Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,160 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+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"
+                )
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+# -------------------#
+# Footer
+# -------------------#
+st.markdown("<div class='footer'>© Group 6 ILP TCS Research ", unsafe_allow_html=True)