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app (2).py

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+import gradio as gr
+import pandas as pd
+import lightgbm as lgb
+import numpy as np
+import os
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder
+from transformers import pipeline, Conversation, set_seed
+from dotenv import load_dotenv
+import google.generativeai as genai
+
+# ---------------------------
+# Load environment variables
+# ---------------------------
+load_dotenv()
+GOOGLE_API_KEY = os.getenv("GAPI")
+
+# Configure Gemini API
+genai.configure(api_key=GOOGLE_API_KEY)
+gemini_model = genai.GenerativeModel("gemini-pro")
+
+# ---------------------------
+# CROP RECOMMENDATION SETUP
+# ---------------------------
+url = "https://raw.githubusercontent.com/89911384/CSV-Files/refs/heads/main/crop_cleaned%20data.csv"
+data = pd.read_csv(url)
+
+X = data.drop('label', axis=1)
+y = data['label']
+le = LabelEncoder()
+y_encoded = le.fit_transform(y)
+
+X_train, X_test, y_train, y_test = train_test_split(X, y_encoded, test_size=0.3, random_state=0)
+model = lgb.LGBMClassifier()
+model.fit(X_train, y_train)
+
+def predict_crop(N, P, K, temperature, humidity, ph, rainfall):
+    input_data = np.array([[N, P, K, temperature, humidity, ph, rainfall]])
+    pred = model.predict(input_data)[0]
+    crop_name = le.inverse_transform([pred])[0]
+    return f"🌾 Recommended Crop: *{crop_name}*"
+
+# ---------------------------
+# CHATBOT SETUP
+# ---------------------------
+chatbot = pipeline("conversational", model="microsoft/DialoGPT-medium")
+set_seed(42)
+
+def chat_with_bot(user_input):
+    conversation = Conversation(user_input)
+    conversation = chatbot(conversation)
+    return conversation.generated_responses[-1]
+
+def chat_with_gemini(prompt):
+    try:
+        response = gemini_model.generate_content(prompt)
+        return response.text
+    except Exception as e:
+        return f"❌ Error from Gemini: {str(e)}"
+
+def smart_chat(user_input, model_choice):
+    if model_choice == "Gemini":
+        return chat_with_gemini(user_input)
+    else:
+        return chat_with_bot(user_input)
+
+# ---------------------------
+# GRADIO APP UI
+# ---------------------------
+with gr.Blocks() as demo:
+    gr.Markdown("# 🌱 **AgroVision: Smart Assistant for Farmers**")
+
+    with gr.Tabs():
+        # TAB 1: CROP RECOMMENDATION
+        with gr.TabItem("🌾 Crop Recommendation"):
+            gr.Markdown("### Enter Soil & Climate Details to Predict Suitable Crop")
+            with gr.Row():
+                N = gr.Slider(minimum=0, maximum=300, step=1, label="Nitrogen (N) in kg/ha")
+                P = gr.Slider(minimum=0, maximum=200, step=1, label="Phosphorus (P) in kg/ha")
+                K = gr.Slider(minimum=0, maximum=200, step=1, label="Potassium (K) in kg/ha")
+            with gr.Row():
+                temperature = gr.Slider(minimum=-10, maximum=50, step=0.1, label="Temperature (°C)")
+                humidity = gr.Slider(minimum=0, maximum=100, step=1, label="Humidity (%)")
+            with gr.Row():
+                ph = gr.Slider(minimum=0, maximum=14, step=0.1, label="Soil pH")
+                rainfall = gr.Slider(minimum=0, maximum=500, step=1, label="Rainfall (mm)")
+            predict_btn = gr.Button("Predict Crop")
+            output_crop = gr.Markdown()
+            predict_btn.click(
+                fn=predict_crop,
+                inputs=[N, P, K, temperature, humidity, ph, rainfall],
+                outputs=output_crop
+            )
+
+        # TAB 2: CROP DISEASE PREDICTION (Placeholder)
+        with gr.TabItem("🌿 Crop Disease Detection"):
+            gr.Markdown("### Upload an image of a crop leaf to detect disease (Coming Soon)")
+            gr.Image(label="Upload Crop Image", type="filepath")
+            gr.Button("Predict Disease (Coming Soon)")
+            gr.Textbox(label="Prediction Output", placeholder="Model response will appear here...")
+
+        # TAB 3: SMART CHATBOT
+        with gr.TabItem("💬 Farmer's Chatbot"):
+            gr.Markdown("### Ask any question related to farming 👨‍🌾")
+
+            with gr.Row():
+                user_input = gr.Textbox(label="Your Question")
+                model_selector = gr.Dropdown(["Gemini", "DialoGPT"], value="Gemini", label="Select Model")
+            chatbot_output = gr.Textbox(label="AgroVision Bot Response")
+            chatbot_btn = gr.Button("Ask")
+            chatbot_btn.click(fn=smart_chat, inputs=[user_input, model_selector], outputs=chatbot_output)
+
+# Launch the app
+demo.launch()

requirements.txt

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+gradio
+pandas
+lightgbm
+numpy
+scikit-learn
+transformers==4.29.2
+torch
+google-generativeai
+python-dotenv