Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,161 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+from agents import Agent, Runner
+from composio_openai_agents import ComposioToolSet, App, Action
 import streamlit as st
+from dotenv import load_dotenv
+import asyncio
+from datetime import datetime, timedelta
+import pandas as pd
+import plotly.express as px
+from wordcloud import WordCloud
+import matplotlib.pyplot as plt
+from io import BytesIO
+import base64
+from pydantic import BaseModel
+from typing import List, Optional
+import os
+
+load_dotenv()
+
+max_date = datetime.now().strftime("%Y-%m-%d")
+min_date = (datetime.now() - timedelta(days=30)).strftime("%Y-%m-%d")   
+
+class SentimentDatum(BaseModel):
+    sentiment: str
+    count: int
+
+class IncidentDatum(BaseModel):
+    source: str
+    incidents: int
+
+class CompetitiveDatum(BaseModel):
+    company: str
+    mentions: int
+
+class Citation(BaseModel):
+    source: str
+    text: str
+    url: str
+    sentiment: str
+
+class DashboardOutput(BaseModel):
+    executive_summary: str
+    sentiment_data: List[SentimentDatum]
+    incident_data: List[IncidentDatum]
+    competitive_data: List[CompetitiveDatum]
+    wordcloud_text: str
+    citations: List[Citation]
+
+openai_key = os.environ.get("OPENAI_API_KEY")
+composio_key = os.environ.get("COMPOSIO_API_KEY")
+
+async def run_analysis(company_name):
+    toolset = ComposioToolSet(api_key=composio_key)
+    tools = toolset.get_tools(actions=[Action.TWITTER_RECENT_SEARCH, Action.REDDIT_SEARCH_ACROSS_SUBREDDITS, Action.HACKERNEWS_SEARCH_POSTS, Action.HACKERNEWS_GET_ITEM_WITH_ID])
+    agent = Agent(
+        name="Social Media Analyst",
+        instructions=f"""
+        Your ONLY job is to deliver a crisp, actionable, clustered executive summary of new, high-utility, real-time technical incidents, security chatter, actionable customer/developer feedback for the company, and competitive intelligence. 
+
+        - Do NOT cover general market or sector sentiment, financial/market positioning, or broad sentiment.
+        - Do NOT explain what you are not doing, do NOT ask for clarification, do NOT disclaim, and do NOT ask for permission or confirmation.
+        - Just deliver the report as specified, following the structure below.
+        - You must use Reddit, Twitter, and Hacker News to get the data. For each source, find and cluster at least 3 relevant, recent signals (if available) and include them in the summary and citations.
+        - The search query for twitter has to be something like this (Adjust the parameters according to the context): "{company_name} to:{max_date} from:{min_date}", don't pass any other fields. Just the query and max results.
+        - For Twitter: If no technical incidents, bugs, outages, or actionable feedback are found, instead summarize the general sentiment about {company_name} on Twitter, with clickable links to the original tweets.
+        - For hackernews, use the search query: "{company_name}", then use the get item with id tool to get the item.
+        - Don't give updates that the company already knows about, like product launches, new features, etc.
+        - For competitive intelligence, look for posts, tweets, or comments where users compare {company_name} to other competitors. Summarize the key points, advantages, disadvantages, and user sentiment in these comparisons. Include citations for each comparison.
+        - Provide more research-detailed information in both the executive summary and competitive intelligence sections. Go deeper into technical, user, and product details, and cite specific sources and findings.
+        Report structure:
+        1. # Executive Summary: A crisp, actionable, clustered summary of all new, high-utility signals. No fluff, only what matters. Include detailed research and findings.
+        2. # Competitive Intelligence: What users are saying when comparing {company_name} to competitors. Summarize key themes, pros/cons, and user sentiment. Include citations and research-level detail.
+        3. # Exact Citations: For each cluster or item, provide clickable links to the original sources (tweets, posts, etc.).
+
+        Everything should be cited and referenced. Focus only on the tech side and actionable intelligence.
+        You will be given the name of the company.
+        If tool call fails, try again with different permutations of the query.
+        """,
+        tools=tools,
+        model='gpt-4.1',
+    )
+    res = await Runner.run(starting_agent=agent, input=f"What is the summary of {company_name}?")
+    return res.final_output
+
+async def dashboard_result(result):
+    agent = Agent(
+        name="Dashboard Generator",
+        instructions=f"""
+        Your job is to generate a dashboard from the given result.
+        Competitors include direct competitors only.
+        """,
+        output_type=DashboardOutput,
+    )
+    res = await Runner.run(starting_agent=agent, input=result)
+    return res.final_output
+
+
+
+name_from_url = st.query_params.get("name", "")
+company_from_url = st.query_params.get("company", "")
+
+
+if name_from_url:
+    st.title(f"{name_from_url}'s Market Pulse Agent ")
+else:
+    st.title("Market Pulse Agent")
+
+company_name = st.text_input("Company name:", value=company_from_url)
+
+if st.button("Analyze"):
+    if company_name:
+        with st.spinner("Analyzing social media sentiment..."):
+            result = asyncio.run(run_analysis(company_name))
+            dashboard_result_response = asyncio.run(dashboard_result(str(result)))
+        st.subheader("Summary and Sentiment Analysis")
+        st.write(result)
+
+        st.markdown("---")
+        st.header("📊 Dashboard")
+        # Metrics Row
+        metric1, metric2, metric3 = st.columns(3)
+        with metric1:
+            st.metric("Total Mentions", sum([s.count for s in dashboard_result_response.sentiment_data]))
+        with metric2:
+            st.metric("Incidents", sum([i.incidents for i in dashboard_result_response.incident_data]))
+        with metric3:
+            st.metric("Competitors Compared", len(dashboard_result_response.competitive_data)-1)
+
+        # Main Grid
+        grid1, grid2 = st.columns([2, 1])
+        with grid1:
+            st.subheader(":green[Sentiment Breakdown]")
+            df_sentiment = pd.DataFrame([s.model_dump() for s in dashboard_result_response.sentiment_data])
+            fig = px.pie(df_sentiment, names='sentiment', values='count', color='sentiment',
+                         color_discrete_map={"Positive": "#22c55e", "Negative": "#ef4444", "Neutral": "#a3a3a3"})
+            fig.update_traces(textinfo='percent+label', pull=[0.05, 0.05, 0.05])
+            st.plotly_chart(fig, use_container_width=True)
+        # Competitive Intelligence full width
+        st.subheader(":blue[Competitive Intelligence]")
+        df_comp = pd.DataFrame([c.model_dump() for c in dashboard_result_response.competitive_data])
+        fig3 = px.bar(df_comp, x='company', y='mentions', color='company', text='mentions',
+                     color_discrete_sequence=px.colors.qualitative.Pastel)
+        fig3.update_layout(yaxis_title=None, xaxis_title=None, plot_bgcolor='#f8fafc')
+        st.plotly_chart(fig3, use_container_width=True)
+        with grid2:
+            if sum([i.incidents for i in dashboard_result_response.incident_data]) > 0:
+                st.subheader(":orange[Incident Frequency]")
+                df_incident = pd.DataFrame([i.model_dump() for i in dashboard_result_response.incident_data])
+                fig2 = px.bar(df_incident, x='source', y='incidents', color='source', text='incidents',
+                             color_discrete_sequence=px.colors.qualitative.Set2)
+                fig2.update_layout(yaxis_title=None, xaxis_title=None, plot_bgcolor='#f8fafc')
+                st.plotly_chart(fig2, use_container_width=True)
 
-"""
-# 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])),
-    ))
+        st.markdown("<hr style='margin:2rem 0;' />", unsafe_allow_html=True)
+        st.subheader(":gray[Top Citations]")
+        df_cite = pd.DataFrame([c.model_dump() for c in dashboard_result_response.citations])
+        def make_clickable(url):
+            return f'<a href="{url}" target="_blank" style="color:#2563eb; text-decoration:underline;">link</a>'
+        df_cite['url'] = df_cite['url'].apply(make_clickable)
+        st.write(df_cite.to_html(escape=False, index=False, classes='tw-table tw-table-auto tw-bg-white tw-shadow-md tw-rounded-lg'), unsafe_allow_html=True)
+    else:
+        st.warning("Please enter a company name.")