse files
app.py
CHANGED
@@ -301,106 +301,143 @@ def save_full_transcript(query, text):
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def parse_arxiv_refs(ref_text: str):
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"""
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Parse
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AUTHORS
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SUMMARY
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Returns list of dicts with paper details, limited to 20 papers.
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Returns empty list if parsing fails.
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"""
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papers = re.split(r'\*\*.*?\|\s*.*?\|\s*.*?\*\*', ref_text)
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headers = re.findall(r'\*\*.*?\|\s*.*?\|\s*.*?\*\*', ref_text)
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results = []
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for i, (header, content) in enumerate(zip(headers, papers[1:])):
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if i >= 20: # Limit to 20 papers
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break
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try:
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#
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header_parts =
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'title': title,
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'summary': summary,
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'authors': authors,
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'year': year,
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'date': date_str
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})
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except Exception as e:
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st.warning(f"Error parsing paper
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continue
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def perform_ai_lookup(q, vocal_summary=True, extended_refs=False,
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"""Perform Arxiv search
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start = time.time()
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# Query the HF RAG pipeline
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client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
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refs = client.predict(q,20,"Semantic Search",
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# Combine for final text output
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result = f"### 🔎 {q}\n\n{r2}\n\n{refs}"
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st.markdown(result)
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# Parse
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# Display papers
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st.write("## Research Papers")
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for idx, paper in enumerate(parsed_refs):
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st.markdown(f"**{paper['date']} | {paper['title']} | ⬇️**")
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st.markdown(f"*{paper['authors']}*")
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st.markdown(paper['summary'])
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# Audio controls
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colA, colB = st.columns(2)
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with colA:
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if st.button(f"🔊 Title", key=f"title_{idx}"):
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text_tts = clean_for_speech(paper['title'])
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audio_file_title = speak_with_edge_tts(text_tts)
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play_and_download_audio(audio_file_title)
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with colB:
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if st.button(f"🔊 Full Details", key=f"summary_{idx}"):
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text_tts = clean_for_speech(f"{paper['title']} by {paper['authors']}. {paper['summary']}")
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audio_file_title_summary = speak_with_edge_tts(text_tts)
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play_and_download_audio(audio_file_title_summary)
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st.write("---")
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# Rest of your existing function...
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elapsed = time.time()-start
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st.write(f"**Total Elapsed:** {elapsed:.2f} s")
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create_file(q, result, "md")
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return result
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def parse_arxiv_refs(ref_text: str):
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"""
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Parse papers by finding lines with two pipe characters as title lines.
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Returns list of paper dictionaries with audio files.
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"""
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if not ref_text:
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return []
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results = []
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current_paper = {}
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lines = ref_text.split('\n')
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for i, line in enumerate(lines):
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# Check if this is a title line (contains exactly 2 pipe characters)
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if line.count('|') == 2:
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# If we have a previous paper, add it to results
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if current_paper:
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results.append(current_paper)
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if len(results) >= 20: # Limit to 20 papers
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break
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# Parse new paper header
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try:
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# Remove ** and split by |
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header_parts = line.strip('* ').split('|')
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date = header_parts[0].strip()
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title = header_parts[1].strip()
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# Extract arXiv URL if present
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url_match = re.search(r'(https://arxiv.org/\S+)', line)
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url = url_match.group(1) if url_match else f"paper_{len(results)}"
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current_paper = {
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'date': date,
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'title': title,
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'url': url,
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'authors': '',
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'summary': '',
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'content_start': i + 1 # Track where content begins
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}
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except Exception as e:
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st.warning(f"Error parsing paper header: {str(e)}")
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current_paper = {}
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continue
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# If we have a current paper and this isn't a title line, add to content
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elif current_paper:
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if not current_paper['authors']: # First line after title is authors
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current_paper['authors'] = line.strip('* ')
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else: # Rest is summary
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if current_paper['summary']:
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current_paper['summary'] += ' ' + line.strip()
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else:
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current_paper['summary'] = line.strip()
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# Don't forget the last paper
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if current_paper:
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results.append(current_paper)
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return results[:20] # Ensure we return maximum 20 papers
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def create_paper_audio_files(papers):
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"""
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Create audio files for each paper's components and add file paths to paper dict.
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"""
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for paper in papers:
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try:
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# Generate audio for title
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title_text = clean_for_speech(paper['title'])
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title_file = speak_with_edge_tts(title_text)
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paper['title_audio'] = title_file
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# Generate audio for full content
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full_text = f"{paper['title']} by {paper['authors']}. {paper['summary']}"
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full_text = clean_for_speech(full_text)
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full_file = speak_with_edge_tts(full_text)
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paper['full_audio'] = full_file
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except Exception as e:
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st.warning(f"Error generating audio for paper {paper['title']}: {str(e)}")
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paper['title_audio'] = None
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paper['full_audio'] = None
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def display_papers(papers):
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"""
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Display papers with their audio controls using URLs as unique keys.
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"""
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st.write("## Research Papers")
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for idx, paper in enumerate(papers):
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with st.expander(f"📄 {paper['title']}", expanded=True):
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st.markdown(f"**{paper['date']} | {paper['title']} | ⬇️**")
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st.markdown(f"*{paper['authors']}*")
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st.markdown(paper['summary'])
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# Audio controls in columns
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col1, col2 = st.columns(2)
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# Use URL as unique key for audio interface
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key_base = paper['url'].split('/')[-1] if paper['url'].startswith('http') else paper['url']
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with col1:
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if paper.get('title_audio'):
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st.write("🎙️ Title Audio")
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st.audio(paper['title_audio'], key=f"title_{key_base}")
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with col2:
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if paper.get('full_audio'):
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st.write("📚 Full Paper Audio")
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st.audio(paper['full_audio'], key=f"full_{key_base}")
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def perform_ai_lookup(q, vocal_summary=True, extended_refs=False,
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titles_summary=True, full_audio=False):
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"""Perform Arxiv search with audio generation per paper."""
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start = time.time()
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# Query the HF RAG pipeline
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client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
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refs = client.predict(q, 20, "Semantic Search",
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"mistralai/Mixtral-8x7B-Instruct-v0.1",
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api_name="/update_with_rag_md")[0]
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r2 = client.predict(q, "mistralai/Mixtral-8x7B-Instruct-v0.1",
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True, api_name="/ask_llm")
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# Combine for final text output
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result = f"### 🔎 {q}\n\n{r2}\n\n{refs}"
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st.markdown(result)
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# Parse and process papers
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papers = parse_arxiv_refs(refs)
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if papers:
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create_paper_audio_files(papers)
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display_papers(papers)
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else:
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st.warning("No papers found in the response.")
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elapsed = time.time()-start
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st.write(f"**Total Elapsed:** {elapsed:.2f} s")
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# Save full transcript
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create_file(q, result, "md")
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return result
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