Fix: Convert protocol_summarizer_webapp from submodule to regular files

- Remove protocol_summarizer_webapp submodule reference
- Add all webapp files as regular files to enable proper PR creation
- Includes Streamlit app, documentation, and configuration files

community-contributions/protocol_summarizer_webapp/app.py

@@ -0,0 +1,121 @@
+import os
+from dotenv import load_dotenv
+import streamlit as st
+import requests
+from openai import OpenAI
+
+load_dotenv()
+
+st.title("Protocol Summarizer")
+
+st.markdown("""
+Search for clinical trials by keyword, select a study, and generate a protocol summary using an LLM.
+""")
+
+# Search input
+
+# Show results only after user presses Enter
+with st.form(key="search_form"):
+    query = st.text_input("Enter a disease, study title, or keyword:")
+    max_results = st.slider("Number of results", 1, 20, 5)
+    submitted = st.form_submit_button("Search")
+
+@st.cache_data(show_spinner=False)
+def search_clinical_trials(query, max_results=5):
+    if not query:
+        return []
+    url = f"https://clinicaltrials.gov/api/v2/studies?query.term={query}&pageSize={max_results}&format=json"
+    resp = requests.get(url)
+    studies = []
+    if resp.status_code == 200:
+        data = resp.json()
+        for study in data.get('studies', []):
+            nct = study.get('protocolSection', {}).get('identificationModule', {}).get('nctId', 'N/A')
+            title = study.get('protocolSection', {}).get('identificationModule', {}).get('officialTitle', 'N/A')
+            studies.append({'nct': nct, 'title': title})
+    return studies
+
+results = search_clinical_trials(query, max_results) if query else []
+
+if results:
+    st.subheader("Search Results")
+    for i, study in enumerate(results):
+        st.markdown(f"**{i+1}. {study['title']}** (NCT: {study['nct']})")
+    selected = st.number_input("Select study number to summarize", min_value=1, max_value=len(results), value=1)
+    selected_study = results[selected-1]
+    st.markdown(f"### Selected Study\n**{selected_study['title']}** (NCT: {selected_study['nct']})")
+    if st.button("Summarize Protocol"):
+        # Fetch the brief summary for the selected study
+        nct_id = selected_study['nct']
+        
+        # Use the V2 API which we know works reliably
+        url = f"https://clinicaltrials.gov/api/v2/studies/{nct_id}?format=json"
+        with st.spinner("Fetching study details..."):
+            resp = requests.get(url)
+            brief = ""
+            
+            if resp.status_code == 200:
+                try:
+                    data = resp.json()
+                    
+                    # V2 API has protocolSection at the root level
+                    if 'protocolSection' in data:
+                        desc_mod = data.get('protocolSection', {}).get('descriptionModule', {})
+                        brief = desc_mod.get('briefSummary', '')
+                        
+                        # If briefSummary is empty, try detailedDescription
+                        if not brief:
+                            brief = desc_mod.get('detailedDescription', '')
+                except Exception as e:
+                    st.error(f"Error parsing study data: {e}")
+            
+            # If API fails, try HTML scraping as a fallback
+            if not brief and resp.status_code != 200:
+                st.warning(f"API returned status code {resp.status_code}. Trying alternative method...")
+                html_url = f"https://clinicaltrials.gov/ct2/show/{nct_id}"
+                html_resp = requests.get(html_url)
+                
+                if "Brief Summary:" in html_resp.text:
+                    start = html_resp.text.find("Brief Summary:") + 15
+                    excerpt = html_resp.text[start:start+1000]
+                    
+                    # Clean up HTML
+                    import re
+                    excerpt = re.sub('<[^<]+?>', ' ', excerpt)
+                    excerpt = re.sub('\\s+', ' ', excerpt)
+                    brief = excerpt.strip()
+            
+            if not brief:
+                st.error("No brief summary or detailed description found for this study.")
+                st.stop()
+            
+        # Now we have the brief summary, send it to the LLM
+        openai = OpenAI()
+        def user_prompt_for_protocol_brief(brief_text):
+            return (
+                "Extract the following details from the clinical trial brief summary in markdown format with clear section headings (e.g., ## Study Design, ## Population, etc.):\n"
+                "- Study design\n"
+                "- Population\n"
+                "- Interventions\n"
+                "- Primary and secondary endpoints\n"
+                "- Study duration\n\n"
+                f"Brief summary text:\n{brief_text}"
+            )
+        system_prompt = "You are a clinical research assistant. Extract and list the requested protocol details in markdown format with clear section headings."
+        messages = [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_prompt_for_protocol_brief(brief)}
+        ]
+        with st.spinner("Summarizing with LLM..."):
+            try:
+                response = openai.chat.completions.create(
+                    model="gpt-4o-mini",
+                    messages=messages
+                )
+                summary = response.choices[0].message.content
+                st.markdown(summary)
+            except Exception as e:
+                st.error(f"LLM call failed: {e}")
+else:
+    if query:
+        st.info("No results found. Try a different keyword.")