Update app.py

app.py

@@ -1,60 +1,111 @@
 import gradio as gr
+import pandas as pd
+import lightgbm as lgb
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder
+from google.generativeai import GenerativeModel, configure
 from gtts import gTTS
-import os
-import google.generativeai as genai
 import speech_recognition as sr
-from pydub import AudioSegment
+import os
+import tempfile
+
+# ---------------------------
+# Gemini Configuration
+# ---------------------------
+GOOGLE_API_KEY = "AIzaSyAju28ijKpMNxr1kh4Ml5GPNmI7reBN7FE"  # Replace with your API key in double quotes
+configure(api_key=GOOGLE_API_KEY)
+gemini_model = GenerativeModel("models/gemini-1.5-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)
 
-# Configure Gemini API
-GOOGLE_API_KEY = "AIzaSyAju28ijKpMNxr1kh4Ml5GPNmI7reBN7FE"  # Replace with your actual key
-genai.configure(api_key=GOOGLE_API_KEY)
-gemini_model = genai.GenerativeModel("models/gemini-1.5-pro")
+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"🌾 ਸਿਫਾਰਸ਼ੀ ਫਸਲ: *{crop_name}*"
 
-# Function to get response from Gemini
-def chat_with_gemini(prompt):
+# ---------------------------
+# Voice to Text Utility
+# ---------------------------
+def transcribe_audio(audio_path):
+    recognizer = sr.Recognizer()
+    with sr.AudioFile(audio_path) as source:
+        audio = recognizer.record(source)
     try:
-        response = gemini_model.generate_content(prompt)
+        return recognizer.recognize_google(audio, language='pa-IN')
+    except sr.UnknownValueError:
+        return "❌ ਆਵਾਜ਼ ਨੂੰ ਸਮਝਿਆ ਨਹੀਂ ਜਾ ਸਕਿਆ।"
+    except sr.RequestError:
+        return "❌ ਗੂਗਲ ਸਪੀਚ ਐਪੀਆਈ ਨਾਲ ਕਨੇਕਟ ਨਹੀਂ ਹੋ ਸਕਿਆ।"
+
+# ---------------------------
+# Gemini Response & TTS
+# ---------------------------
+def get_gemini_response(query):
+    try:
+        response = gemini_model.generate_content(f"ਪੰਜਾਬੀ ਵਿੱਚ ਜਵਾਬ ਦਿਓ: {query}")
         return response.text
     except Exception as e:
-        return f"❌ Error from Gemini: {str(e)}"
+        return f"❌ Gemini ਤਰਫੋਂ ਗਲਤੀ: {str(e)}"
 
-# Function to convert text to speech (Punjabi)
-def text_to_speech(text, lang="pa"):
+def text_to_speech(text, lang='pa'):
     tts = gTTS(text=text, lang=lang)
-    output_path = "output.mp3"
-    tts.save(output_path)
-    return output_path
-
-# Function to handle voice input and output
-def respond_with_audio_and_text(audio_file):
-    try:
-        # Convert WebM to WAV
-        sound = AudioSegment.from_file(audio_file, format="webm")
-        wav_path = "converted.wav"
-        sound.export(wav_path, format="wav")
-
-        # Use speech recognition
-        recognizer = sr.Recognizer()
-        with sr.AudioFile(wav_path) as source:
-            audio = recognizer.record(source)
-        user_text = recognizer.recognize_google(audio, language="pa-IN")
-    except Exception as e:
-        return f"❌ Voice Input Error: {str(e)}", None
+    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3")
+    tts.save(temp_file.name)
+    return temp_file.name
 
-    response_text = chat_with_gemini(user_text)
-    audio_path = text_to_speech(response_text)
-    return response_text, audio_path
+# ---------------------------
+# Combined Function
+# ---------------------------
+def handle_voice_query(audio_file):
+    query = transcribe_audio(audio_file)
+    response = get_gemini_response(query)
+    audio_path = text_to_speech(response)
+    return query, response, audio_path
 
-# Gradio UI
+# ---------------------------
+# Gradio Interface
+# ---------------------------
 with gr.Blocks() as demo:
-    gr.Markdown("## 🌾 Punjabi Farming Chatbot with Voice Support")
-    gr.Markdown("🎤 Speak in Punjabi and get a smart Gemini-based response!")
+    gr.Markdown("# 🌾 **AgroVision: ਪੰਜਾਬੀ ਵੈਚਲਣ ਸਹਾਇਕ**")
 
-    audio_input = gr.Audio(label="🎙️ Speak Your Question", type="filepath")
-    submit_btn = gr.Button("Ask")
-    text_output = gr.Textbox(label="📝 Gemini Response (Punjabi)")
-    audio_output = gr.Audio(label="🔊 Audio Response", type="filepath")
+    with gr.Tabs():
+        with gr.TabItem("🌾 ਫਸਲ ਦੀ ਸਿਫਾਰਸ਼"):
+            with gr.Row():
+                N = gr.Slider(0, 300, step=1, label="ਨਾਈਟ੍ਰੋਜਨ (kg/ha)")
+                P = gr.Slider(0, 200, step=1, label="ਫਾਸਫੋਰਸ (kg/ha)")
+                K = gr.Slider(0, 200, step=1, label="ਪੋਟਾਸ਼ੀਅਮ (kg/ha)")
+            with gr.Row():
+                temperature = gr.Slider(-10, 50, step=0.1, label="ਤਾਪਮਾਨ (°C)")
+                humidity = gr.Slider(0, 100, step=1, label="ਨਮੀ (%)")
+            with gr.Row():
+                ph = gr.Slider(0, 14, step=0.1, label="ਮਿੱਟੀ ਦਾ pH")
+                rainfall = gr.Slider(0, 500, step=1, label="ਵਰਖਾ (mm)")
+            predict_btn = gr.Button("ਫਸਲ ਦੀ ਭਵਿੱਖਬਾਣੀ ਕਰੋ")
+            crop_output = gr.Markdown()
+            predict_btn.click(predict_crop, inputs=[N, P, K, temperature, humidity, ph, rainfall], outputs=crop_output)
 
-    submit_btn.click(fn=respond_with_audio_and_text, inputs=audio_input, outputs=[text_output, audio_output])
+        with gr.TabItem("🗣️ ਆਵਾਜ਼ ਰਾਹੀਂ ਪੁੱਛੋ"):
+            gr.Markdown("### ਆਪਣਾ ਸਵਾਲ ਆਵਾਜ਼ ਰਾਹੀਂ ਪੁੱਛੋ (ਪੰਜਾਬੀ ਵਿੱਚ)")
+            audio_input = gr.Audio(type="filepath", label="🎤 ਸਵਾਲ ਬੋਲੋ")
+            query_text = gr.Textbox(label="🔍 ਬੋਲਿਆ ਗਿਆ ਸਵਾਲ")
+            gemini_response = gr.Textbox(label="📜 Gemini ਜਵਾਬ")
+            audio_output = gr.Audio(label="🔊 ਆਵਾਜ਼ੀ ਜਵਾਬ")
+            submit_btn = gr.Button("➡️ ਜਵਾਬ ਲਵੋ")
+            submit_btn.click(fn=handle_voice_query, inputs=[audio_input], outputs=[query_text, gemini_response, audio_output])
 
 demo.launch()