Create app.py

app.py

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+import gradio as gr
+import requests
+from bs4 import BeautifulSoup
+from duckduckgo_search import ddg
+from transformers import pipeline
+
+# ---- Initialize Summarization Pipeline ----
+# This uses the 'sshleifer/distilbart-cnn-12-6' model to summarize input text.
+summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
+
+# ---- Web Scrape Agent ----
+def web_scrape_agent(url):
+    try:
+        response = requests.get(url, headers={'User-Agent': 'Mozilla/5.0'})
+        soup = BeautifulSoup(response.text, "html.parser")
+        text = soup.get_text(separator="\n")
+        return text.strip() or "No content found."
+    except Exception as e:
+        return f"Error during scraping: {e}"
+
+# ---- Research Agent using DuckDuckGo Search ----
+def research_agent(query):
+    try:
+        results = ddg(query, max_results=5)
+        if results:
+            response = "\n".join([f"{result['title']}:\n{result['href']}" for result in results])
+            return response
+        else:
+            return "No results found."
+    except Exception as e:
+        return f"Error during research: {e}"
+
+# ---- Report Agent using Transformers Summarization ----
+def report_agent(data):
+    try:
+        # If the input text is too short for a meaningful summary, notify the user.
+        if len(data.split()) < 30:
+            return "Input text is too short to generate a summary. Please provide more details."
+        summary = summarizer(data, max_length=130, min_length=30, do_sample=False)
+        return summary[0]['summary_text']
+    except Exception as e:
+        return f"Error during report generation: {e}"
+
+# ---- Planning Agent ----
+def planning_agent(goal):
+    # A basic plan that breaks down any goal into five simple steps.
+    steps = [
+         f"Understand the requirements of '{goal}'.",
+         f"Break down the goal into smaller, manageable tasks.",
+         f"Prioritize the tasks and assign timelines.",
+         f"Execute the plan and monitor progress.",
+         f"Review and adjust the plan as necessary."
+    ]
+    return "\n".join([f"Step {i+1}: {step}" for i, step in enumerate(steps)])
+
+# ---- Weather Report Agent using Open-Meteo and Nominatim for Geocoding ----
+def get_coordinates(city):
+    try:
+        url = f"https://nominatim.openstreetmap.org/search?city={city}&format=json"
+        response = requests.get(url, headers={'User-Agent': 'Mozilla/5.0'})
+        data = response.json()
+        if data:
+            lat = data[0]["lat"]
+            lon = data[0]["lon"]
+            return lat, lon
+        else:
+            return None, None
+    except Exception:
+        return None, None
+
+def weather_report_agent(city):
+    lat, lon = get_coordinates(city)
+    if not lat or not lon:
+         return "City not found. Please check the city name."
+    try:
+        url = f"https://api.open-meteo.com/v1/forecast?latitude={lat}&longitude={lon}&current_weather=true"
+        response = requests.get(url)
+        data = response.json()
+        if "current_weather" in data:
+            weather_code = data["current_weather"]["weathercode"]
+            temp = data["current_weather"]["temperature"]
+            # Mapping weather codes to human-friendly descriptions.
+            weather_mapping = {
+                0: "Clear sky",
+                1: "Mainly clear",
+                2: "Partly cloudy",
+                3: "Overcast",
+                45: "Fog",
+                48: "Depositing rime fog",
+                51: "Light drizzle",
+                53: "Moderate drizzle",
+                55: "Dense drizzle",
+                56: "Light freezing drizzle",
+                57: "Dense freezing drizzle",
+                61: "Slight rain",
+                63: "Moderate rain",
+                65: "Heavy rain",
+                66: "Light freezing rain",
+                67: "Heavy freezing rain",
+                71: "Slight snowfall",
+                73: "Moderate snowfall",
+                75: "Heavy snowfall",
+                77: "Snow grains",
+                80: "Slight rain showers",
+                81: "Moderate rain showers",
+                82: "Violent rain showers",
+                85: "Slight snow showers",
+                86: "Heavy snow showers",
+                95: "Thunderstorm",
+                96: "Thunderstorm with slight hail",
+                99: "Thunderstorm with heavy hail"
+            }
+            description = weather_mapping.get(weather_code, f"Code {weather_code}")
+            return f"Current weather in {city}:\nTemperature: {temp}°C\nCondition: {description}"
+        else:
+            return "Could not fetch weather data."
+    except Exception as e:
+        return f"Error fetching weather data: {e}"
+
+# ---- Gradio Interface Definition ----
+with gr.Blocks() as demo:
+    gr.Markdown("# Multi-Agent Showcase App")
+    gr.Markdown("This app demonstrates multiple agents: Web Scraping, Research, Report Generation, Planning, and Weather Reporting. It's like the Avengers of tools—each with its own superpower!")
+    
+    with gr.Tabs():
+        # Web Scrape Tab
+        with gr.TabItem("Web Scrape"):
+            url_input = gr.Textbox(label="Enter URL", placeholder="https://example.com")
+            scrape_output = gr.Textbox(label="Scraped Content", lines=10)
+            gr.Button("Scrape").click(web_scrape_agent, inputs=url_input, outputs=scrape_output)
+            
+        # Research Tab
+        with gr.TabItem("Research"):
+            research_query = gr.Textbox(label="Enter Research Query", placeholder="Latest trends in AI")
+            research_output = gr.Textbox(label="Research Summary", lines=10)
+            gr.Button("Research").click(research_agent, inputs=research_query, outputs=research_output)
+            
+        # Report Tab
+        with gr.TabItem("Report"):
+            report_input = gr.Textbox(label="Enter Data for Report", placeholder="Paste your text here...", lines=10)
+            report_output = gr.Textbox(label="Generated Report", lines=10)
+            gr.Button("Generate Report").click(report_agent, inputs=report_input, outputs=report_output)
+            
+        # Planning Tab
+        with gr.TabItem("Planning"):
+            planning_input = gr.Textbox(label="Enter Your Goal", placeholder="E.g., Plan a trip to Paris", lines=2)
+            planning_output = gr.Textbox(label="Action Plan", lines=6)
+            gr.Button("Plan").click(planning_agent, inputs=planning_input, outputs=planning_output)
+            
+        # Weather Tab
+        with gr.TabItem("Weather"):
+            city_input = gr.Textbox(label="Enter City Name", placeholder="e.g., London", lines=1)
+            weather_output = gr.Textbox(label="Weather Report", lines=3)
+            gr.Button("Get Weather").click(weather_report_agent, inputs=city_input, outputs=weather_output)
+
+# Launch the app
+demo.launch()