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| # app.py | |
| import gradio as gr | |
| import pandas as pd | |
| import requests | |
| from datasets import load_dataset | |
| from transformers import pipeline | |
| from PIL import Image | |
| import io | |
| # --------------------------- | |
| # 1️⃣ Load Cloud / Colab Dataset (Dynamic) | |
| # --------------------------- | |
| # Example Hugging Face text dataset (can extend to Colab CSV datasets) | |
| dataset = load_dataset("ag_news", split="train[:500]") # small sample for free-tier | |
| df_text = pd.DataFrame(dataset) | |
| # CSV-based crop yield dataset (example dynamic placeholder) | |
| try: | |
| csv_url = "https://raw.githubusercontent.com/username/fake-agri-yield/main/yield_data.csv" | |
| df_yield = pd.read_csv(csv_url) | |
| except: | |
| df_yield = pd.DataFrame({"crop": ["Wheat", "Rice"], "avg_yield": [2.5, 3.0]}) | |
| # Display first few rows | |
| print(df_text.head()) | |
| print(df_yield.head()) | |
| # --------------------------- | |
| # 2️⃣ Text Classification Model | |
| # --------------------------- | |
| text_classifier = pipeline( | |
| "text-classification", | |
| model="distilbert-base-uncased-finetuned-sst-2-english" | |
| ) | |
| # --------------------------- | |
| # 3️⃣ Weather Info Function | |
| # --------------------------- | |
| def get_weather(city_name): | |
| try: | |
| api_url = f"https://wttr.in/{city_name}?format=3" # Free API | |
| response = requests.get(api_url) | |
| if response.status_code == 200: | |
| return response.text | |
| return "Weather API Error!" | |
| except Exception as e: | |
| return str(e) | |
| # --------------------------- | |
| # 4️⃣ Crop Yield Prediction (CSV & Text-based logic) | |
| # --------------------------- | |
| def predict_yield(text_input, crop_name): | |
| # Use CSV if crop exists | |
| if crop_name in df_yield['crop'].values: | |
| avg_yield = df_yield[df_yield['crop'] == crop_name]['avg_yield'].values[0] | |
| return f"Predicted Yield for {crop_name}: {avg_yield:.2f} tons" | |
| # Otherwise, dummy logic from text | |
| words = len(text_input.split()) | |
| return f"Predicted Yield (text-based demo): {words * 0.5:.2f} tons" | |
| # --------------------------- | |
| # 5️⃣ Image-Based Crop Disease Detection (Lightweight CNN placeholder) | |
| # --------------------------- | |
| def detect_disease(image): | |
| if image is None: | |
| return "No image provided" | |
| # Placeholder logic: In real production, replace with lightweight CNN | |
| return "Detected Disease: Healthy (demo placeholder)" | |
| # --------------------------- | |
| # 6️⃣ Urdu AI Chatbot for Farmers | |
| # --------------------------- | |
| # Using a small T5 model (lightweight) for demonstration | |
| try: | |
| chatbot_model = pipeline("text-generation", model="google/flan-t5-small") | |
| except: | |
| chatbot_model = None # fallback if model fails in free tier | |
| def urdu_chatbot(user_input): | |
| if chatbot_model: | |
| response = chatbot_model(f"Answer in Urdu: {user_input}", max_length=50)[0]['generated_text'] | |
| return response | |
| return "Chatbot unavailable in free-tier" | |
| # --------------------------- | |
| # 7️⃣ Gradio Interface | |
| # --------------------------- | |
| def agro_ai_pipeline(text_input, crop_name, city_name, image_input, farmer_question): | |
| # Text classification | |
| cls_result = text_classifier(text_input)[0] | |
| # Yield prediction | |
| yield_result = predict_yield(text_input, crop_name) | |
| # Weather info | |
| weather_result = get_weather(city_name) | |
| # Disease detection | |
| disease_result = detect_disease(image_input) | |
| # Urdu chatbot | |
| chatbot_result = urdu_chatbot(farmer_question) | |
| return cls_result, yield_result, weather_result, disease_result, chatbot_result | |
| title = "🌾 Agriculture AI Assistant - Pakistan Hackathon Level" | |
| description = """ | |
| Welcome to **Agriculture AI**! | |
| Features: | |
| - ✅ Dynamic text classification (advice, articles, etc.) | |
| - ✅ Crop yield prediction (CSV + demo logic) | |
| - ✅ Weather info for any Pakistani city | |
| - ✅ Image-based crop disease detection (lightweight CNN placeholder) | |
| - ✅ Urdu AI chatbot for farmers | |
| All powered by **free Hugging Face models**, cloud datasets, and dynamic Colab/CSV sources! | |
| """ | |
| iface = gr.Interface( | |
| fn=agro_ai_pipeline, | |
| inputs=[ | |
| gr.Textbox(label="Enter Text (Crop Info / Advice)"), | |
| gr.Textbox(label="Enter Crop Name"), | |
| gr.Textbox(label="Enter City Name for Weather Info"), | |
| gr.Image(label="Upload Crop Image for Disease Detection"), | |
| gr.Textbox(label="Ask a Question in Urdu") | |
| ], | |
| outputs=[ | |
| gr.Label(label="Text Classification"), | |
| gr.Textbox(label="Predicted Crop Yield"), | |
| gr.Textbox(label="Weather Info"), | |
| gr.Textbox(label="Crop Disease Detection"), | |
| gr.Textbox(label="Urdu AI Chatbot Response") | |
| ], | |
| title=title, | |
| description=description, | |
| theme="default", | |
| allow_flagging="never" | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() |