updated app.py for graphs

app.py

@@ -1,28 +1,87 @@
 import gradio as gr
 from agent import ResearchAgent
+import pandas as pd
+import matplotlib.pyplot as plt
+import json
+import tempfile
 
 agent = ResearchAgent()
 
 def run_pipeline(file):
     try:
         if file is None:
-            return "Upload a CSV file", None, None, None, None
+            return "Upload CSV", None, None, None, None, None, None, None, None
 
-        result = agent.execute_pipeline(file.name)
+        # Save temp file
+        path = file.name
+
+        result = agent.execute_pipeline(path)
 
         if "error" in result:
-            return result["error"], None, None, None, None
+            return result["error"], None, None, None, None, None, None, None, None
+
+        # Load outputs
+        comp = pd.read_csv("comparison.csv")
+        topic = pd.read_csv("topic_review_table.csv")
+        keywords = pd.read_csv("keywords.csv")
+
+        with open("taxonomy_map.json") as f:
+            taxonomy = json.load(f)
+
+        import plotly.express as px
+
+        # -------- Graph 1: similarity distribution --------
+        fig1 = px.histogram(
+            comp,
+            x="similarity_score",
+            nbins=30,
+            title="Title vs Abstract Similarity Distribution",
+        )
+        fig1.update_layout(xaxis_title="Similarity Score", yaxis_title="Frequency")
+
+        # -------- Graph 2: topic importance --------
+        top_topics = topic.sort_values("document_count", ascending=False).head(15)
 
+        fig2 = px.bar(
+            top_topics,
+            x="topic_id",
+            y="document_count",
+            title="Top 15 Topics by Document Coverage",
+        )
+
+        # -------- Graph 3: keyword relevance --------
+        top_keywords = keywords.sort_values("relevance", ascending=False).head(15)
+
+        fig3 = px.bar(
+            top_keywords,
+            x="ID",
+            y="relevance",
+            title="Top Keyword Clusters by Relevance",
+        )
+
+        # -------- Graph 4: mapping insight --------
+        mapped = len(taxonomy["mapped"])
+        novel = len(taxonomy["novel"])
+
+        fig4 = px.pie(
+            names=["Mapped", "Novel"],
+            values=[mapped, novel],
+            title="Knowledge Mapping: Known vs Novel Themes",
+        )
         return (
             "✅ Pipeline completed",
             "comparison.csv",
             "taxonomy_map.json",
             "topic_review_table.csv",
-            "keywords.csv"
+            "keywords.csv",
+            "comp_plot.png",
+            "topic_plot.png",
+            "keywords_plot.png",
+            "taxonomy_plot.png"
         )
 
     except Exception as e:
-        return str(e), None, None, None, None
+        return str(e), None, None, None, None, None, None, None, None
 
 
 demo = gr.Interface(
@@ -30,12 +89,16 @@ demo = gr.Interface(
     inputs=gr.File(label="Upload CSV"),
     outputs=[
         gr.Textbox(label="Status"),
-        gr.File(label="Download comparison.csv"),
-        gr.File(label="Download taxonomy_map.json"),
-        gr.File(label="Download topic_review_table.csv"),
-        gr.File(label="Download keywords.csv"),
+        gr.File(label="comparison.csv"),
+        gr.File(label="taxonomy_map.json"),
+        gr.File(label="topic_review_table.csv"),
+        gr.File(label="keywords.csv"),
+        gr.Image(label="Similarity Graph"),
+        gr.Image(label="Topic Distribution"),
+        gr.Image(label="Keyword Relevance"),
+        gr.Image(label="Mapping Graph"),
     ],
-    title="Topic Modeling App"
+    title="Topic Modeling Dashboard"
 )
 
-demo.launch()
+demo.launch(share=True)