# 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()