Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,3 +1,164 @@
+# # import os
+# # import json
+# # import streamlit as st
+# # import pandas as pd
+# # import plotly.express as px
+# # from together import Together
+# # from dotenv import load_dotenv
+# # import re
+
+# # # -------------------#
+# # # Secure API key load
+# # # -------------------#
+# # load_dotenv()
+# # TOGETHER_API_KEY = os.getenv("TOGETHER_API_KEY", "987adcf573b9658c775b671270aef959b3d38793771932f372f9f2a9ed5b78bf")
+# # client = Together(api_key=TOGETHER_API_KEY)
+
+# # # -------------------#
+# # # Streamlit UI setup
+# # # -------------------#
+# # st.set_page_config(page_title="FutureScope: Research Direction Explorer", layout="wide")
+
+# # st.markdown("""
+# # <style>
+# # body {
+# #     background: linear-gradient(135deg, #0f2027, #203a43, #2c5364);
+# #     color: #FFFFFF;
+# # }
+# # h1, h2, h3 {
+# #     text-align: center;
+# #     color: #FFD700;
+# #     font-family: 'Poppins', sans-serif;
+# # }
+# # .footer {
+# #     position: fixed;
+# #     left: 0;
+# #     bottom: 0;
+# #     width: 100%;
+# #     color: white;
+# #     text-align: center;
+# #     padding: 10px;
+# #     background-color: rgba(0,0,0,0.4);
+# # }
+# # .stButton > button {
+# #     background-color: #FFD700 !important;
+# #     color: black !important;
+# #     font-weight: bold;
+# #     border-radius: 10px;
+# # }
+# # </style>
+# # """, unsafe_allow_html=True)
+
+# # # -------------------#
+# # # App Title
+# # # -------------------#
+# # st.markdown("<h1>🧭 FutureScope: Research Direction Explorer</h1>", unsafe_allow_html=True)
+# # st.markdown("<p style='text-align:center;'>Discover how your research area evolved and where it's heading next 🚀</p>", unsafe_allow_html=True)
+
+# # # -------------------#
+# # # User Input
+# # # -------------------#
+# # user_topic = st.text_input("🔍 Enter your research topic", placeholder="e.g. Graph Neural Networks for Drug Discovery")
+
+# # # -------------------#
+# # # Main Logic
+# # # -------------------#
+# # if st.button("Generate Research Insights"):
+# #     if not user_topic.strip():
+# #         st.warning("⚠️ Please enter a valid research topic.")
+# #     else:
+# #         with st.spinner("Analyzing topic evolution and forecasting future directions... ⏳"):
+
+# #             # Prompt Design
+# #             prompt = f"""
+# #             You are a world-class AI research assistant specialized in analyzing research trends.
+# #             Given the topic: "{user_topic}", perform the following:
+# #             1. Summarize how this research area has evolved in the past 10–15 years.
+# #             2. Identify key milestones and subfields in a timeline format.
+# #             3. Predict 3–5 future research directions and explain why each matters.
+# #             Return the output strictly in JSON format like this:
+# #             {{
+# #               "evolution_summary": "...",
+# #               "timeline": [{{"year": ..., "trend": "..."}}, ...],
+# #               "future_directions": [{{"title": "...", "reason": "..."}}, ...]
+# #             }}
+# #             """
+
+# #             # Call Together API
+# #             response = client.chat.completions.create(
+# #                 model="meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
+# #                 messages=[{"role": "user", "content": prompt}]
+# #             )
+
+# #             raw_content = response.choices[0].message.content
+
+# #             # -------------------#
+# #             # JSON Cleaning & Parsing
+# #             # -------------------#
+# #             def extract_json(text):
+# #                 """Extract valid JSON portion from the model response."""
+# #                 text = text.strip()
+# #                 text = re.sub(r"^```json|```$", "", text).strip()  # remove code fences
+# #                 match = re.search(r'\{.*\}', text, re.DOTALL)
+# #                 if match:
+# #                     return match.group(0)
+# #                 return text
+
+# #             cleaned = extract_json(raw_content)
+# #             try:
+# #                 data = json.loads(cleaned)
+# #             except Exception as e:
+# #                 st.error(f"⚠️ Failed to parse JSON: {e}")
+# #                 st.text_area("Raw Response", raw_content, height=300)
+# #                 st.stop()
+
+# #             # -------------------#
+# #             # Display Results
+# #             # -------------------#
+# #             st.markdown("## 🧩 Evolution Summary")
+# #             st.markdown(f"<div style='background:#1e2a38;padding:15px;border-radius:10px;'>{data['evolution_summary']}</div>", unsafe_allow_html=True)
+
+# #             # Timeline Chart
+# #             if "timeline" in data and len(data["timeline"]) > 0:
+# #                 df = pd.DataFrame(data["timeline"])
+# #                 if "year" in df.columns and "trend" in df.columns:
+# #                     fig = px.scatter(df, x="year", y="trend", title="📈 Topic Evolution Over Time",
+# #                                      size=[10]*len(df), text="trend", color_discrete_sequence=["gold"])
+# #                     fig.update_traces(textposition='top center', marker=dict(symbol="circle"))
+# #                     fig.update_layout(template="plotly_dark", height=500)
+# #                     st.plotly_chart(fig, use_container_width=True)
+# #                 else:
+# #                     st.warning("Timeline data invalid — showing raw table:")
+# #                     st.dataframe(df)
+
+# #             # Future Directions
+# #             st.markdown("## 🔮 Predicted Future Directions")
+# #             for item in data.get("future_directions", []):
+# #                 st.markdown(f"""
+# #                 <div style='background:#142733;padding:15px;margin:10px;border-radius:10px;'>
+# #                 <h4>🧠 {item['title']}</h4>
+# #                 <p>{item['reason']}</p>
+# #                 </div>
+# #                 """, unsafe_allow_html=True)
+
+# #             # Tools: Copy / Download
+# #             col1, col2 = st.columns(2)
+# #             with col1:
+# #                 if st.button("📋 Copy Insights"):
+# #                     st.write("Copied to clipboard! (Use Ctrl+C manually to copy)")
+# #             with col2:
+# #                 st.download_button(
+# #                     label="💾 Download JSON",
+# #                     data=json.dumps(data, indent=2),
+# #                     file_name=f"{user_topic.replace(' ','_')}_future_directions.json",
+# #                     mime="application/json"
+# #                 )
+
+# # # -------------------#
+# # # Footer
+# # # -------------------#
+# # st.markdown("<div class='footer'>© Group 6 ILP TCS Research ", unsafe_allow_html=True)
+
 # import os
 # import json
 # import streamlit as st
@@ -19,33 +180,59 @@
 # # -------------------#
 # st.set_page_config(page_title="FutureScope: Research Direction Explorer", layout="wide")
 
-# st.markdown("""
+# # Detect Streamlit theme
+# try:
+#     theme = st.get_option("theme.base")
+# except:
+#     theme = "dark"
+
+# # Define theme colors
+# if theme == "light":
+#     BACKGROUND_GRADIENT = "linear-gradient(135deg, #f9f9f9, #eaeaea, #dddddd)"
+#     TEXT_COLOR = "#000000"
+#     TITLE_COLOR = "#DAA520"
+#     CARD_BG = "#ffffff"
+#     FOOTER_BG = "rgba(0, 0, 0, 0.1)"
+# else:
+#     BACKGROUND_GRADIENT = "linear-gradient(135deg, #0f2027, #203a43, #2c5364)"
+#     TEXT_COLOR = "#FFFFFF"
+#     TITLE_COLOR = "#FFD700"
+#     CARD_BG = "#1e2a38"
+#     FOOTER_BG = "rgba(0, 0, 0, 0.4)"
+
+# # -------------------#
+# # Dynamic CSS Styling
+# # -------------------#
+# st.markdown(f"""
 # <style>
-# body {
-#     background: linear-gradient(135deg, #0f2027, #203a43, #2c5364);
-#     color: #FFFFFF;
-# }
-# h1, h2, h3 {
-#     text-align: center;
-#     color: #FFD700;
+# body {{
+#     background: {BACKGROUND_GRADIENT};
+#     color: {TEXT_COLOR};
 #     font-family: 'Poppins', sans-serif;
-# }
-# .footer {
+# }}
+# h1, h2, h3 {{
+#     text-align: center;
+#     color: {TITLE_COLOR};
+# }}
+# .footer {{
 #     position: fixed;
 #     left: 0;
 #     bottom: 0;
 #     width: 100%;
-#     color: white;
+#     color: {TEXT_COLOR};
 #     text-align: center;
 #     padding: 10px;
-#     background-color: rgba(0,0,0,0.4);
-# }
-# .stButton > button {
-#     background-color: #FFD700 !important;
+#     background-color: {FOOTER_BG};
+# }}
+# .stButton > button {{
+#     background-color: {TITLE_COLOR} !important;
 #     color: black !important;
 #     font-weight: bold;
 #     border-radius: 10px;
-# }
+# }}
+# div[data-testid="stMarkdownContainer"] p {{
+#     color: {TEXT_COLOR};
+# }}
 # </style>
 # """, unsafe_allow_html=True)
 
@@ -96,13 +283,10 @@
 #             # JSON Cleaning & Parsing
 #             # -------------------#
 #             def extract_json(text):
-#                 """Extract valid JSON portion from the model response."""
 #                 text = text.strip()
-#                 text = re.sub(r"^```json|```$", "", text).strip()  # remove code fences
+#                 text = re.sub(r"^```json|```$", "", text).strip()
 #                 match = re.search(r'\{.*\}', text, re.DOTALL)
-#                 if match:
-#                     return match.group(0)
-#                 return text
+#                 return match.group(0) if match else text
 
 #             cleaned = extract_json(raw_content)
 #             try:
@@ -116,7 +300,7 @@
 #             # Display Results
 #             # -------------------#
 #             st.markdown("## 🧩 Evolution Summary")
-#             st.markdown(f"<div style='background:#1e2a38;padding:15px;border-radius:10px;'>{data['evolution_summary']}</div>", unsafe_allow_html=True)
+#             st.markdown(f"<div style='background:{CARD_BG};padding:15px;border-radius:10px;color:{TEXT_COLOR};'>{data['evolution_summary']}</div>", unsafe_allow_html=True)
 
 #             # Timeline Chart
 #             if "timeline" in data and len(data["timeline"]) > 0:
@@ -125,7 +309,7 @@
 #                     fig = px.scatter(df, x="year", y="trend", title="📈 Topic Evolution Over Time",
 #                                      size=[10]*len(df), text="trend", color_discrete_sequence=["gold"])
 #                     fig.update_traces(textposition='top center', marker=dict(symbol="circle"))
-#                     fig.update_layout(template="plotly_dark", height=500)
+#                     fig.update_layout(template="plotly_dark" if theme == "dark" else "plotly_white", height=500)
 #                     st.plotly_chart(fig, use_container_width=True)
 #                 else:
 #                     st.warning("Timeline data invalid — showing raw table:")
@@ -135,7 +319,7 @@
 #             st.markdown("## 🔮 Predicted Future Directions")
 #             for item in data.get("future_directions", []):
 #                 st.markdown(f"""
-#                 <div style='background:#142733;padding:15px;margin:10px;border-radius:10px;'>
+#                 <div style='background:{CARD_BG};padding:15px;margin:10px;border-radius:10px;color:{TEXT_COLOR};'>
 #                 <h4>🧠 {item['title']}</h4>
 #                 <p>{item['reason']}</p>
 #                 </div>
@@ -157,189 +341,276 @@
 # # -------------------#
 # # Footer
 # # -------------------#
-# st.markdown("<div class='footer'>© Group 6 ILP TCS Research ", unsafe_allow_html=True)
+# st.markdown(f"<div class='footer'>© Group 6 ILP TCS Research</div>", unsafe_allow_html=True)
 
-import os
-import json
-import streamlit as st
+
+
+import os, json, time, re, requests, random
 import pandas as pd
-import plotly.express as px
+import streamlit as st
 from together import Together
-from dotenv import load_dotenv
-import re
 
-# -------------------#
-# Secure API key load
-# -------------------#
-load_dotenv()
+# =========================
+# 0️⃣ Configuration & Setup
+# =========================
+st.set_page_config(page_title="📚 pResearch Retrieval", layout="wide", page_icon=":books:")
+st.title("🤖 **pResearch: Multi-Agent Research Retrieval System**")
+st.caption("Built with LLM-based reasoning, multi-agent intelligence, and human-in-loop control.")
+st.markdown("---")
+
 TOGETHER_API_KEY = os.getenv("TOGETHER_API_KEY", "987adcf573b9658c775b671270aef959b3d38793771932f372f9f2a9ed5b78bf")
+SEMANTIC_API_KEY = os.getenv("SEMANTIC_API_KEY", "b2EsaPVVN1890PXdCeum37K9zKq4AYY46n8QyLvp")
 client = Together(api_key=TOGETHER_API_KEY)
 
-# -------------------#
-# Streamlit UI setup
-# -------------------#
-st.set_page_config(page_title="FutureScope: Research Direction Explorer", layout="wide")
-
-# Detect Streamlit theme
-try:
-    theme = st.get_option("theme.base")
-except:
-    theme = "dark"
-
-# Define theme colors
-if theme == "light":
-    BACKGROUND_GRADIENT = "linear-gradient(135deg, #f9f9f9, #eaeaea, #dddddd)"
-    TEXT_COLOR = "#000000"
-    TITLE_COLOR = "#DAA520"
-    CARD_BG = "#ffffff"
-    FOOTER_BG = "rgba(0, 0, 0, 0.1)"
-else:
-    BACKGROUND_GRADIENT = "linear-gradient(135deg, #0f2027, #203a43, #2c5364)"
-    TEXT_COLOR = "#FFFFFF"
-    TITLE_COLOR = "#FFD700"
-    CARD_BG = "#1e2a38"
-    FOOTER_BG = "rgba(0, 0, 0, 0.4)"
-
-# -------------------#
-# Dynamic CSS Styling
-# -------------------#
-st.markdown(f"""
-<style>
-body {{
-    background: {BACKGROUND_GRADIENT};
-    color: {TEXT_COLOR};
-    font-family: 'Poppins', sans-serif;
-}}
-h1, h2, h3 {{
-    text-align: center;
-    color: {TITLE_COLOR};
-}}
-.footer {{
-    position: fixed;
-    left: 0;
-    bottom: 0;
-    width: 100%;
-    color: {TEXT_COLOR};
-    text-align: center;
-    padding: 10px;
-    background-color: {FOOTER_BG};
-}}
-.stButton > button {{
-    background-color: {TITLE_COLOR} !important;
-    color: black !important;
-    font-weight: bold;
-    border-radius: 10px;
-}}
-div[data-testid="stMarkdownContainer"] p {{
-    color: {TEXT_COLOR};
-}}
-</style>
-""", unsafe_allow_html=True)
-
-# -------------------#
-# App Title
-# -------------------#
-st.markdown("<h1>🧭 FutureScope: Research Direction Explorer</h1>", unsafe_allow_html=True)
-st.markdown("<p style='text-align:center;'>Discover how your research area evolved and where it's heading next 🚀</p>", unsafe_allow_html=True)
-
-# -------------------#
-# User Input
-# -------------------#
-user_topic = st.text_input("🔍 Enter your research topic", placeholder="e.g. Graph Neural Networks for Drug Discovery")
-
-# -------------------#
-# Main Logic
-# -------------------#
-if st.button("Generate Research Insights"):
-    if not user_topic.strip():
-        st.warning("⚠️ Please enter a valid research topic.")
-    else:
-        with st.spinner("Analyzing topic evolution and forecasting future directions... ⏳"):
-
-            # Prompt Design
-            prompt = f"""
-            You are a world-class AI research assistant specialized in analyzing research trends.
-            Given the topic: "{user_topic}", perform the following:
-            1. Summarize how this research area has evolved in the past 10–15 years.
-            2. Identify key milestones and subfields in a timeline format.
-            3. Predict 3–5 future research directions and explain why each matters.
-            Return the output strictly in JSON format like this:
-            {{
-              "evolution_summary": "...",
-              "timeline": [{{"year": ..., "trend": "..."}}, ...],
-              "future_directions": [{{"title": "...", "reason": "..."}}, ...]
-            }}
-            """
-
-            # Call Together API
-            response = client.chat.completions.create(
-                model="meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
-                messages=[{"role": "user", "content": prompt}]
+# =========================
+# Unified LLM Call
+# =========================
+@st.cache_data(show_spinner=False)
+def llm_call(prompt: str, temperature=0.2, max_retries=3):
+    for attempt in range(max_retries):
+        try:
+            resp = client.chat.completions.create(
+                model="meta-llama/Llama-3.3-70B-Instruct-Turbo",
+                messages=[{"role": "user", "content": prompt}],
+                temperature=temperature
             )
-
-            raw_content = response.choices[0].message.content
-
-            # -------------------#
-            # JSON Cleaning & Parsing
-            # -------------------#
-            def extract_json(text):
-                text = text.strip()
-                text = re.sub(r"^```json|```$", "", text).strip()
-                match = re.search(r'\{.*\}', text, re.DOTALL)
-                return match.group(0) if match else text
-
-            cleaned = extract_json(raw_content)
-            try:
-                data = json.loads(cleaned)
-            except Exception as e:
-                st.error(f"⚠️ Failed to parse JSON: {e}")
-                st.text_area("Raw Response", raw_content, height=300)
-                st.stop()
-
-            # -------------------#
-            # Display Results
-            # -------------------#
-            st.markdown("## 🧩 Evolution Summary")
-            st.markdown(f"<div style='background:{CARD_BG};padding:15px;border-radius:10px;color:{TEXT_COLOR};'>{data['evolution_summary']}</div>", unsafe_allow_html=True)
-
-            # Timeline Chart
-            if "timeline" in data and len(data["timeline"]) > 0:
-                df = pd.DataFrame(data["timeline"])
-                if "year" in df.columns and "trend" in df.columns:
-                    fig = px.scatter(df, x="year", y="trend", title="📈 Topic Evolution Over Time",
-                                     size=[10]*len(df), text="trend", color_discrete_sequence=["gold"])
-                    fig.update_traces(textposition='top center', marker=dict(symbol="circle"))
-                    fig.update_layout(template="plotly_dark" if theme == "dark" else "plotly_white", height=500)
-                    st.plotly_chart(fig, use_container_width=True)
-                else:
-                    st.warning("Timeline data invalid — showing raw table:")
-                    st.dataframe(df)
-
-            # Future Directions
-            st.markdown("## 🔮 Predicted Future Directions")
-            for item in data.get("future_directions", []):
-                st.markdown(f"""
-                <div style='background:{CARD_BG};padding:15px;margin:10px;border-radius:10px;color:{TEXT_COLOR};'>
-                <h4>🧠 {item['title']}</h4>
-                <p>{item['reason']}</p>
-                </div>
-                """, unsafe_allow_html=True)
-
-            # Tools: Copy / Download
-            col1, col2 = st.columns(2)
-            with col1:
-                if st.button("📋 Copy Insights"):
-                    st.write("Copied to clipboard! (Use Ctrl+C manually to copy)")
-            with col2:
-                st.download_button(
-                    label="💾 Download JSON",
-                    data=json.dumps(data, indent=2),
-                    file_name=f"{user_topic.replace(' ','_')}_future_directions.json",
-                    mime="application/json"
-                )
-
-# -------------------#
-# Footer
-# -------------------#
-st.markdown(f"<div class='footer'>© Group 6 ILP TCS Research</div>", unsafe_allow_html=True)
-
+            return resp.choices[0].message.content.strip()
+        except Exception as e:
+            time.sleep(1 + attempt)
+    return "LLM error (see logs)"
+
+
+# ============================================================
+# 1️⃣ Query Reformulator Agent
+# ============================================================
+def agent_query_reformulator(query: str):
+    prompt = f"""
+    You are an expert academic assistant.
+    Reformulate the query below into 5 semantically diverse and rich alternatives
+    that explore different perspectives (methods, datasets, applications, etc.)
+
+    Query: "{query}"
+
+    Respond in JSON format:
+    {{
+      "reformulated_queries": [
+        {{ "id": 1, "query": "..." }},
+        {{ "id": 2, "query": "..." }},
+        {{ "id": 3, "query": "..." }},
+        {{ "id": 4, "query": "..." }},
+        {{ "id": 5, "query": "..." }}
+      ]
+    }}
+    """
+    output = llm_call(prompt)
+    cleaned = re.sub(r"```json|```", "", output).strip()
+
+    try:
+        data = json.loads(cleaned)
+        queries = [q["query"] for q in data.get("reformulated_queries", []) if "query" in q]
+    except Exception:
+        queries = []
+
+    # fallback diversity
+    while len(queries) < 5:
+        alt = llm_call(f"Generate a diverse reformulation of: {query}")
+        queries.append(alt[:300])
+    queries = list(dict.fromkeys(queries))[:5]
+    return {"original_query": query, "reformulated_queries": [{"id": i+1, "query": q} for i, q in enumerate(queries)]}
+
+
+# ============================================================
+# 2️⃣ Retriever Agent (Semantic Scholar)
+# ============================================================
+def agent_retriever(query, top_k=20):
+    url = "https://api.semanticscholar.org/graph/v1/paper/search"
+    headers = {"x-api-key": SEMANTIC_API_KEY}
+    params = {
+        "query": query, "limit": top_k,
+        "fields": "paperId,title,abstract,year,authors,url,venue,citationCount"
+    }
+    resp = requests.get(url, headers=headers, params=params)
+    if resp.status_code != 200:
+        return []
+    return resp.json().get("data", [])
+
+
+# ============================================================
+# 3️⃣ Reranker Agent
+# ============================================================
+def agent_reranker(query, papers):
+    cleaned_papers = [p for p in papers if p.get("abstract")]
+    random.shuffle(cleaned_papers)
+    for batch_start in range(0, len(cleaned_papers), 5):
+        batch = cleaned_papers[batch_start:batch_start+5]
+        papers_str = "\n\n".join([
+            f"[{i+1}] Title: {p.get('title','N/A')}\nAbstract: {p.get('abstract','')[:500]}"
+            for i,p in enumerate(batch)
+        ])
+        prompt = f"""
+        You are a relevance scoring agent. 
+        Given a research query and 5 papers, assign a score (0–1) to each paper for its relevance.
+
+        Query: {query}
+        Papers:
+        {papers_str}
+
+        Respond strictly in JSON:
+        {{ "results": [{{"id": 1, "score": 0.85}}, ...] }}
+        """
+        output = llm_call(prompt)
+        cleaned = re.sub(r"```json|```", "", output).strip()
+        try:
+            results = json.loads(cleaned)["results"]
+            for i,r in enumerate(results):
+                cleaned_papers[batch_start+i]["semantic_score"] = r.get("score",0)
+        except:
+            for i in range(len(batch)):
+                cleaned_papers[batch_start+i]["semantic_score"] = 0.0
+    return sorted(cleaned_papers, key=lambda x: x.get("semantic_score",0), reverse=True)
+
+
+# ============================================================
+# 4️⃣ Weighting Agent (Meta Scorer)
+# ============================================================
+def agent_weighting(papers):
+    prompt = """
+    You are an expert in bibliometrics.
+    Assign importance weights (sum=1.0) for how papers should be ranked based on:
+    - semantic_score (LLM relevance)
+    - citationCount
+    - recency
+    - venue quality
+
+    Return JSON:
+    {"weights":{"semantic_score":0.55,"citations":0.25,"recency":0.15,"venue":0.05}}
+    """
+    output = llm_call(prompt)
+    cleaned = re.sub(r"```json|```", "", output).strip()
+    try:
+        weights = json.loads(cleaned)["weights"]
+    except:
+        weights = {"semantic_score":0.55,"citations":0.25,"recency":0.15,"venue":0.05}
+    total = sum(weights.values())
+    return {k:v/total for k,v in weights.items()}
+
+
+# ============================================================
+# 5️⃣ Meta-Scoring and Ranking
+# ============================================================
+def agent_meta_scorer(papers, weights):
+    current_year = 2025
+    prestige = {"CVPR":1.0,"ICCV":0.95,"ECCV":0.9,"NEURIPS":0.9,"ICML":0.85,"AAAI":0.8,"IJCAI":0.8,"ARXIV":0.4}
+    for p in papers:
+        sem = p.get("semantic_score",0)
+        cit = min(p.get("citationCount",0)/1000,1.0)
+        rec = max(0, 1 - (current_year - p.get("year",2000))/10)
+        venue_name = (p.get("venue") or "").upper()
+        ven = next((v for k,v in prestige.items() if k in venue_name), 0.3)
+        p["final_score"] = (
+            weights["semantic_score"]*sem +
+            weights["citations"]*cit +
+            weights["recency"]*rec +
+            weights["venue"]*ven
+        )
+    return sorted(papers, key=lambda x: x["final_score"], reverse=True)
+
+
+# ============================================================
+# 6️⃣ Critique Agent
+# ============================================================
+def agent_critique(papers, query):
+    top_titles = [p["title"] for p in papers[:5]]
+    prompt = f"""
+    As a research critic, evaluate whether these top papers are relevant to:
+    "{query}"
+
+    Papers: {json.dumps(top_titles, indent=2)}
+
+    Respond as JSON:
+    {{ "critique": "...", "relevance_score": 0–1 }}
+    """
+    output = llm_call(prompt)
+    cleaned = re.sub(r"```json|```", "", output).strip()
+    try:
+        return json.loads(cleaned)
+    except:
+        return {"critique":"Automatic check fallback.","relevance_score":0.7}
+
+
+# ============================================================
+# 7️⃣ Human-in-Loop Fallback
+# ============================================================
+def human_feedback_loop(papers):
+    st.warning("⚠️ Low relevance detected — human feedback required.")
+    for i,p in enumerate(papers[:3]):
+        st.markdown(f"**{i+1}. {p['title']}** *(Score: {p['final_score']:.3f})*")
+        st.caption(f"{p.get('abstract','')[:250]}...")
+    choice = st.radio("Approve ranking?", ["Yes","No"], index=0)
+    if choice == "No":
+        st.info("🔄 Re-ranking by citation count.")
+        papers = sorted(papers, key=lambda x: x.get("citationCount",0), reverse=True)
+    return papers
+
+
+# ============================================================
+# 8️⃣ Streamlit Master Orchestrator
+# ============================================================
+def run_pipeline(query, top_k=10):
+    st.markdown("## 🧩 Stage 1: Query Reformulation")
+    with st.spinner("Generating diverse reformulations..."):
+        q_data = agent_query_reformulator(query)
+    queries = [query] + [q["query"] for q in q_data["reformulated_queries"]]
+    for q in queries[1:]:
+        st.markdown(f"🔹 *{q}*")
+
+    st.markdown("## 🔍 Stage 2: Retrieval")
+    all_papers = []
+    progress = st.progress(0)
+    for i, q in enumerate(queries):
+        new_papers = agent_retriever(q, top_k)
+        all_papers.extend(new_papers)
+        progress.progress((i+1)/len(queries))
+        time.sleep(0.3)
+    progress.empty()
+    st.success(f"✅ Retrieved {len(all_papers)} papers in total.")
+
+    st.markdown("## 🧠 Stage 3: Semantic Reranking")
+    with st.spinner("Reranking papers semantically..."):
+        reranked = agent_reranker(query, all_papers)
+    st.info(f"Top paper after rerank: **{reranked[0]['title']}**")
+
+    st.markdown("## ⚖️ Stage 4: Weighting Agent & Meta-Scoring")
+    weights = agent_weighting(reranked)
+    st.json(weights)
+    meta_ranked = agent_meta_scorer(reranked, weights)
+
+    st.markdown("## 🔎 Stage 5: Critique Agent")
+    critique = agent_critique(meta_ranked, query)
+    st.info(f"**Critique:** {critique['critique']} | Relevance: {critique['relevance_score']:.2f}")
+
+    if critique["relevance_score"] < 0.6:
+        meta_ranked = human_feedback_loop(meta_ranked)
+
+    df = pd.DataFrame(meta_ranked)
+    st.download_button(
+        label="💾 Download Results as CSV",
+        data=df.to_csv(index=False),
+        file_name="final_ranked_papers.csv",
+        mime="text/csv"
+    )
+
+    st.dataframe(df.head(20))
+    st.success("🎯 Pipeline completed successfully!")
+
+
+# ============================================================
+# 9️⃣ Streamlit UI
+# ============================================================
+with st.form("research_form"):
+    query = st.text_input("Enter your research query:", "spatio-temporal action detection and localization")
+    top_k = st.slider("Number of papers per reformulation", 5, 50, 10)
+    run = st.form_submit_button("🚀 Run Multi-Agent Retrieval")
+
+if run:
+    run_pipeline(query, top_k)