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app.py

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+# -*- coding: utf-8 -*-
+"""Market trent.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/17xZqaTqSxGLAUBlBnktmkfsON24wtK5d
+"""
+
+!pip install requests #install dependency
+
+import requests
+import json
+
+!pip install streamlit
+
+import streamlit as st
+
+def search_google(query, num_results=5):
+    url = "https://google.serper.dev/search"
+    headers = {
+        "X-API-KEY": SERPER_API_KEY,
+        "Content-Type": "application/json"
+    }
+    payload = {
+        "q": query
+    }
+
+    response = requests.post(url, headers=headers, json=payload)
+    if response.status_code == 200:
+        data = response.json()
+        links = [result['link'] for result in data.get('organic', [])][:num_results]
+        return links
+    else:
+        raise Exception("Google Search failed:", response.text)
+
+def scrape_with_firecrawl(url):
+
+    endpoint = "https://api.firecrawl.dev/v1/scrape"
+    headers = {
+        "Authorization": f"Bearer {FIRECRAWL_API_KEY}",
+        "Content-Type": "application/json"
+    }
+    payload = {
+        "url": url,
+        "formats": ["markdown"],
+        "onlyMainContent": True
+    }
+
+    response = requests.post(endpoint, headers=headers, json=payload)
+    if response.status_code == 200:
+        result = response.json()
+        return result.get("data", {}).get("markdown", "No markdown content found.")
+    else:
+        raise Exception(f"Firecrawl error: {response.text}")
+
+def mistral_call(prompt):
+    url = "https://api.mistral.ai/v1/chat/completions"
+    headers = {
+        "Authorization": f"Bearer {MISTRAL_API_KEY}",
+        "Content-Type": "application/json"
+    }
+    data = {
+        "model": "mistral-medium",
+        "messages": [
+            {"role": "system", "content": " Youre an emotion classifier for fashion-related content. Classify the overall emotional tone from the reader's perspective — does the content feel exciting, dull, or negative in tone? only respond with one word: exciting, dull, or negative"},
+            {"role": "user", "content": prompt}
+        ],
+        "temperature": 0.3
+    }
+
+    response = requests.post(url, json=data, headers=headers)
+    if response.status_code == 200:
+        return response.json()["choices"][0]["message"]["content"].strip().lower()
+    else:
+        raise Exception(f"Mistral API Error: {response.status_code} | {response.text}")
+
+def filter_sentiment_mistral(text):
+    print("🧪 Running sentiment analysis...")
+    sentiment = mistral_call(text[:3000])  # truncating large input for safety
+    print(f"📊 Sentiment detected: {sentiment}")
+    return sentiment
+
+def extract_trends_with_mistral(content):
+    endpoint = "https://api.mistral.ai/v1/chat/completions"
+    headers = {
+        "Authorization": f"Bearer {MISTRAL_API_KEY}",
+        "Content-Type": "application/json"
+    }
+    prompt = f"""
+    You are a trend analyst. From the following content, identify up to 5 emerging microtrends.
+    Format your response as a JSON list with trend name and short description.
+
+    Content:
+    {content[:3000]}
+    """
+
+    payload = {
+        "model": "mistral-medium",
+        "messages": [{"role": "user", "content": prompt}],
+        "temperature": 0.7
+    }
+
+    response = requests.post(endpoint, headers=headers, json=payload)
+    if response.status_code == 200:
+        return response.json()['choices'][0]['message']['content']
+    else:
+        return "Error: Could not extract trends."
+
+EXCLUDED_DOMAINS = ["instagram.com", "facebook.com", "linkedin.com", "youtube.com"]
+
+def is_valid_url(url):
+    return not any(domain in url for domain in EXCLUDED_DOMAINS)
+
+'''def run_trend_detection(topic):
+    urls = search_google(topic)
+    for url in urls:
+        try:
+            content = scrape_with_firecrawl(url)
+            sentiment = filter_sentiment_mistral(content)
+            print(f"📊 Sentiment detected: {sentiment}")
+
+            # --- Insert the loosening filter here ---
+            if sentiment.lower() in ["exciting", "neutral"]:
+                trend_prompt = f"Extract 3 fashion trends as hashtags from this content:\n{content[:3000]}"
+                trends = mistral_call(trend_prompt)
+                print(f"🔥 Trends:\n{trends}")
+            else:
+                print(f"⚠️ Content sentiment '{sentiment}' not exciting enough for trend extraction")
+
+        except Exception as e:
+            print(f"❌ Skipping {url} due to error: {e}")
+
+st.title("👗 TrendDetective: AI-Powered Trend Spotter")
+
+topic = st.text_input("Enter a topic to analyze trends:", value="Summer fashion 2025 in Bengaluru")
+
+if st.button("Run Trend Detection"):
+    with st.spinner("🔍 Scanning the web and analyzing..."):
+        urls = search_google(topic)
+        if not urls:
+            st.error("No URLs found.")
+        else:
+            for url in urls:
+                if not is_valid_url(url):
+                    continue
+                st.markdown(f"**🔗 URL:** {url}")
+                content = scrape_with_firecrawl(url)
+                if not content:
+                    st.warning("No content extracted.")
+                    continue
+                sentiment = filter_sentiment_mistral(content)
+                st.markdown(f"**🧠 Sentiment:** `{sentiment}`")
+                if sentiment in ["exciting", "neutral"]:
+                    trends = extract_trends_with_mistral(content)
+                    st.markdown("**📈 Extracted Trends:**")
+                    st.code(trends)
+                else:
+                    st.markdown("_Skipped due to sentiment filter_")
+                st.markdown("---")
+
+streamlit run /usr/local/lib/python3.11/dist-packages/colab_kernel_launcher.py

readme.md

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+# 👗 TrendDetective – AI-Powered Microtrend Analyzer
+
+This app uses AI agents to detect emerging microtrends from web content. Built using Streamlit, Mistral AI, Firecrawl, and Serper.dev.
+
+### How it works:
+1. Search relevant articles based on your topic
+2. Scrape content from the articles
+3. Analyze sentiment
+4. Extract actionable fashion trends as hashtags
+
+### Technologies:
+- Firecrawl MCP (web scraping)
+- Mistral LLM API (sentiment + trend extraction)
+- Serper.dev (Google Search API)
+- Streamlit (UI)
+
+Built by Vaisakh, aspiring AI Product Manager.

requirement.txt

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+streamlit
+requests