Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+from transformers import pipeline
+
+# Load farmer data from CSV
+farmers_df = pd.read_csv("farmers.csv")
+
+# Load pre-trained NLP models (free)
+qa_pipeline = pipeline("question-answering", model="bert-large-uncased-whole-word-masking-finetuned-squad")
+classifier = pipeline("text-classification", model="distilbert-base-uncased")
+
+def get_farmer_context(phone):
+    farmer = farmers_df[farmers_df["Phone"] == phone]
+    if farmer.empty:
+        return None
+    return farmer.iloc[0].to_string()
+
+def verify_details(phone, query):
+    context = get_farmer_context(phone)
+    if not context:
+        return "Farmer not found. Check your phone number."
+    
+    # Handle specific queries
+    if "survey" in query.lower():
+        answer = qa_pipeline(question="Is the survey done?", context=context)['answer']
+        return f"Survey Status: {answer}"
+    elif "area" in query.lower():
+        answer = qa_pipeline(question="What is the farm area?", context=context)['answer']
+        return f"Farm Area: {answer} acres"
+    elif "ownership" in query.lower():
+        answer = qa_pipeline(question="What is the ownership status?", context=context)['answer']
+        return f"Ownership: {answer}"
+    elif "paddy" in query.lower():
+        answer = qa_pipeline(question="Which paddy method is used?", context=context)['answer']
+        return f"Paddy Method: {answer}"
+    elif "wheat" in query.lower():
+        answer = qa_pipeline(question="Wheat tillage method?", context=context)['answer']
+        return f"Wheat Tillage: {answer}"
+    else:
+        return "Ask about: survey, area, ownership, paddy, wheat."
+
+def classify_practice(text):
+    result = classifier(text)[0]
+    return f"Classification: {result['label']} (Confidence: {round(result['score']*100)}%)"
+
+with gr.Blocks() as demo:
+    gr.Markdown("# 🌾 Varahaa Farmer Verification Bot")
+    
+    with gr.Tab("Verify Farmer Details"):
+        phone = gr.Textbox(label="Enter Phone Number (+91XXXXXXXXXX)")
+        query = gr.Textbox(label="Ask a question (e.g., 'Survey status?')")
+        output = gr.Textbox(label="Response")
+        verify_btn = gr.Button("Verify")
+        verify_btn.click(fn=verify_details, inputs=[phone, query], outputs=output)
+    
+    with gr.Tab("Classify Farming Practice"):
+        text = gr.Textbox(label="Describe your practice (e.g., 'I use DSR')")
+        cls_output = gr.Textbox(label="Result")
+        cls_btn = gr.Button("Classify")
+        cls_btn.click(fn=classify_practice, inputs=text, outputs=cls_output)
+
+demo.launch()